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	<title>bioRxiv Channel: Human Cell Atlas</title>
	<link>https://biorxiv.org</link>
	<description>
	This feed contains articles for bioRxiv Channel "Human Cell Atlas"
	</description>

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	<title>bioRxiv</title>
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	<link>https://biorxiv.org</link>
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	<item rdf:about="https://biorxiv.org/cgi/content/short/527408v1?rss=1">
<title>
<![CDATA[
A cellular census of healthy lung and asthmatic airway wall identifies novel cell states in health and disease 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/527408v1?rss=1"
</link>
<description><![CDATA[
Human lungs enable efficient gas exchange, and form an interface with the environment which depends on mucosal immunity for protection against infectious agents. Tightly controlled interactions between structural and immune cells are required to maintain lung homeostasis. Here, we use single cell transcriptomics to chart the cellular landscape of upper and lower airways and lung parenchyma in health. We report location-dependent airway epithelial cell states, and a novel subset of tissue-resident memory T cells. In lower airways of asthma patients, mucous cell hyperplasia is shown to stem from a novel mucous ciliated cell state, as well as goblet cell hyperplasia. We report presence of pathogenic effector Th2 cells in asthma, and find evidence for type-2 cytokines in maintaining the altered epithelial cell states. Unbiased analysis of cell-cell interactions identify a shift from airway structural cell communication in health to a Th2-dominated interactome in asthma.
]]></description>
<dc:creator>Vieira Braga, F. A.</dc:creator>
<dc:creator>Kar, G.</dc:creator>
<dc:creator>Berg, M.</dc:creator>
<dc:creator>Carpaij, O. A.</dc:creator>
<dc:creator>Polanski, K.</dc:creator>
<dc:creator>Simon, L. M.</dc:creator>
<dc:creator>Brouwer, S.</dc:creator>
<dc:creator>Gomes, T.</dc:creator>
<dc:creator>Hesse, L.</dc:creator>
<dc:creator>Jiang, J.</dc:creator>
<dc:creator>Fasouli, E. S.</dc:creator>
<dc:creator>Efremova, M.</dc:creator>
<dc:creator>Vento-Tormo, R.</dc:creator>
<dc:creator>Affleck, K.</dc:creator>
<dc:creator>Palit, S.</dc:creator>
<dc:creator>Strzelecka, P.</dc:creator>
<dc:creator>Firth, H. V.</dc:creator>
<dc:creator>Mahbubani, K. T.</dc:creator>
<dc:creator>Cvejic, A.</dc:creator>
<dc:creator>Meyer, K. B.</dc:creator>
<dc:creator>Saeb-Parsy, K.</dc:creator>
<dc:creator>Luinge, M.</dc:creator>
<dc:creator>Brandsma, C.-A.</dc:creator>
<dc:creator>Timens, W.</dc:creator>
<dc:creator>Angelidis, I.</dc:creator>
<dc:creator>Strunz, M.</dc:creator>
<dc:creator>Koppelman, G. H.</dc:creator>
<dc:creator>van Oosterhout, A. J.</dc:creator>
<dc:creator>Schiller, H. B.</dc:creator>
<dc:creator>Theis, F. J.</dc:creator>
<dc:creator>van den Berge, M.</dc:creator>
<dc:creator>Nawijn, M. C.</dc:creator>
<dc:creator>Teichmann, S. A.</dc:creator>
<dc:date>2019-01-23</dc:date>
<dc:identifier>doi:10.1101/527408</dc:identifier>
<dc:title><![CDATA[A cellular census of healthy lung and asthmatic airway wall identifies novel cell states in health and disease]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-01-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/384826v1?rss=1">
<title>
<![CDATA[
Conserved cell types with divergent features between human and mouse cortex 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/384826v1?rss=1"
</link>
<description><![CDATA[
Elucidating the cellular architecture of the human neocortex is central to understanding our cognitive abilities and susceptibility to disease. Here we applied single nucleus RNA-sequencing to perform a comprehensive analysis of cell types in the middle temporal gyrus of human cerebral cortex. We identify a highly diverse set of excitatory and inhibitory neuronal types that are mostly sparse, with excitatory types being less layer-restricted than expected. Comparison to a similar mouse cortex single cell RNA-sequencing dataset revealed a surprisingly well-conserved cellular architecture that enables matching of homologous types and predictions of human cell type properties. Despite this general conservation, we also find extensive differences between homologous human and mouse cell types, including dramatic alterations in proportions, laminar distributions, gene expression, and morphology. These species-specific features emphasize the importance of directly studying human brain.
]]></description>
<dc:creator>Hodge, R. D.</dc:creator>
<dc:creator>Bakken, T. E.</dc:creator>
<dc:creator>Miller, J. A.</dc:creator>
<dc:creator>Smith, K. A.</dc:creator>
<dc:creator>Barkan, E. R.</dc:creator>
<dc:creator>Graybuck, L. T.</dc:creator>
<dc:creator>Close, J. L.</dc:creator>
<dc:creator>Long, B.</dc:creator>
<dc:creator>Penn, O.</dc:creator>
<dc:creator>Yao, Z.</dc:creator>
<dc:creator>Eggermont, J.</dc:creator>
<dc:creator>Hollt, T.</dc:creator>
<dc:creator>Levi, B. P.</dc:creator>
<dc:creator>Shehata, S. I.</dc:creator>
<dc:creator>Aevermann, B.</dc:creator>
<dc:creator>Beller, A.</dc:creator>
<dc:creator>Bertagnolli, D.</dc:creator>
<dc:creator>Brouner, K.</dc:creator>
<dc:creator>Casper, T.</dc:creator>
<dc:creator>Cobbs, C.</dc:creator>
<dc:creator>Dalley, R.</dc:creator>
<dc:creator>Dee, N.</dc:creator>
<dc:creator>Ding, S.-L.</dc:creator>
<dc:creator>Ellenbogen, R. G.</dc:creator>
<dc:creator>Fong, O.</dc:creator>
<dc:creator>Garren, E.</dc:creator>
<dc:creator>Goldy, J.</dc:creator>
<dc:creator>Gwinn, R. P.</dc:creator>
<dc:creator>Hirschstein, D.</dc:creator>
<dc:creator>Keene, C. D.</dc:creator>
<dc:creator>Keshk, M.</dc:creator>
<dc:creator>Ko, A. L.</dc:creator>
<dc:creator>Lathia, K.</dc:creator>
<dc:creator>Mahfouz, A.</dc:creator>
<dc:creator>Maltzer, Z.</dc:creator>
<dc:creator>McGraw, M.</dc:creator>
<dc:creator>Nguyen, T. N.</dc:creator>
<dc:creator>Nyhus, J.</dc:creator>
<dc:creator>Ojemann, J. G.</dc:creator>
<dc:creator>Oldre, A.</dc:creator>
<dc:creator>Parry, S.</dc:creator>
<dc:creator>Reynolds, S.</dc:creator>
<dc:creator>Rimorin, C.</dc:creator>
<dc:creator>Shapovalova, N. V.</dc:creator>
<dc:creator>Somasundaram, S.</dc:creator>
<dc:creator></dc:creator>
<dc:date>2018-08-05</dc:date>
<dc:identifier>doi:10.1101/384826</dc:identifier>
<dc:title><![CDATA[Conserved cell types with divergent features between human and mouse cortex]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-08-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/429589v1?rss=1">
<title>
<![CDATA[
Reconstructing the human first trimester fetal-maternal interface using single cell transcriptomics 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/429589v1?rss=1"
</link>
<description><![CDATA[
During the early weeks of human pregnancy, the fetal placenta implants into the uterine mucosa (decidua) where placental trophoblast cells intermingle and communicate with maternal cells. Here, we profile transcriptomes of [~]50,000 single cells from this unique microenvironment, sampling matched first trimester maternal blood and decidua, and fetal cells from the placenta itself. We define the cellular composition of human decidua, revealing five distinct subsets of decidual fibroblasts with differing growth factors and hormone production profiles, and show that fibroblast states define two distinct decidual layers. Among decidual NK cells, we resolve three subsets, each with a different immunomodulatory and chemokine profile. We develop a repository of ligand-receptor pairs (www.CellPhoneDB.org) and a statistical tool to predict the probability of cell-cell interactions via these pairs, highlighting specific interactions between decidual NK cells and invading fetal extravillous trophoblast cells, maternal immune and stromal cells. Our single cell atlas of the maternal-fetal interface reveals the cellular organization and interactions critical for placentation and reproductive success.
]]></description>
<dc:creator>Vento-Tormo, R.</dc:creator>
<dc:creator>Efremova, M.</dc:creator>
<dc:creator>Botting, R. A.</dc:creator>
<dc:creator>Turco, M. Y.</dc:creator>
<dc:creator>Vento-Tormo, M.</dc:creator>
<dc:creator>Meyer, K. B.</dc:creator>
<dc:creator>Park, J.</dc:creator>
<dc:creator>Stephenson, E.</dc:creator>
<dc:creator>Polanski, K.</dc:creator>
<dc:creator>Payne, R. P.</dc:creator>
<dc:creator>Goncalves, A.</dc:creator>
<dc:creator>Zou, A.</dc:creator>
<dc:creator>Henriksson, J.</dc:creator>
<dc:creator>Wood, L.</dc:creator>
<dc:creator>Lisgo, S.</dc:creator>
<dc:creator>Filby, A.</dc:creator>
<dc:creator>Wright, G. J.</dc:creator>
<dc:creator>Stubbington, M. J.</dc:creator>
<dc:creator>Haniffa, M.</dc:creator>
<dc:creator>Moffett, A.</dc:creator>
<dc:creator>Teichmann, S. A.</dc:creator>
<dc:date>2018-09-29</dc:date>
<dc:identifier>doi:10.1101/429589</dc:identifier>
<dc:title><![CDATA[Reconstructing the human first trimester fetal-maternal interface using single cell transcriptomics]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-09-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/385328v1?rss=1">
<title>
<![CDATA[
Palantir characterizes cell fate continuities in human hematopoiesis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/385328v1?rss=1"
</link>
<description><![CDATA[
Recent studies using single cell RNA-seq (scRNA-seq) data derived from differentiating systems have raised fundamental questions regarding the discrete vs continuous nature of both differentiation and cell fate. Here we present Palantir, an algorithm that models trajectories of differentiating cells, which treats cell-fate as a probabilistic process, and leverages entropy to measure the changing nature of cell plasticity along the differentiation trajectory. Palantir generates a high resolution pseudotime ordering of cells, and assigns each cell state with its probability to differentiate into each terminal state. We apply Palantir to human bone marrow scRNA-seq data and detect key landmarks of hematopoietic differentiation. Palantirs resolution enables identification of key transcription factors driving lineage fate choices, as these TFs closely track when cells lose plasticity. We demonstrate that Palantir is generalizable to diverse tissue types and well-suited to resolve less studied differentiating systems.
]]></description>
<dc:creator>Setty, M.</dc:creator>
<dc:creator>Kiseliovas, V.</dc:creator>
<dc:creator>Levine, J.</dc:creator>
<dc:creator>Gayoso, A.</dc:creator>
<dc:creator>Mazutis, L.</dc:creator>
<dc:creator>Pe'er, D.</dc:creator>
<dc:date>2018-08-05</dc:date>
<dc:identifier>doi:10.1101/385328</dc:identifier>
<dc:title><![CDATA[Palantir characterizes cell fate continuities in human hematopoiesis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-08-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/296608v1?rss=1">
<title>
<![CDATA[
Single-Cell Transcriptomic Analysis of Human Lung Reveals Complex Multicellular Changes During Pulmonary Fibrosis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/296608v1?rss=1"
</link>
<description><![CDATA[
Pulmonary fibrosis is a devastating disorder that results in the progressive replacement of normal lung tissue with fibrotic scar. Available therapies slow disease progression, but most patients go on to die or require lung transplantation. Single-cell RNA-seq is a powerful tool that can reveal cellular identity via analysis of the transcriptome, but its ability to provide biologically or clinically meaningful insights in a disease context is largely unexplored. Accordingly, we performed single-cell RNA-seq on lung tissue obtained from eight transplant donors and eight recipients with pulmonary fibrosis and one bronchoscopic cryobiospy sample. Integrated single-cell transcriptomic analysis of donors and patients with pulmonary fibrosis identified the emergence of distinct populations of epithelial cells and macrophages that were common to all patients with lung fibrosis. Analysis of transcripts in the Wnt pathway suggested that within the same cell type, Wnt secretion and response are restricted to distinct non-overlapping cells, which was confirmed using in situ RNA hybridization. Single-cell RNA-seq revealed heterogeneity within alveolar macrophages from individual patients, which was confirmed by immunohistochemistry. These results support the feasibility of discovery-based approaches applying next generation sequencing technologies to clinically obtained samples with a goal of developing personalized therapies.nnOne Sentence SummarySingle-cell RNA-seq applied to tissue from diseased and donor lungs and a living patient with pulmonary fibrosis identifies cell type-specific disease-associated molecular pathways.
]]></description>
<dc:creator>Reyfman, P. A.</dc:creator>
<dc:creator>Walter, J. M.</dc:creator>
<dc:creator>Joshi, N.</dc:creator>
<dc:creator>Anekalla, K. R.</dc:creator>
<dc:creator>McQuattie-Pimentel, A. C.</dc:creator>
<dc:creator>Chiu, S.</dc:creator>
<dc:creator>Fernandez, R.</dc:creator>
<dc:creator>Akbarpour, M.</dc:creator>
<dc:creator>Chen, C.-I.</dc:creator>
<dc:creator>Ren, Z.</dc:creator>
<dc:creator>Verma, R.</dc:creator>
<dc:creator>Abdala-Valencia, H.</dc:creator>
<dc:creator>Nam, K.</dc:creator>
<dc:creator>Chi, M.</dc:creator>
<dc:creator>Han, S.</dc:creator>
<dc:creator>Gonzalez-Gonzalez, F. J.</dc:creator>
<dc:creator>Soberanes, S.</dc:creator>
<dc:creator>Watanabe, S.</dc:creator>
<dc:creator>Williams, K. J. N.</dc:creator>
<dc:creator>Flozak, A. S.</dc:creator>
<dc:creator>Nicholson, T. T.</dc:creator>
<dc:creator>Morgan, V. K.</dc:creator>
<dc:creator>Hrusch, C. L.</dc:creator>
<dc:creator>Guzy, R. D.</dc:creator>
<dc:creator>Bonham, C. A.</dc:creator>
<dc:creator>Sperling, A. I.</dc:creator>
<dc:creator>Bag, R.</dc:creator>
<dc:creator>Hamanaka, R. B.</dc:creator>
<dc:creator>Mutlu, G. M.</dc:creator>
<dc:creator>Yeldandi, A. V.</dc:creator>
<dc:creator>Marshall, S. A.</dc:creator>
<dc:creator>Shilatifard, A.</dc:creator>
<dc:creator>Amaral, L. A. N.</dc:creator>
<dc:creator>Perlman, H.</dc:creator>
<dc:creator>Sznajder, J. I.</dc:creator>
<dc:creator>Winter, D. R.</dc:creator>
<dc:creator>Hinchcliff, M.</dc:creator>
<dc:creator>Argento, A. C.</dc:creator>
<dc:creator>Gillespie, C. T.</dc:creator>
<dc:creator>Dematt</dc:creator>
<dc:date>2018-04-06</dc:date>
<dc:identifier>doi:10.1101/296608</dc:identifier>
<dc:title><![CDATA[Single-Cell Transcriptomic Analysis of Human Lung Reveals Complex Multicellular Changes During Pulmonary Fibrosis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-04-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/455451v1?rss=1">
<title>
<![CDATA[
Rewiring of the cellular and inter-cellular landscape of the human colon during ulcerative colitis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/455451v1?rss=1"
</link>
<description><![CDATA[
The paper has been withdrawn owing to erroneous inclusion of confidential information relating to a third party.
]]></description>
<dc:creator>Smillie, C. S.</dc:creator>
<dc:creator>Biton, M.</dc:creator>
<dc:creator>Ordovas-Montanes, J.</dc:creator>
<dc:creator>Sullivan, K. M.</dc:creator>
<dc:creator>Burgin, G.</dc:creator>
<dc:creator>Graham, D. B.</dc:creator>
<dc:creator>Herbst, R. H.</dc:creator>
<dc:creator>Rogel, N.</dc:creator>
<dc:creator>Slyper, M.</dc:creator>
<dc:creator>Waldman, J.</dc:creator>
<dc:creator>Sud, M.</dc:creator>
<dc:creator>Andrews, E.</dc:creator>
<dc:creator>Haber, A. L.</dc:creator>
<dc:creator>Vickovic, S.</dc:creator>
<dc:creator>Dionne, D.</dc:creator>
<dc:creator>Nguyen, L. T.</dc:creator>
<dc:creator>Villani, A. C.</dc:creator>
<dc:creator>Hofree, M.</dc:creator>
<dc:creator>Creasey, E. A.</dc:creator>
<dc:creator>Huang, H.</dc:creator>
<dc:creator>Rozenblatt-Rosen, O.</dc:creator>
<dc:creator>Garber, J. J.</dc:creator>
<dc:creator>Khalili, H.</dc:creator>
<dc:creator>Desch, A. N.</dc:creator>
<dc:creator>Daly, M. J.</dc:creator>
<dc:creator>Ananthakrishnan, A. N.</dc:creator>
<dc:creator>Shalek, A. K.</dc:creator>
<dc:creator>Xavier, R. J.</dc:creator>
<dc:creator>Regev, A.</dc:creator>
<dc:date>2018-10-29</dc:date>
<dc:identifier>doi:10.1101/455451</dc:identifier>
<dc:title><![CDATA[Rewiring of the cellular and inter-cellular landscape of the human colon during ulcerative colitis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-10-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/630087v1?rss=1">
<title>
<![CDATA[
Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/630087v1?rss=1"
</link>
<description><![CDATA[
Single-cell RNA sequencing (scRNA-seq) is the leading technique for charting the molecular properties of individual cells. The latest methods are scalable to thousands of cells, enabling in-depth characterization of sample composition without prior knowledge. However, there are important differences between scRNA-seq techniques, and it remains unclear which are the most suitable protocols for drawing cell atlases of tissues, organs and organisms. We have generated benchmark datasets to systematically evaluate techniques in terms of their power to comprehensively describe cell types and states. We performed a multi-center study comparing 13 commonly used single-cell and single-nucleus RNA-seq protocols using a highly heterogeneous reference sample resource. Comparative and integrative analysis at cell type and state level revealed marked differences in protocol performance, highlighting a series of key features for cell atlas projects. These should be considered when defining guidelines and standards for international consortia, such as the Human Cell Atlas project.
]]></description>
<dc:creator>Mereu, E.</dc:creator>
<dc:creator>Lafzi, A.</dc:creator>
<dc:creator>Moutinho, C.</dc:creator>
<dc:creator>Ziegenhain, C.</dc:creator>
<dc:creator>MacCarthy, D. J.</dc:creator>
<dc:creator>Alvarez, A.</dc:creator>
<dc:creator>Batlle, E.</dc:creator>
<dc:creator>Sagar,</dc:creator>
<dc:creator>Grün, D.</dc:creator>
<dc:creator>Lau, J. K.</dc:creator>
<dc:creator>Boutet, S.</dc:creator>
<dc:creator>Sanada, C.</dc:creator>
<dc:creator>Ooi, A.</dc:creator>
<dc:creator>Jones, R. C.</dc:creator>
<dc:creator>Kaihara, K.</dc:creator>
<dc:creator>Brampton, C.</dc:creator>
<dc:creator>Talaga, Y.</dc:creator>
<dc:creator>Sasagawa, Y.</dc:creator>
<dc:creator>Tanaka, K.</dc:creator>
<dc:creator>Hayashi, T.</dc:creator>
<dc:creator>Nikaido, I.</dc:creator>
<dc:creator>Fischer, C.</dc:creator>
<dc:creator>Sauer, S.</dc:creator>
<dc:creator>Trefzer, T.</dc:creator>
<dc:creator>Conrad, C.</dc:creator>
<dc:creator>Adiconis, X.</dc:creator>
<dc:creator>Nguyen, L. T.</dc:creator>
<dc:creator>Regev, A.</dc:creator>
<dc:creator>Levin, J. Z.</dc:creator>
<dc:creator>Janjic, A.</dc:creator>
<dc:creator>Wange, L. E.</dc:creator>
<dc:creator>Bagnoli, J. W.</dc:creator>
<dc:creator>Parekh, S.</dc:creator>
<dc:creator>Enard, W.</dc:creator>
<dc:creator>Gut, M.</dc:creator>
<dc:creator>Sandberg, R.</dc:creator>
<dc:creator>Gut, I.</dc:creator>
<dc:creator>Stegle, O.</dc:creator>
<dc:creator>Heyn, H.</dc:creator>
<dc:date>2019-05-13</dc:date>
<dc:identifier>doi:10.1101/630087</dc:identifier>
<dc:title><![CDATA[Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-05-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/632216v1?rss=1">
<title>
<![CDATA[
Systematic comparative analysis of single cell RNA-sequencing methods 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/632216v1?rss=1"
</link>
<description><![CDATA[
A multitude of single-cell RNA sequencing methods have been developed in recent years, with dramatic advances in scale and power, and enabling major discoveries and large scale cell mapping efforts. However, these methods have not been systematically and comprehensively benchmarked. Here, we directly compare seven methods for single cell and/or single nucleus profiling from three types of samples - cell lines, peripheral blood mononuclear cells and brain tissue - generating 36 libraries in six separate experiments in a single center. To analyze these datasets, we developed and applied scumi, a flexible computational pipeline that can be used for any scRNA-seq method. We evaluated the methods for both basic performance and for their ability to recover known biological information in the samples. Our study will help guide experiments with the methods in this study as well as serve as a benchmark for future studies and for computational algorithm development.
]]></description>
<dc:creator>Ding, J.</dc:creator>
<dc:creator>Adiconis, X.</dc:creator>
<dc:creator>Simmons, S. K.</dc:creator>
<dc:creator>Kowalczyk, M. S.</dc:creator>
<dc:creator>Hession, C. C.</dc:creator>
<dc:creator>Marjanovic, N. D.</dc:creator>
<dc:creator>Hughes, T. K.</dc:creator>
<dc:creator>Wadsworth, M. H.</dc:creator>
<dc:creator>Burks, T.</dc:creator>
<dc:creator>Nguyen, L. T.</dc:creator>
<dc:creator>Kwon, J. Y. H.</dc:creator>
<dc:creator>Barak, B.</dc:creator>
<dc:creator>Ge, W.</dc:creator>
<dc:creator>Kedaigle, A. J.</dc:creator>
<dc:creator>Carroll, S.</dc:creator>
<dc:creator>Li, S.</dc:creator>
<dc:creator>Hacohen, N.</dc:creator>
<dc:creator>Rozenblatt-Rosen, O.</dc:creator>
<dc:creator>Shalek, A. K.</dc:creator>
<dc:creator>Villani, A.-C.</dc:creator>
<dc:creator>Regev, A.</dc:creator>
<dc:creator>Levin, J. Z.</dc:creator>
<dc:date>2019-05-09</dc:date>
<dc:identifier>doi:10.1101/632216</dc:identifier>
<dc:title><![CDATA[Systematic comparative analysis of single cell RNA-sequencing methods]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-05-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/476036v1?rss=1">
<title>
<![CDATA[
Nuclei multiplexing with barcoded antibodies for single-nucleus genomics 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/476036v1?rss=1"
</link>
<description><![CDATA[
Single-nucleus RNA-Seq (snRNA-seq) enables the interrogation of cellular states in complex tissues that are challenging to dissociate, including frozen clinical samples. This opens the way, in principle, to large studies, such as those required for human genetics, clinical trials, or precise cell atlases of large organs. However, such applications are currently limited by batch effects, sequential processing, and costs. To address these challenges, we present an approach for multiplexing snRNA-seq, using sample-barcoded antibodies against the nuclear pore complex to uniquely label nuclei from distinct samples. Comparing human brain cortex samples profiled in multiplex with or without hashing antibodies, we demonstrate that nucleus hashing does not significantly alter the recovered transcriptome profiles. We further developed demuxEM, a novel computational tool that robustly detects inter-sample nucleus multiplets and assigns singlets to their samples of origin by antibody barcodes, and validated its accuracy using gender-specific gene expression, species-mixing and natural genetic variation. Nucleus hashing significantly reduces cost per nucleus, recovering up to about 5 times as many single nuclei per microfluidc channel. Our approach provides a robust technique for diverse studies including tissue atlases of isogenic model organisms or from a single larger human organ, multiple biopsies or longitudinal samples of one donor, and large-scale perturbation screens.
]]></description>
<dc:creator>Gaublomme, J. T.</dc:creator>
<dc:creator>Li, B.</dc:creator>
<dc:creator>McCabe, C.</dc:creator>
<dc:creator>Knecht, A.</dc:creator>
<dc:creator>Drokhlyansky, E.</dc:creator>
<dc:creator>Van Wittenberghe, N.</dc:creator>
<dc:creator>Waldman, J.</dc:creator>
<dc:creator>Dionne, D.</dc:creator>
<dc:creator>Nguyen, L.</dc:creator>
<dc:creator>De Jager, P.</dc:creator>
<dc:creator>Yeung, B.</dc:creator>
<dc:creator>Zhao, X.</dc:creator>
<dc:creator>Habib, N.</dc:creator>
<dc:creator>Rozenblatt-Rosen, O.</dc:creator>
<dc:creator>Regev, A.</dc:creator>
<dc:date>2018-11-23</dc:date>
<dc:identifier>doi:10.1101/476036</dc:identifier>
<dc:title><![CDATA[Nuclei multiplexing with barcoded antibodies for single-nucleus genomics]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-11-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/294918v1?rss=1">
<title>
<![CDATA[
Molecular architecture of the mouse nervous system 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/294918v1?rss=1"
</link>
<description><![CDATA[
The mammalian nervous system executes complex behaviors controlled by specialised, precisely positioned and interacting cell types. Here, we used RNA sequencing of half a million single cells to create a detailed census of cell types in the mouse nervous system. We mapped cell types spatially and derived a hierarchical, data-driven taxonomy. Neurons were the most diverse, and were grouped by developmental anatomical units, and by the expression of neurotransmitters and neuropeptides. Neuronal diversity was driven by genes encoding cell identity, synaptic connectivity, neurotransmission and membrane conductance. We discovered several distinct, regionally restricted, astrocytes types, which obeyed developmental boundaries and correlated with the spatial distribution of key glutamate and glycine neurotransmitters. In contrast, oligodendrocytes showed a loss of regional identity, followed by a secondary diversification. The resource presented here lays a solid foundation for understanding the molecular architecture of the mammalian nervous system, and enables genetic manipulation of specific cell types.
]]></description>
<dc:creator>Zeisel, A.</dc:creator>
<dc:creator>Hochgerner, H.</dc:creator>
<dc:creator>Lonnerberg, P.</dc:creator>
<dc:creator>Johnsson, A.</dc:creator>
<dc:creator>Memic, F.</dc:creator>
<dc:creator>van der Zwan, J.</dc:creator>
<dc:creator>Haring, M.</dc:creator>
<dc:creator>Braun, E.</dc:creator>
<dc:creator>Borm, L.</dc:creator>
<dc:creator>La Manno, G.</dc:creator>
<dc:creator>Codeluppi, S.</dc:creator>
<dc:creator>Furlan, A.</dc:creator>
<dc:creator>Skene, N.</dc:creator>
<dc:creator>Harris, K. D.</dc:creator>
<dc:creator>Hjerling Leffler, J.</dc:creator>
<dc:creator>Arenas, E.</dc:creator>
<dc:creator>Ernfors, P.</dc:creator>
<dc:creator>Marklund, U.</dc:creator>
<dc:creator>Linnarsson, S.</dc:creator>
<dc:date>2018-04-05</dc:date>
<dc:identifier>doi:10.1101/294918</dc:identifier>
<dc:title><![CDATA[Molecular architecture of the mouse nervous system]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-04-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/649194v1?rss=1">
<title>
<![CDATA[
A Human Liver Cell Atlas: Revealing Cell Type Heterogeneity and Adult Liver Progenitors by Single-Cell RNA-sequencing 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/649194v1?rss=1"
</link>
<description><![CDATA[
The human liver is an essential multifunctional organ, and liver diseases are rising with limited treatment options. However, the cellular complexity and heterogeneity of the liver remain poorly understood. Here, we performed single-cell RNA-sequencing of ~5,000 cells from normal liver tissue of 6 human donors to construct the first human liver cell atlas. Our analysis revealed previously unknown sub-types among endothelial cells, Kupffer cells, and hepatocytes with transcriptome-wide zonation of these populations. We show that the EPCAM+ population is highly heterogeneous and consists of hepatocyte progenitors, cholangiocytes and a MUC6+ stem cell population with a specific potential to form liver organoids. As proof-of-principle, we applied our atlas to unravel phenotypic changes in cells from hepatocellular carcinoma tissue and to investigate cellular phenotypes of human hepatocytes and liver endothelial cells engrafted into a humanized FAH-/- mouse liver. Our human liver cell atlas provides a powerful and innovative resource enabling the discovery of previously unknown cell types in the normal and diseased liver.
]]></description>
<dc:creator>Aizarani, N.</dc:creator>
<dc:creator>Saviano, A.</dc:creator>
<dc:creator>Sagar,</dc:creator>
<dc:creator>Mailly, L.</dc:creator>
<dc:creator>Durand, S.</dc:creator>
<dc:creator>Pessaux, P.</dc:creator>
<dc:creator>Baumert, T. F.</dc:creator>
<dc:creator>Grün, D.</dc:creator>
<dc:date>2019-05-24</dc:date>
<dc:identifier>doi:10.1101/649194</dc:identifier>
<dc:title><![CDATA[A Human Liver Cell Atlas: Revealing Cell Type Heterogeneity and Adult Liver Progenitors by Single-Cell RNA-sequencing]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-05-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/654210v1?rss=1">
<title>
<![CDATA[
Decoding the development of the blood and immune systems during human fetal liver haematopoiesis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/654210v1?rss=1"
</link>
<description><![CDATA[
Definitive haematopoiesis in the fetal liver supports self-renewal and differentiation of haematopoietic stem cells/multipotent progenitors (HSC/MPPs), yet remains poorly defined in humans. Using single cell transcriptome profiling of ~133,000 fetal liver and ~65,000 fetal skin and kidney cells, we identify the repertoire of blood and immune cells in first and early second trimesters of development. From this data, we infer differentiation trajectories from HSC/MPPs, and evaluate the impact of tissue microenvironment on blood and immune cell development. We predict coupling of mast cell differentiation with erythro-megakaryopoiesis and identify physiological erythropoiesis in fetal skin. We demonstrate a shift in fetal liver haematopoietic composition during gestation away from being erythroid-predominant, accompanied by a parallel change in HSC/MPP differentiation potential, which we functionally validate. Our integrated map of fetal liver haematopoiesis provides a blueprint for the study of paediatric blood and immune disorders, and a valuable reference for understanding and harnessing the therapeutic potential of HSC/MPPs.
]]></description>
<dc:creator>Popescu, D.-M.</dc:creator>
<dc:creator>Botting, R. A.</dc:creator>
<dc:creator>Stephenson, E.</dc:creator>
<dc:creator>Green, K.</dc:creator>
<dc:creator>Jardine, L.</dc:creator>
<dc:creator>Calderbank, E. F.</dc:creator>
<dc:creator>Efremova, M.</dc:creator>
<dc:creator>Acres, M.</dc:creator>
<dc:creator>Maunder, D.</dc:creator>
<dc:creator>Vegh, P.</dc:creator>
<dc:creator>Goh, I.</dc:creator>
<dc:creator>Gitton, Y.</dc:creator>
<dc:creator>Park, J.</dc:creator>
<dc:creator>Polanski, K.</dc:creator>
<dc:creator>Vento-Tormo, R.</dc:creator>
<dc:creator>Miao, Z.</dc:creator>
<dc:creator>Rowell, R.</dc:creator>
<dc:creator>McDonald, D.</dc:creator>
<dc:creator>Fletcher, J.</dc:creator>
<dc:creator>Dixon, D.</dc:creator>
<dc:creator>Poyner, E.</dc:creator>
<dc:creator>Reynolds, G.</dc:creator>
<dc:creator>Mather, M.</dc:creator>
<dc:creator>Moldovan, C.</dc:creator>
<dc:creator>Mamanova, L.</dc:creator>
<dc:creator>Greig, F.</dc:creator>
<dc:creator>Young, M. D.</dc:creator>
<dc:creator>Meyer, K.</dc:creator>
<dc:creator>Lisgo, S.</dc:creator>
<dc:creator>Bacardit, J.</dc:creator>
<dc:creator>Fuller, A.</dc:creator>
<dc:creator>Millar, B.</dc:creator>
<dc:creator>Innes, B.</dc:creator>
<dc:creator>Lindsay, S.</dc:creator>
<dc:creator>Stubbington, M. J. T.</dc:creator>
<dc:creator>Kowalczyk, M. D.</dc:creator>
<dc:creator>Li, B. D.</dc:creator>
<dc:creator>Ashenbrg, O.</dc:creator>
<dc:creator>Tabaka, M. D.</dc:creator>
<dc:creator>Dionne, D.</dc:creator>
<dc:creator>Tickle, T. L.</dc:creator>
<dc:creator>Slyper, M.</dc:creator>
<dc:creator>Rozenblatt-Rosen, O.</dc:creator>
<dc:creator>Filby, A.</dc:creator>
<dc:creator>Villani, A</dc:creator>
<dc:date>2019-05-31</dc:date>
<dc:identifier>doi:10.1101/654210</dc:identifier>
<dc:title><![CDATA[Decoding the development of the blood and immune systems during human fetal liver haematopoiesis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-05-31</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/425223v1?rss=1">
<title>
<![CDATA[
Generation of human neural retina transcriptome atlas by single cell RNA sequencing 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/425223v1?rss=1"
</link>
<description><![CDATA[
The retina is a highly specialized neural tissue that senses light and initiates image processing. Although the functional organisation of specific cells within the retina has been well-studied, the molecular profile of many cell types remains unclear in humans. To comprehensively profile cell types in the human retina, we performed single cell RNA-sequencing on 20,009 cells obtained post-mortem from three donors and compiled a reference transcriptome atlas. Using unsupervised clustering analysis, we identified 18 transcriptionally distinct clusters representing all known retinal cells: rod photoreceptors, cone photoreceptors, Muller glia cells, bipolar cells, amacrine cells, retinal ganglion cells, horizontal cells, retinal astrocytes and microglia. Notably, our data captured molecular profiles for healthy and early degenerating rod photoreceptors, and revealed a novel role of MALAT1 in putative rod degeneration. We also demonstrated the use of this retina transcriptome atlas to benchmark pluripotent stem cell-derived cone photoreceptors and an adult Muller glia cell line. This work provides an important reference with unprecedented insights into the transcriptional landscape of human retinal cells, which is fundamental to our understanding of retinal biology and disease.
]]></description>
<dc:creator>Lukowski, S.</dc:creator>
<dc:creator>Lo, C.</dc:creator>
<dc:creator>Sharov, A.</dc:creator>
<dc:creator>Nguyen, Q.</dc:creator>
<dc:creator>Fang, L.</dc:creator>
<dc:creator>Hung, S.</dc:creator>
<dc:creator>Zhu, L.</dc:creator>
<dc:creator>Zhang, T.</dc:creator>
<dc:creator>Nguyen, T.</dc:creator>
<dc:creator>Senabouth, A.</dc:creator>
<dc:creator>Jabbari, J.</dc:creator>
<dc:creator>Welby, E.</dc:creator>
<dc:creator>Sowden, J.</dc:creator>
<dc:creator>Waugh, H.</dc:creator>
<dc:creator>Mackey, A.</dc:creator>
<dc:creator>Pollock, G.</dc:creator>
<dc:creator>Lamb, T.</dc:creator>
<dc:creator>Wang, P.-Y.</dc:creator>
<dc:creator>Hewitt, A.</dc:creator>
<dc:creator>Gillies, M.</dc:creator>
<dc:creator>Powell, J.</dc:creator>
<dc:creator>Wong, R.</dc:creator>
<dc:date>2018-09-24</dc:date>
<dc:identifier>doi:10.1101/425223</dc:identifier>
<dc:title><![CDATA[Generation of human neural retina transcriptome atlas by single cell RNA sequencing]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-09-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/721258v1?rss=1">
<title>
<![CDATA[
Single cell transcriptome atlas of immune cells in human small intestine and in celiac disease 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/721258v1?rss=1"
</link>
<description><![CDATA[
Celiac disease (CeD) is an autoimmune disorder in which ingestion of dietary gluten triggers an immune reaction in the small intestine1,2. The CeD lesion is characterized by crypt hyperplasia, villous atrophy and chronic inflammation with accumulation of leukocytes both in the lamina propria (LP) and in the epithelium3, which eventually leads to destruction of the intestinal epithelium1 and subsequent digestive complications and higher risk of non-hodgkin lymphoma4. A lifetime gluten-free diet is currently the only available treatment5. Gluten-specific LP CD4 T cells and cytotoxic intraepithelial CD8+ T cells are thought to be central in disease pathology1,6-8, however, CeD is a complex immune-mediated disorder and to date the findings are mostly based on analysis of heterogeneous cell populations and on animal models. Here, we comprehensively explore the cellular heterogeneity of CD45+ immune cells in human small intestine using index-sorting single-cell RNA-sequencing9,10. We find that myeloid and mast cell transcriptomes are reshaped in CeD. We observe extensive changes in the proportion and transcriptomes of CD4+ and CD8+ T cells and define a CD3zeta expressing NK-T-like cell population present in the control LP and epithelial layers that is absent and replaced in CeD. Our findings show that the immune landscape is dramatically changed in active CeD which provide new insights and considerably extend the current knowledge of CeD immunopathology.
]]></description>
<dc:creator>Stunnenberg, H.</dc:creator>
<dc:creator>Atlasy, N.</dc:creator>
<dc:creator>Bujko, A.</dc:creator>
<dc:creator>Brazda, P. B.</dc:creator>
<dc:creator>Janssen-Megens, E.</dc:creator>
<dc:creator>Bakkenvold, E. S.</dc:creator>
<dc:creator>Jahnsen, J.</dc:creator>
<dc:creator>Jahsen, F.</dc:creator>
<dc:date>2019-08-01</dc:date>
<dc:identifier>doi:10.1101/721258</dc:identifier>
<dc:title><![CDATA[Single cell transcriptome atlas of immune cells in human small intestine and in celiac disease]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-08-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/733964v1?rss=1">
<title>
<![CDATA[
Single nucleus RNA sequencing maps acinar cell states in a human pancreas cell atlas 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/733964v1?rss=1"
</link>
<description><![CDATA[
Molecular evidence of cellular heterogeneity in the human exocrine pancreas has not been established, due to the local concentration of hydrolytic enzymes that can rapidly degrade cells and RNA upon resection. Here we innovated single-nucleus RNA sequencing protocols, and profiled more than 120,000 cells from adult and neonatal human donors to create the first comprehensive atlas of human pancreas cells, including epithelial and non-epithelial constituents. Adult and neonatal pancreata shared common features, including the presence of previously undetected acinar subtypes, but also showed marked differences in the composition of the endocrine, endothelial, and immune compartments. Spatial cartography, including cell proximity mapping through in situ sequencing, revealed dynamic developmental cell topographies in the endocrine and exocrine pancreas. Our human pancreas cell atlas can be interrogated to understand pancreatic cell biology, and provides a crucial reference set for future comparisons with diseased tissue samples to map the cellular foundations of pancreatic diseases.
]]></description>
<dc:creator>Tosti, L.</dc:creator>
<dc:creator>Hang, Y.</dc:creator>
<dc:creator>Trefzer, T.</dc:creator>
<dc:creator>Steiger, K.</dc:creator>
<dc:creator>Ten, F. W.</dc:creator>
<dc:creator>Lukassen, S.</dc:creator>
<dc:creator>Ballke, S.</dc:creator>
<dc:creator>Kuehl, A. A.</dc:creator>
<dc:creator>Spieckermann, S.</dc:creator>
<dc:creator>Bottino, R.</dc:creator>
<dc:creator>Weichert, W.</dc:creator>
<dc:creator>Kim, S. K.</dc:creator>
<dc:creator>Eils, R.</dc:creator>
<dc:creator>Conrad, C.</dc:creator>
<dc:date>2019-08-14</dc:date>
<dc:identifier>doi:10.1101/733964</dc:identifier>
<dc:title><![CDATA[Single nucleus RNA sequencing maps acinar cell states in a human pancreas cell atlas]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-08-14</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/740415v1?rss=1">
<title>
<![CDATA[
Gene set inference from single-cell sequencing data using a hybrid of matrix factorization and variational autoencoders 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/740415v1?rss=1"
</link>
<description><![CDATA[
Recent advances in single-cell RNA sequencing (scRNA-Seq) have driven the simultaneous measurement of the expression of 1,000s of genes in 1,000s of single cells. These growing data sets allow us to model gene sets in biological networks at an unprecedented level of detail, in spite of heterogenous cell populations. Here, we propose an unsupervised deep neural network model that is a hybrid of matrix factorization and conditional variational autoencoders (CVA), which utilizes weights as matrix factorizations to obtain gene sets, while class-specific inputs to the latent variable space facilitate a plausible identification of cell types. This artificial neural network model seamlessly integrates functional gene set inference, experimental batch effect correction, and static gene identification, which we conceptually prove here for three single-cell RNA-Seq datasets and suggest for future single-cell-gene analytics.
]]></description>
<dc:creator>Lukassen, S.</dc:creator>
<dc:creator>Ten, F. W.</dc:creator>
<dc:creator>Eils, R.</dc:creator>
<dc:creator>Conrad, C.</dc:creator>
<dc:date>2019-08-20</dc:date>
<dc:identifier>doi:10.1101/740415</dc:identifier>
<dc:title><![CDATA[Gene set inference from single-cell sequencing data using a hybrid of matrix factorization and variational autoencoders]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-08-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/741405v1?rss=1">
<title>
<![CDATA[
Lung, spleen and oesophagus tissue remains stable for scRNAseq in cold preservation 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/741405v1?rss=1"
</link>
<description><![CDATA[
BackgroundThe Human Cell Atlas is a large international collaborative effort to map all cell types of the human body. Single cell RNA sequencing can generate high quality data for the delivery of such an atlas. However, delays between fresh sample collection and processing may lead to poor data and difficulties in experimental design. Despite this, there has not yet been a systematic assessment of the effect of cold storage time on the quality of scRNAseqnnResultsThis study assessed the effect of cold storage on fresh healthy spleen, oesophagus and lung from [&ge;]5 donors over 72 hours. We collected 240,000 high quality single cell transcriptomes with detailed cell type annotations and whole genome sequences of donors, enabling future eQTL studies. Our data provide a valuable resource for the study of these three organs and will allow cross-organ comparison of cell types.nnWe see little effect of cold ischaemic time on cell viability, yield, total number of reads per cell and other quality control metrics in any of the tissues within the first 24 hours. However, we observed higher percentage of mitochondrial reads, indicative of cellular stress, and increased contamination by background "ambient RNA" reads in the 72h samples in spleen, which is cell type specific.nnConclusionsIn conclusion, we present robust protocols for tissue preservation for up to 24 hours prior to scRNAseq analysis. This greatly facilitates the logistics of sample collection for Human Cell Atlas or clinical studies since it increases the time frames for sample processing.
]]></description>
<dc:creator>Madissoon, E.</dc:creator>
<dc:creator>Wilbrey-Clark, A. L.</dc:creator>
<dc:creator>Miragaia, R. J.</dc:creator>
<dc:creator>Saeb-Parsy, K.</dc:creator>
<dc:creator>Mahbubani, K. T.</dc:creator>
<dc:creator>Georgakopoulos, N.</dc:creator>
<dc:creator>Harding, P.</dc:creator>
<dc:creator>Polanski, K.</dc:creator>
<dc:creator>Nowicki-Osuch, K.</dc:creator>
<dc:creator>Fitzgerald, R. C.</dc:creator>
<dc:creator>Loudon, K. W.</dc:creator>
<dc:creator>Ferdinand, J. R.</dc:creator>
<dc:creator>Clatworthy, M. R.</dc:creator>
<dc:creator>Tsingene, A.</dc:creator>
<dc:creator>van Dongen, S.</dc:creator>
<dc:creator>Dabrowska, M.</dc:creator>
<dc:creator>Patel, M.</dc:creator>
<dc:creator>Stubbington, M. J.</dc:creator>
<dc:creator>Teichmann, S.</dc:creator>
<dc:creator>Stegle, O.</dc:creator>
<dc:creator>Meyer, K. B.</dc:creator>
<dc:date>2019-08-31</dc:date>
<dc:identifier>doi:10.1101/741405</dc:identifier>
<dc:title><![CDATA[Lung, spleen and oesophagus tissue remains stable for scRNAseq in cold preservation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-08-31</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/746743v1?rss=1">
<title>
<![CDATA[
The enteric nervous system of the human and mouse colon at a single-cell resolution 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/746743v1?rss=1"
</link>
<description><![CDATA[
As the largest branch of the autonomic nervous system, the enteric nervous system (ENS) controls the entire gastrointestinal tract, but remains incompletely characterized. Here, we develop RAISIN RNA-seq, which enables the capture of intact single nuclei along with ribosome-bound mRNA, and use it to profile the adult mouse and human colon to generate a reference map of the ENS at a single-cell resolution. This map reveals an extraordinary diversity of neuron subsets across intestinal locations, ages, and circadian phases, with conserved transcriptional programs that are shared between human and mouse. These data suggest possible revisions to the current model of peristalsis and molecular mechanisms that may allow enteric neurons to orchestrate tissue homeostasis, including immune regulation and stem cell maintenance. Human enteric neurons specifically express risk genes for neuropathic, inflammatory, and extra-intestinal diseases with concomitant gut dysmotility. Our study therefore provides a roadmap to understanding the ENS in health and disease.
]]></description>
<dc:creator>Drokhlyansky, E.</dc:creator>
<dc:creator>Smillie, C. S.</dc:creator>
<dc:creator>Van Wittenberghe, N.</dc:creator>
<dc:creator>Ericsson, M.</dc:creator>
<dc:creator>Griffin, G. K.</dc:creator>
<dc:creator>Dionne, D.</dc:creator>
<dc:creator>Cuoco, M. S.</dc:creator>
<dc:creator>Goder-Reiser, M. N.</dc:creator>
<dc:creator>Sharova, T.</dc:creator>
<dc:creator>Aguirre, A. J.</dc:creator>
<dc:creator>Boland, G. M.</dc:creator>
<dc:creator>Graham, D.</dc:creator>
<dc:creator>Rozenblatt-Rosen, O.</dc:creator>
<dc:creator>Xavier, R. J.</dc:creator>
<dc:creator>Regev, A.</dc:creator>
<dc:date>2019-08-28</dc:date>
<dc:identifier>doi:10.1101/746743</dc:identifier>
<dc:title><![CDATA[The enteric nervous system of the human and mouse colon at a single-cell resolution]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-08-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/766113v1?rss=1">
<title>
<![CDATA[
Resolving the fibrotic niche of human liver cirrhosis using single-cell transcriptomics 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/766113v1?rss=1"
</link>
<description><![CDATA[
Currently there are no effective antifibrotic therapies for liver cirrhosis, a major killer worldwide. To obtain a cellular resolution of directly-relevant pathogenesis and to inform therapeutic design, we profile the transcriptomes of over 100,000 primary human single cells, yielding molecular definitions for the major non-parenchymal cell types present in healthy and cirrhotic human liver. We uncover a novel scar-associated TREM2+CD9+ macrophage subpopulation with a fibrogenic phenotype, that has a distinct differentiation trajectory from circulating monocytes. In the endothelial compartment, we show that newly-defined ACKR1+ and PLVAP+ endothelial cells expand in cirrhosis and are topographically located in the fibrotic septae. Multi-lineage ligand-receptor modelling of specific interactions between the novel scar-associated macrophages, endothelial cells and collagen-producing myofibroblasts in the fibrotic niche, reveals intra-scar activity of several major pathways which promote hepatic fibrosis. Our work dissects unanticipated aspects of the cellular and molecular basis of human organ fibrosis at a single-cell level, and provides the conceptual framework required to discover rational therapeutic targets in liver cirrhosis.
]]></description>
<dc:creator>Ramachandran, P.</dc:creator>
<dc:creator>Dobie, R.</dc:creator>
<dc:creator>Wilson-Kanamori, J. R.</dc:creator>
<dc:creator>Dora, E. F.</dc:creator>
<dc:creator>Henderson, B. E.</dc:creator>
<dc:creator>Taylor, R. S.</dc:creator>
<dc:creator>Matchett, K. P.</dc:creator>
<dc:creator>Portman, J. R.</dc:creator>
<dc:creator>Efremova, M.</dc:creator>
<dc:creator>Vento-Tormo, R.</dc:creator>
<dc:creator>Luu, N. T.</dc:creator>
<dc:creator>Weston, C. J.</dc:creator>
<dc:creator>Newsome, P. N.</dc:creator>
<dc:creator>Harrison, E. M.</dc:creator>
<dc:creator>Mole, D. J.</dc:creator>
<dc:creator>Wigmore, S. J.</dc:creator>
<dc:creator>Iredale, J. P.</dc:creator>
<dc:creator>Tacke, F.</dc:creator>
<dc:creator>Pollard, J. W.</dc:creator>
<dc:creator>Ponting, C. P.</dc:creator>
<dc:creator>Marioni, J.</dc:creator>
<dc:creator>Teichmann, S. A.</dc:creator>
<dc:creator>Henderson, N. C.</dc:creator>
<dc:date>2019-09-12</dc:date>
<dc:identifier>doi:10.1101/766113</dc:identifier>
<dc:title><![CDATA[Resolving the fibrotic niche of human liver cirrhosis using single-cell transcriptomics]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-09-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/761429v1?rss=1">
<title>
<![CDATA[
A single-cell and single-nucleus RNA-seq toolbox for fresh and frozen human tumors 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/761429v1?rss=1"
</link>
<description><![CDATA[
Single cell genomics is essential to chart the complex tumor ecosystem. While single cell RNA-Seq (scRNA-Seq) profiles RNA from cells dissociated from fresh tumor tissues, single nucleus RNA-Seq (snRNA-Seq) is needed to profile frozen or hard-to-dissociate tumors. Each strategy requires modifications to fit the unique characteristics of different tissue and tumor types, posing a barrier to adoption. Here, we developed a systematic toolbox for profiling fresh and frozen clinical tumor samples using scRNA-Seq and snRNA-Seq, respectively. We tested eight tumor types of varying tissue and sample characteristics (resection, biopsy, ascites, and orthotopic patient-derived xenograft): lung cancer, metastatic breast cancer, ovarian cancer, melanoma, neuroblastoma, pediatric sarcoma, glioblastoma, pediatric high-grade glioma, and chronic lymphocytic leukemia. Analyzing 212,498 cells and nuclei from 39 clinical samples, we evaluated protocols by cell quality, recovery rate, and cellular composition. We optimized protocols for fresh tissue dissociation for different tumor types using a decision tree to account for the technical and biological variation between clinical samples. We established methods for nucleus isolation from OCT embedded and fresh-frozen tissues, with an optimization matrix varying mechanical force, buffer, and detergent. scRNA-Seq and snRNA-Seq from matched samples recovered the same cell types and intrinsic expression profiles, but at different proportions. Our work provides direct guidance across a broad range of tumors, including criteria for testing and selecting methods from the toolbox for other tumors, thus paving the way for charting tumor atlases.
]]></description>
<dc:creator>Slyper, M.</dc:creator>
<dc:creator>Porter, C. B. M.</dc:creator>
<dc:creator>Ashenberg, O.</dc:creator>
<dc:creator>Waldman, J.</dc:creator>
<dc:creator>Drokhlyansky, E.</dc:creator>
<dc:creator>Wakiro, I.</dc:creator>
<dc:creator>Smillie, C.</dc:creator>
<dc:creator>Smith-Rosario, G.</dc:creator>
<dc:creator>Wu, J.</dc:creator>
<dc:creator>Dionne, D.</dc:creator>
<dc:creator>Vigneau, S.</dc:creator>
<dc:creator>Jane-Valbuena, J.</dc:creator>
<dc:creator>Napolitano, S.</dc:creator>
<dc:creator>Su, M.-J.</dc:creator>
<dc:creator>Patel, A. G.</dc:creator>
<dc:creator>Karlstrom, A.</dc:creator>
<dc:creator>Gritsch, S.</dc:creator>
<dc:creator>Nomura, M.</dc:creator>
<dc:creator>Waghray, A.</dc:creator>
<dc:creator>Gohil, S. H.</dc:creator>
<dc:creator>Tsankov, A. M.</dc:creator>
<dc:creator>Jerby-Arnon, L.</dc:creator>
<dc:creator>Cohen, O.</dc:creator>
<dc:creator>Klughammer, J.</dc:creator>
<dc:creator>Rosen, Y.</dc:creator>
<dc:creator>Gould, J.</dc:creator>
<dc:creator>Li, B.</dc:creator>
<dc:creator>Nguyen, L.</dc:creator>
<dc:creator>Wu, C. J.</dc:creator>
<dc:creator>Izar, B.</dc:creator>
<dc:creator>Haq, R.</dc:creator>
<dc:creator>Hodi, F. S.</dc:creator>
<dc:creator>Yoon, C. H.</dc:creator>
<dc:creator>Hata, A. N.</dc:creator>
<dc:creator>Baker, S. J.</dc:creator>
<dc:creator>Suva, M. L.</dc:creator>
<dc:creator>Bueno, R.</dc:creator>
<dc:creator>Stover, E. H.</dc:creator>
<dc:creator>Matulonis, U. A.</dc:creator>
<dc:creator>Clay, M. R.</dc:creator>
<dc:creator>Dyer, M. A.</dc:creator>
<dc:creator>Collins, N. B.</dc:creator>
<dc:creator>Wagle, N.</dc:creator>
<dc:creator>Rotem, A.</dc:creator>
<dc:creator>Johnson, B. E.</dc:creator>
<dc:date>2019-09-12</dc:date>
<dc:identifier>doi:10.1101/761429</dc:identifier>
<dc:title><![CDATA[A single-cell and single-nucleus RNA-seq toolbox for fresh and frozen human tumors]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-09-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/585901v1?rss=1">
<title>
<![CDATA[
Systematic Comparison of High-throughput Single-Cell and Single-Nucleus Transcriptomes during Cardiomyocyte Differentiation 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/585901v1?rss=1"
</link>
<description><![CDATA[
A comprehensive reference map of all cell types in the human body is necessary for improving our understanding of fundamental biological processes and in diagnosing and treating disease. High-throughput single-cell RNA sequencing techniques have emerged as powerful tools to identify and characterize cell types in complex and heterogeneous tissues. However, extracting intact cells from tissues and organs is often technically challenging or impossible, for example in heart or brain tissue. Single-nucleus RNA sequencing provides an alternative way to obtain transcriptome profiles of such tissues. To systematically assess the differences between high-throughput single-cell and single-nuclei RNA-seq approaches, we compared Drop-seq and DroNc-seq, two microfluidic-based 3 RNA capture technologies that profile total cellular and nuclear RNA, respectively, during a time course experiment of human induced pluripotent stem cells (iPSCs) differentiating into cardiomyocytes. Clustering of time-series transcriptomes from Drop-seq and DroNc-seq revealed six distinct cell types, five of which were found in both techniques. Furthermore, single-cell trajectories reconstructed from both techniques reproduced expected differentiation dynamics. We then applied DroNc-seq to postmortem heart tissue to test its performance on heterogeneous human tissue samples. We compared the detected cell types from primary tissue with iPSC-derived cardiomyocytes profiled with DroNc-seq. Our data confirm that DroNc-seq yields similar results to Drop-seq on matched samples and can be successfully used to generate reference maps for the human cell atlas.
]]></description>
<dc:creator>Selewa, A.</dc:creator>
<dc:creator>Dohn, R.</dc:creator>
<dc:creator>Eckart, H.</dc:creator>
<dc:creator>Lozano, S.</dc:creator>
<dc:creator>Xie, B.</dc:creator>
<dc:creator>Gauchat, E.</dc:creator>
<dc:creator>Elorbany, R.</dc:creator>
<dc:creator>Rhodes, K.</dc:creator>
<dc:creator>Burnett, J.</dc:creator>
<dc:creator>Gilad, Y.</dc:creator>
<dc:creator>Pott, S.</dc:creator>
<dc:creator>Basu, A.</dc:creator>
<dc:date>2019-03-22</dc:date>
<dc:identifier>doi:10.1101/585901</dc:identifier>
<dc:title><![CDATA[Systematic Comparison of High-throughput Single-Cell and Single-Nucleus Transcriptomes during Cardiomyocyte Differentiation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-03-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/800748v1?rss=1">
<title>
<![CDATA[
Segmentation-free inference of cell-types from in situ transcriptomics data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/800748v1?rss=1"
</link>
<description><![CDATA[
Multiplexed fluorescence in situ hybridization techniques have enabled cell-type identification, linking transcriptional heterogeneity with spatial heterogeneity of cells. However, inaccurate cell segmentation reduces the efficacy of cell-type identification and tissue characterization. Here, we present a novel method called Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM), a robust cell segmentation-free computational framework for identifying cell-types and tissue domains in 2D and 3D. SSAM is applicable to a variety of in situ transcriptomics techniques and capable of integrating prior knowledge of cell types. We apply SSAM to three mouse brain tissue images: the somatosensory cortex imaged by osmFISH, the hypothalamic preoptic region by MERFISH, and the visual cortex by multiplexed smFISH. We found that SSAM detects regions occupied by known cell types that were previously missed and discovers new cell types.
]]></description>
<dc:creator>Park, J.</dc:creator>
<dc:creator>Choi, W.</dc:creator>
<dc:creator>Tiesmeyer, S.</dc:creator>
<dc:creator>Long, B.</dc:creator>
<dc:creator>Borm, L. E.</dc:creator>
<dc:creator>Garren, E.</dc:creator>
<dc:creator>Nguyen, T. N.</dc:creator>
<dc:creator>Codeluppi, S.</dc:creator>
<dc:creator>Schlesner, M.</dc:creator>
<dc:creator>Tasic, B.</dc:creator>
<dc:creator>Eils, R.</dc:creator>
<dc:creator>Ishaque, N.</dc:creator>
<dc:date>2019-10-13</dc:date>
<dc:identifier>doi:10.1101/800748</dc:identifier>
<dc:title><![CDATA[Segmentation-free inference of cell-types from in situ transcriptomics data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-10-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/853457v1?rss=1">
<title>
<![CDATA[
Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/853457v1?rss=1"
</link>
<description><![CDATA[
Single-cell RNA-Seq (scRNA-seq) has become an invaluable tool for studying biological systems in health and diseases. While dimensionality reduction is a crucial step in interpreting the relation between cells based on scRNA-seq, current methods often are hampered by "crowding" of cells in the center of the latent space, biased by batch effects, or inadequately capture developmental relationships. Here, we introduced scPhere, a scalable deep generative model to embed cells into low-dimensional hyperspherical or hyperbolic spaces, as a more accurate representation of the data. ScPhere resolves cell crowding, corrects multiple, complex batch factors, facilitates interactive visualization of large datasets, and gracefully uncovers pseudotemporal trajectories. We demonstrate scPhere on six large datasets in complex tissue from human patients or animal development, demonstrating how it controls for both technical and biological factors and highlights complex cellular relations and biological insights.
]]></description>
<dc:creator>Ding, J.</dc:creator>
<dc:creator>Regev, A.</dc:creator>
<dc:date>2019-11-25</dc:date>
<dc:identifier>doi:10.1101/853457</dc:identifier>
<dc:title><![CDATA[Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-11-25</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2019.12.12.871657v1?rss=1">
<title>
<![CDATA[
Distinct microbial and immune niches of the human colon 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2019.12.12.871657v1?rss=1"
</link>
<description><![CDATA[
Gastrointestinal microbiota and immune cells interact closely and display regional specificity, but little is known about how these communities differ with location. Here, we simultaneously assess microbiota and single immune cells across the healthy, adult human colon, with paired characterisation of immune cells in the mesenteric lymph nodes, to delineate colonic immune niches at steady-state. We describe distinct T helper cell activation and migration profiles along the colon and characterise the transcriptional adaptation trajectory of T regulatory cells between lymphoid tissue and colon. Finally, we show increasing B cell accumulation, clonal expansion and mutational frequency from caecum to sigmoid colon, and link this to the increasing number of reactive bacterial species.
]]></description>
<dc:creator>James, K. R.</dc:creator>
<dc:creator>Gomes, T.</dc:creator>
<dc:creator>Elmentaite, R.</dc:creator>
<dc:creator>Kumar, N.</dc:creator>
<dc:creator>Gulliver, E. L.</dc:creator>
<dc:creator>King, H. W.</dc:creator>
<dc:creator>Stares, M. D.</dc:creator>
<dc:creator>Bareham, B. R.</dc:creator>
<dc:creator>Ferdinand, J. R.</dc:creator>
<dc:creator>Petrova, V.</dc:creator>
<dc:creator>Polanski, K.</dc:creator>
<dc:creator>Forster, S. C.</dc:creator>
<dc:creator>Jarvis, L. B.</dc:creator>
<dc:creator>Suchanek, O.</dc:creator>
<dc:creator>Howlett, S.</dc:creator>
<dc:creator>James, L.</dc:creator>
<dc:creator>Jones, J. L.</dc:creator>
<dc:creator>Meyer, K. B.</dc:creator>
<dc:creator>Clatworthy, M. R.</dc:creator>
<dc:creator>Saeb-Parsy, K.</dc:creator>
<dc:creator>Lawley, T.</dc:creator>
<dc:creator>Teichmann, S. A.</dc:creator>
<dc:date>2019-12-12</dc:date>
<dc:identifier>doi:10.1101/2019.12.12.871657</dc:identifier>
<dc:title><![CDATA[Distinct microbial and immune niches of the human colon]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-12-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.01.15.897066v1?rss=1">
<title>
<![CDATA[
Sampling artifacts in single-cell genomics cohort studies 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.01.15.897066v1?rss=1"
</link>
<description><![CDATA[
Robust protocols and automation now enable large-scale single-cell RNA and ATAC sequencing experiments and their application on biobank and clinical cohorts. However, technical biases introduced during sample acquisition can hinder solid, reproducible results and a systematic benchmarking is required before entering large-scale data production. Here, we report the existence and extent of gene expression and chromatin accessibility artifacts introduced during sampling and identify experimental and computational solutions for their prevention.
]]></description>
<dc:creator>Massoni-Badosa, R.</dc:creator>
<dc:creator>Iacono, G.</dc:creator>
<dc:creator>Moutinho, C.</dc:creator>
<dc:creator>Kulis, M.</dc:creator>
<dc:creator>Palau, N.</dc:creator>
<dc:creator>Marchese, D.</dc:creator>
<dc:creator>Rodriguez-Ubreva, J.</dc:creator>
<dc:creator>Ballestar, E.</dc:creator>
<dc:creator>Rodriguez-Esteban, G.</dc:creator>
<dc:creator>Marsal, S.</dc:creator>
<dc:creator>Aymerich, M.</dc:creator>
<dc:creator>Colomer, D.</dc:creator>
<dc:creator>Campo, E.</dc:creator>
<dc:creator>Jula, A.</dc:creator>
<dc:creator>Martin-Subero, J. I.</dc:creator>
<dc:creator>Heyn, H.</dc:creator>
<dc:date>2020-01-15</dc:date>
<dc:identifier>doi:10.1101/2020.01.15.897066</dc:identifier>
<dc:title><![CDATA[Sampling artifacts in single-cell genomics cohort studies]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-01-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.01.28.911115v1?rss=1">
<title>
<![CDATA[
A cell atlas of human thymic development defines T cell repertoire formation 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.01.28.911115v1?rss=1"
</link>
<description><![CDATA[
The thymus provides a nurturing environment for the differentiation and selection of T cells, a process orchestrated by their interaction with multiple thymic cell types. We utilised single-cell RNA-sequencing (scRNA-seq) to create a cell census of the human thymus and to reconstruct T-cell differentiation trajectories and T-cell receptor (TCR) recombination kinetics. Using this approach, we identified and located in situ novel CD8+ T-cell populations, thymic fibroblast subtypes and activated dendritic cell (aDC) states. In addition, we reveal a bias in TCR recombination and selection, which is attributed to genomic position and suggests later commitment of the CD8+ T-cell lineage. Taken together, our data provide a comprehensive atlas of the human thymus across the lifespan with new insights into human T-cell development.
]]></description>
<dc:creator>Park, J.-E.</dc:creator>
<dc:creator>Botting, R. A.</dc:creator>
<dc:creator>Dominguez Conde, C.</dc:creator>
<dc:creator>Popescu, D.-M.</dc:creator>
<dc:creator>Lavaert, M.</dc:creator>
<dc:creator>Kunz, D. J.</dc:creator>
<dc:creator>Stephenson, E.</dc:creator>
<dc:creator>Ragazzini, R.</dc:creator>
<dc:creator>Tuck, E.</dc:creator>
<dc:creator>Wilbrey-Clark, A. L.</dc:creator>
<dc:creator>Ferdinand, J. R.</dc:creator>
<dc:creator>Webb, S.</dc:creator>
<dc:creator>Maunder, D.</dc:creator>
<dc:creator>Vandamme, N.</dc:creator>
<dc:creator>Mahbubani, K. T.</dc:creator>
<dc:creator>Polanski, K.</dc:creator>
<dc:creator>Mamanova, L.</dc:creator>
<dc:creator>Fuller, A.</dc:creator>
<dc:creator>Filby, A.</dc:creator>
<dc:creator>Reynolds, G.</dc:creator>
<dc:creator>Dixon, D.</dc:creator>
<dc:creator>Saeb-Parsy, K.</dc:creator>
<dc:creator>Lisgo, S.</dc:creator>
<dc:creator>Henderson, D.</dc:creator>
<dc:creator>Vento-Tormo, R.</dc:creator>
<dc:creator>Meyer, K. B.</dc:creator>
<dc:creator>Saeys, Y.</dc:creator>
<dc:creator>Bonfanti, P.</dc:creator>
<dc:creator>Behjati, S.</dc:creator>
<dc:creator>Clatworthy, M. R.</dc:creator>
<dc:creator>Taghon, T.</dc:creator>
<dc:creator>Haniffa, M.</dc:creator>
<dc:creator>Teichmann, S. A.</dc:creator>
<dc:date>2020-01-28</dc:date>
<dc:identifier>doi:10.1101/2020.01.28.911115</dc:identifier>
<dc:title><![CDATA[A cell atlas of human thymic development defines T cell repertoire formation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-01-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.03.13.991455v1?rss=1">
<title>
<![CDATA[
SARS-CoV-2 receptor ACE2 and TMPRSS2 are predominantly expressed in a transient secretory cell type in subsegmental bronchial branches 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.03.13.991455v1?rss=1"
</link>
<description><![CDATA[
The SARS-CoV-2 pandemic affecting the human respiratory system severely challenges public health and urgently demands for increasing our understanding of COVID-19 pathogenesis, especially host factors facilitating virus infection and replication. SARS-CoV-2 was reported to enter cells via binding to ACE2, followed by its priming by TMPRSS2. Here, we investigate ACE2 and TMPRSS2 expression levels and their distribution across cell types in lung tissue (twelve donors, 39,778 cells) and in cells derived from subsegmental bronchial branches (four donors, 17,521 cells) by single nuclei and single cell RNA sequencing, respectively. While TMPRSS2 is expressed in both tissues, in the subsegmental bronchial branches ACE2 is predominantly expressed in a transient secretory cell type. Interestingly, these transiently differentiating cells show an enrichment for pathways related to RHO GTPase function and viral processes suggesting increased vulnerability for SARS-CoV-2 infection. Our data provide a rich resource for future investigations of COVID-19 infection and pathogenesis.
]]></description>
<dc:creator>Lukassen, S.</dc:creator>
<dc:creator>Chua, R. L.</dc:creator>
<dc:creator>Trefzer, T.</dc:creator>
<dc:creator>Kahn, N. C.</dc:creator>
<dc:creator>Schneider, M. A.</dc:creator>
<dc:creator>Muley, T.</dc:creator>
<dc:creator>Winter, H.</dc:creator>
<dc:creator>Meister, M.</dc:creator>
<dc:creator>Veith, C.</dc:creator>
<dc:creator>Boots, A. W.</dc:creator>
<dc:creator>Hennig, B. P.</dc:creator>
<dc:creator>Kreuter, M.</dc:creator>
<dc:creator>Conrad, C.</dc:creator>
<dc:creator>Eils, R.</dc:creator>
<dc:date>2020-03-14</dc:date>
<dc:identifier>doi:10.1101/2020.03.13.991455</dc:identifier>
<dc:title><![CDATA[SARS-CoV-2 receptor ACE2 and TMPRSS2 are predominantly expressed in a transient secretory cell type in subsegmental bronchial branches]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-03-14</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.04.03.024075v1?rss=1">
<title>
<![CDATA[
Cells and gene expression programs in the adult human heart 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.04.03.024075v1?rss=1"
</link>
<description><![CDATA[
Cardiovascular disease is the leading cause of death worldwide. Advanced insights into disease mechanisms and strategies to improve therapeutic opportunities require deeper understanding of the molecular processes of the normal heart. Knowledge of the full repertoire of cardiac cells and their gene expression profiles is a fundamental first step in this endeavor. Here, using large-scale single cell and nuclei transcriptomic profiling together with state-of-the-art analytical techniques, we characterise the adult human heart cellular landscape covering six anatomical cardiac regions (left and right atria and ventricles, apex and interventricular septum). Our results highlight the cellular heterogeneity of cardiomyocytes, pericytes and fibroblasts, revealing distinct subsets in the atria and ventricles indicative of diverse developmental origins and specialized properties. Further we define the complexity of the cardiac vascular network which includes clusters of arterial, capillary, venous, lymphatic endothelial cells and an atrial-enriched population. By comparing cardiac cells to skeletal muscle and kidney, we identify cardiac tissue resident macrophage subsets with transcriptional signatures indicative of both inflammatory and reparative phenotypes. Further, inference of cell-cell interactions highlight a macrophage-fibroblast-cardiomyocyte network that differs between atria and ventricles, and compared to skeletal muscle. We expect this reference human cardiac cell atlas to advance mechanistic studies of heart homeostasis and disease.
]]></description>
<dc:creator>Litvinukova, M.</dc:creator>
<dc:creator>Talavera-Lopez, C.</dc:creator>
<dc:creator>Maatz, H.</dc:creator>
<dc:creator>Reichart, D.</dc:creator>
<dc:creator>Worth, C. L.</dc:creator>
<dc:creator>Lindberg, E. L.</dc:creator>
<dc:creator>Kanda, M.</dc:creator>
<dc:creator>Polanski, K.</dc:creator>
<dc:creator>Fasouli, E. S.</dc:creator>
<dc:creator>Samari, S.</dc:creator>
<dc:creator>Roberts, K.</dc:creator>
<dc:creator>Tuck, E.</dc:creator>
<dc:creator>Heinig, M.</dc:creator>
<dc:creator>DeLaughter, D.</dc:creator>
<dc:creator>McDonough, B.</dc:creator>
<dc:creator>Wakimoto, H.</dc:creator>
<dc:creator>Gorham, J. M.</dc:creator>
<dc:creator>Nadelmann, E.</dc:creator>
<dc:creator>Mahbubani, K. T.</dc:creator>
<dc:creator>Saeb-Parsy, K.</dc:creator>
<dc:creator>Patone, G.</dc:creator>
<dc:creator>Boyle, J. J.</dc:creator>
<dc:creator>Zhang, H.</dc:creator>
<dc:creator>Zhang, H.</dc:creator>
<dc:creator>Viveiros, A.</dc:creator>
<dc:creator>Oudit, G.</dc:creator>
<dc:creator>Bayraktar, O.</dc:creator>
<dc:creator>Seidman, J. G.</dc:creator>
<dc:creator>Seidman, C.</dc:creator>
<dc:creator>Noseda, M.</dc:creator>
<dc:creator>Hubner, N.</dc:creator>
<dc:creator>Teichmann, S. A.</dc:creator>
<dc:date>2020-04-05</dc:date>
<dc:identifier>doi:10.1101/2020.04.03.024075</dc:identifier>
<dc:title><![CDATA[Cells and gene expression programs in the adult human heart]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-04-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.04.10.022103v1?rss=1">
<title>
<![CDATA[
Single-cell atlas of a non-human primate reveals new pathogenic mechanisms of COVID-19 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.04.10.022103v1?rss=1"
</link>
<description><![CDATA[
Stopping COVID-19 is a priority worldwide. Understanding which cell types are targeted by SARS-CoV-2 virus, whether interspecies differences exist, and how variations in cell state influence viral entry is fundamental for accelerating therapeutic and preventative approaches. In this endeavor, we profiled the transcriptome of nine tissues from a Macaca fascicularis monkey at single-cell resolution. The distribution of SARS-CoV-2 facilitators, ACE2 and TMRPSS2, in different cell subtypes showed substantial heterogeneity across lung, kidney, and liver. Through co-expression analysis, we identified immunomodulatory proteins such as IDO2 and ANPEP as potential SARS-CoV-2 targets responsible for immune cell exhaustion. Furthermore, single-cell chromatin accessibility analysis of the kidney unveiled a plausible link between IL6-mediated innate immune responses aiming to protect tissue and enhanced ACE2 expression that could promote viral entry. Our work constitutes a unique resource for understanding the physiology and pathophysiology of two phylogenetically close species, which might guide in the development of therapeutic approaches in humans.

Bullet pointsO_LIWe generated a single-cell transcriptome atlas of 9 monkey tissues to study COVID-19.
C_LIO_LIACE2+TMPRSS2+ epithelial cells of lung, kidney and liver are targets for SARS-CoV-2.
C_LIO_LIACE2 correlation analysis shows IDO2 and ANPEP as potential therapeutic opportunities.
C_LIO_LIWe unveil a link between IL6, STAT transcription factors and boosted SARS-CoV-2 entry.
C_LI
]]></description>
<dc:creator>Han, L.</dc:creator>
<dc:creator>Wei, X.</dc:creator>
<dc:creator>Liu, C.</dc:creator>
<dc:creator>Volpe, G.</dc:creator>
<dc:creator>Wang, Z.</dc:creator>
<dc:creator>Pan, T.</dc:creator>
<dc:creator>Yuan, Y.</dc:creator>
<dc:creator>Lei, Y.</dc:creator>
<dc:creator>Lai, Y.</dc:creator>
<dc:creator>Ward, C.</dc:creator>
<dc:creator>Yu, Y.</dc:creator>
<dc:creator>Wang, M.</dc:creator>
<dc:creator>Shi, Q.</dc:creator>
<dc:creator>Wu, T.</dc:creator>
<dc:creator>Wu, L.</dc:creator>
<dc:creator>Liu, Y.</dc:creator>
<dc:creator>Wang, C.</dc:creator>
<dc:creator>Zhang, Y.</dc:creator>
<dc:creator>Sun, H.</dc:creator>
<dc:creator>Yu, H.</dc:creator>
<dc:creator>Zhuang, Z.</dc:creator>
<dc:creator>Tang, T.</dc:creator>
<dc:creator>Huang, Y.</dc:creator>
<dc:creator>Lu, H.</dc:creator>
<dc:creator>Xu, L.</dc:creator>
<dc:creator>Xu, J.</dc:creator>
<dc:creator>Cheng, M.</dc:creator>
<dc:creator>Liu, Y.</dc:creator>
<dc:creator>Wong, C. W.</dc:creator>
<dc:creator>Tan, T.</dc:creator>
<dc:creator>Ji, W.</dc:creator>
<dc:creator>Maxwell, P. H.</dc:creator>
<dc:creator>Yang, H.</dc:creator>
<dc:creator>Wang, J.</dc:creator>
<dc:creator>Zhu, S.</dc:creator>
<dc:creator>Liu, S.</dc:creator>
<dc:creator>Xu, X.</dc:creator>
<dc:creator>Hou, Y.</dc:creator>
<dc:creator>Esteban, M. A.</dc:creator>
<dc:creator>Liu, L.</dc:creator>
<dc:date>2020-04-10</dc:date>
<dc:identifier>doi:10.1101/2020.04.10.022103</dc:identifier>
<dc:title><![CDATA[Single-cell atlas of a non-human primate reveals new pathogenic mechanisms of COVID-19]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-04-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.04.07.007237v1?rss=1">
<title>
<![CDATA[
Single cell profiling of immature human postnatal thymocytes resolves the complexity of intra-thymic lineage differentiation and thymus seeding precursors. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.04.07.007237v1?rss=1"
</link>
<description><![CDATA[
During postnatal life, thymopoiesis depends on the continuous colonization of the thymus by bone marrow derived hematopoietic progenitors that migrate through the bloodstream. In human, the nature of these thymus immigrants has remained unclear. Here, we employ single-cell RNA sequencing on approximately 70.000 CD34+ thymocytes to unravel the heterogeneity of the human immature postnatal thymocytes. Integration of bone marrow and peripheral blood precursors datasets identifies several putative thymus seeding precursors that display heterogeneity for currently used surface markers as revealed by CITEseq. Besides T cell precursors, we discover branches of intrathymic developing dendritic cells with predominantly plasmacytoid DCs. Trough trajectory inference, we delineate the transcriptional dynamics underlying early human T-lineage development from which we predict transcription factor modules that drive stage-specific steps of human T cell development. Thus, our work resolves the heterogeneity of thymus seeding precursors in human and reveals the molecular mechanisms that drive their in vivo cell fate.
]]></description>
<dc:creator>Lavaert, M.</dc:creator>
<dc:creator>Liang, K. L.</dc:creator>
<dc:creator>Vandamme, N.</dc:creator>
<dc:creator>Park, J.-E.</dc:creator>
<dc:creator>Roels, J.</dc:creator>
<dc:creator>Kowalczyk, M. S.</dc:creator>
<dc:creator>Li, B.</dc:creator>
<dc:creator>Ashenberg, O.</dc:creator>
<dc:creator>Tabaka, M.</dc:creator>
<dc:creator>Dionne, D.</dc:creator>
<dc:creator>Tickle, T. L.</dc:creator>
<dc:creator>Slyper, M.</dc:creator>
<dc:creator>Rozenblatt-Rosen, O.</dc:creator>
<dc:creator>Vandekerckhove, B.</dc:creator>
<dc:creator>Leclercq, G.</dc:creator>
<dc:creator>Regev, A.</dc:creator>
<dc:creator>Van Vlierberghe, P.</dc:creator>
<dc:creator>Guilliams, M.</dc:creator>
<dc:creator>Teichmann, S. A.</dc:creator>
<dc:creator>Saeys, Y.</dc:creator>
<dc:creator>Taghon, T.</dc:creator>
<dc:date>2020-04-08</dc:date>
<dc:identifier>doi:10.1101/2020.04.07.007237</dc:identifier>
<dc:title><![CDATA[Single cell profiling of immature human postnatal thymocytes resolves the complexity of intra-thymic lineage differentiation and thymus seeding precursors.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-04-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.04.19.049254v1?rss=1">
<title>
<![CDATA[
Integrated analyses of single-cell atlases reveal age, gender, and smoking status associations with cell type-specific expression of mediators of SARS-CoV-2 viral entry and highlights inflammatory programs in putative target cells 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.04.19.049254v1?rss=1"
</link>
<description><![CDATA[
The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, creates an urgent need for identifying molecular mechanisms that mediate viral entry, propagation, and tissue pathology. Cell membrane bound angiotensin-converting enzyme 2 (ACE2) and associated proteases, transmembrane protease serine 2 (TMPRSS2) and Cathepsin L (CTSL), were previously identified as mediators of SARS-CoV2 cellular entry. Here, we assess the cell type-specific RNA expression of ACE2, TMPRSS2, and CTSL through an integrated analysis of 107 single-cell and single-nucleus RNA-Seq studies, including 22 lung and airways datasets (16 unpublished), and 85 datasets from other diverse organs. Joint expression of ACE2 and the accessory proteases identifies specific subsets of respiratory epithelial cells as putative targets of viral infection in the nasal passages, airways, and alveoli. Cells that co-express ACE2 and proteases are also identified in cells from other organs, some of which have been associated with COVID-19 transmission or pathology, including gut enterocytes, corneal epithelial cells, cardiomyocytes, heart pericytes, olfactory sustentacular cells, and renal epithelial cells. Performing the first meta-analyses of scRNA-seq studies, we analyzed 1,176,683 cells from 282 nasal, airway, and lung parenchyma samples from 164 donors spanning fetal, childhood, adult, and elderly age groups, associate increased levels of ACE2, TMPRSS2, and CTSL in specific cell types with increasing age, male gender, and smoking, all of which are epidemiologically linked to COVID-19 susceptibility and outcomes. Notably, there was a particularly low expression of ACE2 in the few young pediatric samples in the analysis. Further analysis reveals a gene expression program shared by ACE2+TMPRSS2+ cells in nasal, lung and gut tissues, including genes that may mediate viral entry, subtend key immune functions, and mediate epithelial-macrophage cross-talk. Amongst these are IL6, its receptor and co-receptor, IL1R, TNF response pathways, and complement genes. Cell type specificity in the lung and airways and smoking effects were conserved in mice. Our analyses suggest that differences in the cell type-specific expression of mediators of SARS-CoV-2 viral entry may be responsible for aspects of COVID-19 epidemiology and clinical course, and point to putative molecular pathways involved in disease susceptibility and pathogenesis.
]]></description>
<dc:creator>Muus, C.</dc:creator>
<dc:creator>Luecken, M. D.</dc:creator>
<dc:creator>Eraslan, G.</dc:creator>
<dc:creator>Waghray, A.</dc:creator>
<dc:creator>Heimberg, G.</dc:creator>
<dc:creator>Sikkema, L.</dc:creator>
<dc:creator>Kobayashi, Y.</dc:creator>
<dc:creator>Vaishnav, E. D.</dc:creator>
<dc:creator>Subramanian, A.</dc:creator>
<dc:creator>Smillie, C.</dc:creator>
<dc:creator>Jagadeesh, K.</dc:creator>
<dc:creator>Duong, E. T.</dc:creator>
<dc:creator>Fiskin, E.</dc:creator>
<dc:creator>Torlai Triglia, E.</dc:creator>
<dc:creator>Ansari, M.</dc:creator>
<dc:creator>Cai, P.</dc:creator>
<dc:creator>Lin, B.</dc:creator>
<dc:creator>Buchanan, J.</dc:creator>
<dc:creator>Chen, S.</dc:creator>
<dc:creator>Shu, J.</dc:creator>
<dc:creator>Haber, A. L.</dc:creator>
<dc:creator>Chung, H.</dc:creator>
<dc:creator>Montoro, D. T.</dc:creator>
<dc:creator>Adams, T.</dc:creator>
<dc:creator>Aliee, H.</dc:creator>
<dc:creator>Allon, S. J.</dc:creator>
<dc:creator>Andrusivova, Z.</dc:creator>
<dc:creator>Angelidis, I.</dc:creator>
<dc:creator>Ashenberg, O.</dc:creator>
<dc:creator>Bassler, K.</dc:creator>
<dc:creator>Becavin, C.</dc:creator>
<dc:creator>Benhar, I.</dc:creator>
<dc:creator>Bergenstrahle, J.</dc:creator>
<dc:creator>Bergenstrahle, L.</dc:creator>
<dc:creator>Bolt, L.</dc:creator>
<dc:creator>Braun, E.</dc:creator>
<dc:creator>Bui, L. T.</dc:creator>
<dc:creator>Chaffin, M.</dc:creator>
<dc:creator>Chichelnitskiy, E.</dc:creator>
<dc:creator>Chiou, J.</dc:creator>
<dc:creator>Conlon, T. M.</dc:creator>
<dc:creator>Cuoco, M. S.</dc:creator>
<dc:creator>Deprez, M.</dc:creator>
<dc:creator>Fischer, D. S.</dc:creator>
<dc:creator>G</dc:creator>
<dc:date>2020-04-20</dc:date>
<dc:identifier>doi:10.1101/2020.04.19.049254</dc:identifier>
<dc:title><![CDATA[Integrated analyses of single-cell atlases reveal age, gender, and smoking status associations with cell type-specific expression of mediators of SARS-CoV-2 viral entry and highlights inflammatory programs in putative target cells]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-04-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/121202v1?rss=1">
<title>
<![CDATA[
The Human Cell Atlas 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/121202v1?rss=1"
</link>
<description><![CDATA[
The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body, by undertaking a Human Cell Atlas Project as an international collaborative effort. The aim would be to define all human cell types in terms of distinctive molecular profiles (e.g., gene expression) and connect this information with classical cellular descriptions (e.g., location and morphology). A comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, as well as provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas.
]]></description>
<dc:creator>Regev, A.</dc:creator>
<dc:creator>Teichmann, S.</dc:creator>
<dc:creator>Lander, E. S.</dc:creator>
<dc:creator>Amit, I.</dc:creator>
<dc:creator>Benoist, C.</dc:creator>
<dc:creator>Birney, E.</dc:creator>
<dc:creator>Bodenmiller, B.</dc:creator>
<dc:creator>Campbell, P.</dc:creator>
<dc:creator>Carninci, P.</dc:creator>
<dc:creator>Clatworthy, M.</dc:creator>
<dc:creator>Clevers, H.</dc:creator>
<dc:creator>Deplancke, B.</dc:creator>
<dc:creator>Dunham, I.</dc:creator>
<dc:creator>Eberwine, J.</dc:creator>
<dc:creator>Eils, R.</dc:creator>
<dc:creator>Enard, W.</dc:creator>
<dc:creator>Farmer, A.</dc:creator>
<dc:creator>Fugger, L.</dc:creator>
<dc:creator>Gottgens, B.</dc:creator>
<dc:creator>Hacohen, N.</dc:creator>
<dc:creator>Haniffa, M.</dc:creator>
<dc:creator>Hemberg, M.</dc:creator>
<dc:creator>Kim, S. K.</dc:creator>
<dc:creator>Klenerman, P.</dc:creator>
<dc:creator>Kriegstein, A.</dc:creator>
<dc:creator>Lein, E.</dc:creator>
<dc:creator>Linnarsson, S.</dc:creator>
<dc:creator>Lundeberg, J.</dc:creator>
<dc:creator>Majumder, P.</dc:creator>
<dc:creator>Marioni, J.</dc:creator>
<dc:creator>Merad, M.</dc:creator>
<dc:creator>Mhlanga, M.</dc:creator>
<dc:creator>Nawijn, M.</dc:creator>
<dc:creator>Netea, M.</dc:creator>
<dc:creator>Nolan, G.</dc:creator>
<dc:creator>Pe'er, D.</dc:creator>
<dc:creator>Philipakis, A.</dc:creator>
<dc:creator>Ponting, C. P.</dc:creator>
<dc:creator>Quake, S. R.</dc:creator>
<dc:creator>Reik, W.</dc:creator>
<dc:creator>Rozenblatt-Rosen, O.</dc:creator>
<dc:creator>Sanes, J. R.</dc:creator>
<dc:creator>Satija, R.</dc:creator>
<dc:creator>Shumacher, T.</dc:creator>
<dc:creator>Shalek, A. K</dc:creator>
<dc:date>2017-05-08</dc:date>
<dc:identifier>doi:10.1101/121202</dc:identifier>
<dc:title><![CDATA[The Human Cell Atlas]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-05-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.04.22.056473v1?rss=1">
<title>
<![CDATA[
scConsensus: combining supervised and unsupervised clustering for cell type identification in single-cell RNA sequencing data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.04.22.056473v1?rss=1"
</link>
<description><![CDATA[
Clustering is a crucial step in the analysis of single-cell data. Clusters identified using unsupervised clustering are typically annotated to cell types based on differentially expressed genes. In contrast, supervised methods use a reference panel of labelled transcriptomes to guide both clustering and cell type identification. Supervised and unsupervised clustering strategies have their distinct advantages and limitations. Therefore, they can lead to different but often complementary clustering results. Hence, a consensus approach leveraging the merits of both clustering paradigms could result in a more accurate clustering and a more precise cell type annotation. We present O_SCPLOWSCC_SCPLOWCO_SCPLOWONSENSUSC_SCPLOW, an R framework for generating a consensus clustering by (i) integrating the results from both unsupervised and supervised approaches and (ii) refining the consensus clusters using differentially expressed (DE) genes. The value of our approach is demonstrated on several existing single-cell RNA sequencing datasets, including data from sorted PBMC sub-populations. O_SCPLOWSCC_SCPLOWCO_SCPLOWONSENSUSC_SCPLOW is freely available on GitHub at https://github.com/prabhakarlab/scConsensus.
]]></description>
<dc:creator>Ranjan, B.</dc:creator>
<dc:creator>Schmidt, F.</dc:creator>
<dc:creator>Sun, W.</dc:creator>
<dc:creator>Park, J.</dc:creator>
<dc:creator>Honardoost, M. A.</dc:creator>
<dc:creator>Tan, J.</dc:creator>
<dc:creator>Arul Rayan, N.</dc:creator>
<dc:creator>Prabhakar, S.</dc:creator>
<dc:date>2020-04-24</dc:date>
<dc:identifier>doi:10.1101/2020.04.22.056473</dc:identifier>
<dc:title><![CDATA[scConsensus: combining supervised and unsupervised clustering for cell type identification in single-cell RNA sequencing data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-04-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.06.30.174391v1?rss=1">
<title>
<![CDATA[
Spatial and single-cell transcriptional landscape of human cerebellar development 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.06.30.174391v1?rss=1"
</link>
<description><![CDATA[
ABSTRACTCerebellar development and function require precise regulation of molecular and cellular programs to coordinate motor functions and integrate network signals required for cognition and emotional regulation. However, molecular understanding of human cerebellar development is limited. Here, we combined spatially resolved and single-cell transcriptomics to systematically map the molecular, cellular, and spatial composition of early and mid-gestational human cerebellum. This enabled us to transcriptionally profile major cell types and examine the dynamics of gene expression within cell types and lineages across development. The resulting ‘Developmental Cell Atlas of the Human Cerebellum’ demonstrates that the molecular organization of the cerebellar anlage reflects cytoarchitecturally distinct regions and developmentally transient cell types that are insufficiently captured in bulk transcriptional profiles. By mapping disease genes onto cell types, we implicate the dysregulation of specific cerebellar cell types, especially Purkinje cells, in pediatric and adult neurological disorders. These data provide a critical resource for understanding human cerebellar development with implications for the cellular basis of cerebellar diseases.Competing Interest StatementA.B.R, C.R., and G.Se. are shareholders of Split Bioscience.View Full Text
]]></description>
<dc:creator>Aldinger, K. A.</dc:creator>
<dc:creator>Thomson, Z.</dc:creator>
<dc:creator>Haldipur, P.</dc:creator>
<dc:creator>Deng, M.</dc:creator>
<dc:creator>Timms, A. E.</dc:creator>
<dc:creator>Hirano, M.</dc:creator>
<dc:creator>Santpere, G.</dc:creator>
<dc:creator>Roco, C.</dc:creator>
<dc:creator>Rosenberg, A. B.</dc:creator>
<dc:creator>Lorente-Galdos, B.</dc:creator>
<dc:creator>Gulden, F. O.</dc:creator>
<dc:creator>O'Day, D.</dc:creator>
<dc:creator>Overman, L. M.</dc:creator>
<dc:creator>Lisgo, S.</dc:creator>
<dc:creator>Alexandre, P.</dc:creator>
<dc:creator>Sestan, N.</dc:creator>
<dc:creator>Doherty, D.</dc:creator>
<dc:creator>Dobyns, W. B.</dc:creator>
<dc:creator>Seelig, G.</dc:creator>
<dc:creator>Glass, I. A.</dc:creator>
<dc:creator>Millen, K. J.</dc:creator>
<dc:date>2020-07-01</dc:date>
<dc:identifier>doi:10.1101/2020.06.30.174391</dc:identifier>
<dc:title><![CDATA[Spatial and single-cell transcriptional landscape of human cerebellar development]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-07-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2019.12.21.884759v1?rss=1">
<title>
<![CDATA[
A single-cell atlas of the human healthy airways 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2019.12.21.884759v1?rss=1"
</link>
<description><![CDATA[
RationaleThe respiratory tract constitutes an elaborated line of defense based on a unique cellular ecosystem. Single-cell profiling methods enable the investigation of cell population distributions and transcriptional changes along the airways.

MethodsWe have explored cellular heterogeneity of the human airway epithelium in 10 healthy living volunteers by single-cell RNA profiling. 77,969 cells were collected by bronchoscopy at 35 distinct locations, from the nose to the 12th division of the airway tree.

ResultsThe resulting atlas is composed of a high percentage of epithelial cells (89.1%), but also immune (6.2%) and stromal (4.7%) cells with peculiar cellular proportions in different sites of the airways. It reveals differential gene expression between identical cell types (suprabasal, secretory, and multiciliated cells) from the nose (MUC4, PI3, SIX3) and tracheobronchial (SCGB1A1, TFF3) airways. By contrast, cell-type specific gene expression was stable across all tracheobronchial samples. Our atlas improves the description of ionocytes, pulmonary neuro-endocrine (PNEC) and brush cells, which are likely derived from a common population of precursor cells. We also report a population of KRT13 positive cells with a high percentage of dividing cells which are reminiscent of "hillock" cells previously described in mouse.

ConclusionsRobust characterization of this unprecedented large single-cell cohort establishes an important resource for future investigations. The precise description of the continuum existing from nasal epithelium to successive divisions of lung airways and the stable gene expression profile of these regions better defines conditions under which relevant tracheobronchial proxies of human respiratory diseases can be developed.
]]></description>
<dc:creator>Deprez, M.</dc:creator>
<dc:creator>Zaragosi, L.-E.</dc:creator>
<dc:creator>Truchi, M.</dc:creator>
<dc:creator>Ruiz Garcia, S.</dc:creator>
<dc:creator>Arguel, M.-J.</dc:creator>
<dc:creator>Lebrigand, K.</dc:creator>
<dc:creator>Paquet, A.</dc:creator>
<dc:creator>Pee'r, D.</dc:creator>
<dc:creator>Marquette, C.-H.</dc:creator>
<dc:creator>Leroy, S.</dc:creator>
<dc:creator>BARBRY, P.</dc:creator>
<dc:date>2019-12-23</dc:date>
<dc:identifier>doi:10.1101/2019.12.21.884759</dc:identifier>
<dc:title><![CDATA[A single-cell atlas of the human healthy airways]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-12-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/831495v1?rss=1">
<title>
<![CDATA[
High throughput, error corrected Nanopore single cell transcriptome sequencing 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/831495v1?rss=1"
</link>
<description><![CDATA[
Droplet-based high throughput single cell isolation techniques tremendously boosted the throughput of single cell transcriptome profiling experiments. However, those approaches only allow analysis of one extremity of the transcript after short read sequencing. We introduce an approach that combines Oxford Nanopore sequencing with unique molecular identifiers to obtain error corrected full length sequence information with the 10xGenomics single cell isolation system. This allows to examine differential RNA splicing and RNA editing at a single cell level.
]]></description>
<dc:creator>Lebrigand, K.</dc:creator>
<dc:creator>Magnone, V.</dc:creator>
<dc:creator>BARBRY, P.</dc:creator>
<dc:creator>Waldmann, R.</dc:creator>
<dc:date>2019-11-05</dc:date>
<dc:identifier>doi:10.1101/831495</dc:identifier>
<dc:title><![CDATA[High throughput, error corrected Nanopore single cell transcriptome sequencing]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-11-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.08.11.245795v1?rss=1">
<title>
<![CDATA[
Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.08.11.245795v1?rss=1"
</link>
<description><![CDATA[
Single-cell RNA sequencing (scRNA-seq) revolutionised our understanding of disease biology and presented the promise of transforming translational research. We developed Besca, a toolkit that streamlines scRNA-seq analyses according to current best practices. A standard workflow covers quality control, filtering, and clustering. Two complementary Besca modules, utilizing hierarchical cell signatures or supervised machine learning, automate cell annotation and provide harmonised nomenclatures across studies. Subsequently, Besca enables estimation of cell type proportions in bulk transcriptomics studies. Using multiple heterogeneous scRNA-seq datasets we show how Besca aids acceleration, interoperability, reusability, and interpretability of scRNA-seq data analysis, crucial aspects in translational research and beyond.
]]></description>
<dc:creator>Maedler, S. C.</dc:creator>
<dc:creator>Julien-Laferriere, A.</dc:creator>
<dc:creator>Wyss, L.</dc:creator>
<dc:creator>Phan, M.</dc:creator>
<dc:creator>Kang, A. S. W.</dc:creator>
<dc:creator>Ulrich, E.</dc:creator>
<dc:creator>Schmucki, R.</dc:creator>
<dc:creator>Zhang, J. D.</dc:creator>
<dc:creator>Ebeling, M.</dc:creator>
<dc:creator>Badi, L.</dc:creator>
<dc:creator>Kam-Thong, T.</dc:creator>
<dc:creator>Schwalie, P. C.</dc:creator>
<dc:creator>Hatje, K.</dc:creator>
<dc:date>2020-08-12</dc:date>
<dc:identifier>doi:10.1101/2020.08.11.245795</dc:identifier>
<dc:title><![CDATA[Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-08-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.08.29.272831v1?rss=1">
<title>
<![CDATA[
Deep learning and alignment of spatially-resolved whole transcriptomes of single cells in the mouse brain with Tangram 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.08.29.272831v1?rss=1"
</link>
<description><![CDATA[
Charting a biological atlas of an organ, such as the brain, requires us to spatially-resolve whole transcriptomes of single cells, and to relate such cellular features to the histological and anatomical scales. Single-cell and single-nucleus RNA-Seq (sc/snRNA-seq) can map cells comprehensively5,6, but relating those to their histological and anatomical positions in the context of an organs common coordinate framework remains a major challenge and barrier to the construction of a cell atlas7-10. Conversely, Spatial Transcriptomics allows for in-situ measurements11-13 at the histological level, but at lower spatial resolution and with limited sensitivity. Targeted in situ technologies1-3 solve both issues, but are limited in gene throughput which impedes profiling of the entire transcriptome. Finally, as samples are collected for profiling, their registration to anatomical atlases often require human supervision, which is a major obstacle to build pipelines at scale. Here, we demonstrate spatial mapping of cells, histology, and anatomy in the somatomotor area and the visual area of the healthy adult mouse brain. We devise Tangram, a method that aligns snRNA-seq data to various forms of spatial data collected from the same brain region, including MERFISH1, STARmap2, smFISH3, and Spatial Transcriptomics4 (Visium), as well as histological images and public atlases. Tangram can map any type of sc/snRNA-seq data, including multi-modal data such as SHARE-seq data5, which we used to reveal spatial patterns of chromatin accessibility. We equipped Tangram with a deep learning computer vision pipeline, which allows for automatic identification of anatomical annotations on histological images of mouse brain. By doing so, Tangram reconstructs a genome-wide, anatomically-integrated, spatial map of the visual and somatomotor area with [~]30,000 genes at single-cell resolution, revealing spatial gene expression and chromatin accessibility patterning beyond current limitation of in-situ technologies.
]]></description>
<dc:creator>Biancalani, T.</dc:creator>
<dc:creator>Scalia, G.</dc:creator>
<dc:creator>Buffoni, L.</dc:creator>
<dc:creator>Avasthi, R.</dc:creator>
<dc:creator>Lu, Z.</dc:creator>
<dc:creator>Sanger, A.</dc:creator>
<dc:creator>Tokcan, N.</dc:creator>
<dc:creator>Vanderburg, C. R.</dc:creator>
<dc:creator>Segerstolpe, A.</dc:creator>
<dc:creator>Zhang, M.</dc:creator>
<dc:creator>Avraham-Davidi, I.</dc:creator>
<dc:creator>Vickovic, S.</dc:creator>
<dc:creator>Nitzan, M.</dc:creator>
<dc:creator>Ma, S.</dc:creator>
<dc:creator>Buenrostro, J. D.</dc:creator>
<dc:creator>Brown, N. B.</dc:creator>
<dc:creator>Fanelli, D.</dc:creator>
<dc:creator>Zhuang, X.</dc:creator>
<dc:creator>Macosko, E.</dc:creator>
<dc:creator>Regev, A.</dc:creator>
<dc:date>2020-08-30</dc:date>
<dc:identifier>doi:10.1101/2020.08.29.272831</dc:identifier>
<dc:title><![CDATA[Deep learning and alignment of spatially-resolved whole transcriptomes of single cells in the mouse brain with Tangram]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-08-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.10.07.330563v1?rss=1">
<title>
<![CDATA[
DUBStepR: correlation-based feature selection for clustering single-cell RNA sequencing data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.10.07.330563v1?rss=1"
</link>
<description><![CDATA[
Feature selection (marker gene selection) is widely believed to improve clustering accuracy, and is thus a key component of single cell clustering pipelines. However, we found that the performance of existing feature selection methods was inconsistent across benchmark datasets, and occasionally even worse than without feature selection. Moreover, existing methods ignored information contained in gene-gene correlations. We therefore developed DUBStepR (Determining the Underlying Basis using Stepwise Regression), a feature selection algorithm that leverages gene-gene correlations with a novel measure of inhomogeneity in feature space, termed the Density Index (DI). Despite selecting a relatively small number of genes, DUBStepR substantially outperformed existing single-cell feature selection methods across diverse clustering benchmarks. In a published scRNA-seq dataset from sorted monocytes, DUBStepR sensitively detected a rare and previously invisible population of contaminating basophils. DUBStepR is scalable to over a million cells, and can be straightforwardly applied to other data types such as single-cell ATAC-seq. We propose DUBStepR as a general-purpose feature selection solution for accurately clustering single-cell data.
]]></description>
<dc:creator>Ranjan, B.</dc:creator>
<dc:creator>Sun, W.</dc:creator>
<dc:creator>Park, J.</dc:creator>
<dc:creator>Xie, R.</dc:creator>
<dc:creator>Alipour, F.</dc:creator>
<dc:creator>Singhal, V.</dc:creator>
<dc:creator>Prabhakar, S.</dc:creator>
<dc:date>2020-10-08</dc:date>
<dc:identifier>doi:10.1101/2020.10.07.330563</dc:identifier>
<dc:title><![CDATA[DUBStepR: correlation-based feature selection for clustering single-cell RNA sequencing data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.11.05.369363v1?rss=1">
<title>
<![CDATA[
Poised cell circuits in human skin are activated in disease 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.11.05.369363v1?rss=1"
</link>
<description><![CDATA[
The human skin confers biophysical and immunological protection through a complex cellular network that is established early in development. We profiled ~500,000 single cells using RNA-sequencing from healthy adult and developing skin, and skin from patients with atopic dermatitis and psoriasis. Our findings reveal a predominance of innate lymphoid cells and macrophages in developing skin in contrast to T cells and migratory dendritic cells in adult skin. We demonstrate dual keratinocyte differentiation trajectories and activated cellular circuits comprising vascular endothelial cells mediating immune cell trafficking, disease-specific clonally expanded IL13/IL22 and IL17A/F-expressing lymphocytes, epidermal IL23-expressing dendritic cells and inflammatory keratinocytes in disease. Our findings provide key insights into the dynamic cellular landscape of human skin in health and disease.

One Sentence SummarySingle cell atlas of human skin reveals cell circuits which are quantitatively and qualitatively reconfigured in inflammatory skin disease.
]]></description>
<dc:creator>Reynolds, G.</dc:creator>
<dc:creator>Vegh, P.</dc:creator>
<dc:creator>Fletcher, J.</dc:creator>
<dc:creator>Poyner, E.</dc:creator>
<dc:creator>Stephenson, E.</dc:creator>
<dc:creator>Goh, I.</dc:creator>
<dc:creator>Botting, R.</dc:creator>
<dc:creator>Huang, N.</dc:creator>
<dc:creator>Olabi, B.</dc:creator>
<dc:creator>Dubois, A.</dc:creator>
<dc:creator>Dixon, D.</dc:creator>
<dc:creator>Green, K.</dc:creator>
<dc:creator>Maunder, D.</dc:creator>
<dc:creator>Engelbert, J.</dc:creator>
<dc:creator>Efremova, M.</dc:creator>
<dc:creator>Polanski, K.</dc:creator>
<dc:creator>Jardine, L.</dc:creator>
<dc:creator>Jones, C.</dc:creator>
<dc:creator>Ness, T.</dc:creator>
<dc:creator>Horsfall, D.</dc:creator>
<dc:creator>McGrath, J.</dc:creator>
<dc:creator>Carey, C.</dc:creator>
<dc:creator>Popescu, D.-M.</dc:creator>
<dc:creator>Webb, S.</dc:creator>
<dc:creator>Wang, X.-n.</dc:creator>
<dc:creator>Sayer, B.</dc:creator>
<dc:creator>Park, J.-E.</dc:creator>
<dc:creator>Negri, V.</dc:creator>
<dc:creator>Belokhvostova, D.</dc:creator>
<dc:creator>Lynch, M.</dc:creator>
<dc:creator>McDonald, D.</dc:creator>
<dc:creator>Filby, A.</dc:creator>
<dc:creator>Hagai, T.</dc:creator>
<dc:creator>Meyer, K.</dc:creator>
<dc:creator>Husain, A.</dc:creator>
<dc:creator>Coxhead, J.</dc:creator>
<dc:creator>Vento-Tormo, R.</dc:creator>
<dc:creator>Behjati, S.</dc:creator>
<dc:creator>Lisgo, S.</dc:creator>
<dc:creator>Villani, A.-C.</dc:creator>
<dc:creator>Bacardit, J.</dc:creator>
<dc:creator>Jones, P.</dc:creator>
<dc:creator>OToole, E.</dc:creator>
<dc:creator>Ogg, G.</dc:creator>
<dc:creator>Rajan, N.</dc:creator>
<dc:creator>Reynolds, N.</dc:creator>
<dc:creator>Teichmann, S.</dc:creator>
<dc:creator>Watt, F.</dc:creator>
<dc:creator></dc:creator>
<dc:date>2020-11-05</dc:date>
<dc:identifier>doi:10.1101/2020.11.05.369363</dc:identifier>
<dc:title><![CDATA[Poised cell circuits in human skin are activated in disease]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-11-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.02.06.937110v1?rss=1">
<title>
<![CDATA[
Single-cell sequencing of developing human gut reveals transcriptional links to childhood Crohns disease 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.02.06.937110v1?rss=1"
</link>
<description><![CDATA[
Human gut development requires the orchestrated interaction of various differentiating cell types. Here we generate an in-depth single-cell map of the developing human intestine at 6-10 weeks post-conception, a period marked by crypt-villus formation. Our analysis reveals the transcriptional profile of cycling epithelial precursor cells, which are distinct from LGR5-expressing cells. We use computational analyses to show that these cells contribute to differentiated cell subsets directly and indirectly via the generation of LGR5-expressing stem cells and receive signals from the surrounding mesenchymal cells. Furthermore, we draw parallels between the transcriptomes of ex vivo tissues and in vitro fetal organoids, revealing the maturation of organoid cultures in a dish. Lastly, we compare scRNAseq profiles from paediatric Crohns disease epithelium alongside matched healthy controls to reveal disease associated changes in epithelial composition. Contrasting these with the fetal profiles reveals re-activation of fetal transcription factors in Crohns disease epithelium. Our study provides a unique resource, available at www.gutcellatlas.org, and underscores the importance of unravelling fetal development in understanding disease.
]]></description>
<dc:creator>Elmentaite, R.</dc:creator>
<dc:creator>Ross, A.</dc:creator>
<dc:creator>James, K. R.</dc:creator>
<dc:creator>Ortmann, D.</dc:creator>
<dc:creator>Gomes, T.</dc:creator>
<dc:creator>Roberts, K.</dc:creator>
<dc:creator>Nayak, K.</dc:creator>
<dc:creator>Tuck, L.</dc:creator>
<dc:creator>Bayraktar, O.</dc:creator>
<dc:creator>Heuschkel, R.</dc:creator>
<dc:creator>Vallier, L.</dc:creator>
<dc:creator>Teichmann, S. A.</dc:creator>
<dc:creator>Zilbauer, M.</dc:creator>
<dc:date>2020-02-07</dc:date>
<dc:identifier>doi:10.1101/2020.02.06.937110</dc:identifier>
<dc:title><![CDATA[Single-cell sequencing of developing human gut reveals transcriptional links to childhood Crohns disease]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-02-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.02.16.431527v1?rss=1">
<title>
<![CDATA[
Robust clustering and interpretation ofscRNA-seq data using reference componentanalysis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.02.16.431527v1?rss=1"
</link>
<description><![CDATA[
MotivationThe transcriptomic diversity of the hundreds of cell types in the human body can be analysed in unprecedented detail using single cell (SC) technologies. Though clustering of cellular transcriptomes is the default technique for defining cell types and subtypes, single cell clustering can be strongly influenced by technical variation. In fact, the prevalent unsupervised clustering algorithms can cluster cells by technical, rather than biological, variation.

ResultsCompared to de novo (unsupervised) clustering methods, we demonstrate using multiple benchmarks that supervised clustering, which uses reference transcriptomes as a guide, is robust to batch effects. To leverage the advantages of supervised clustering, we present RCA2, a new, scalable, and broadly applicable version of our RCA algorithm. RCA2 provides a user-friendly framework for supervised clustering and downstream analysis of large scRNA-seq data sets. RCA2 can be seamlessly incorporated into existing algorithmic pipelines. It incorporates various new reference panels for human and mouse, supports generation of custom panels and uses efficient graph-based clustering and sparse data structures to ensure scalability. We demonstrate the applicability of RCA2 on SC data from human bone marrow, healthy PBMCs and PBMCs from COVID-19 patients. Importantly, RCA2 facilitates cell-type-specific QC, which we show is essential for accurate clustering of SC data from heterogeneous tissues. In the era of cohort-scale SC analysis, supervised clustering methods such as RCA2 will facilitate unified analysis of diverse SC datasets.

AvailabilityRCA2 is implemented in R and is available at github.com/prabhakarlab/RCAv2
]]></description>
<dc:creator>Schmidt, F.</dc:creator>
<dc:creator>Ranjan, B.</dc:creator>
<dc:creator>Xiao Xuan Lin, Q.</dc:creator>
<dc:creator>Krishnan, V.</dc:creator>
<dc:creator>Joanito, I.</dc:creator>
<dc:creator>Honardoost, M. A.</dc:creator>
<dc:creator>Nawaz, Z.</dc:creator>
<dc:creator>Venkatesh, P. N.</dc:creator>
<dc:creator>Tan, J.</dc:creator>
<dc:creator>Rayan, N. A.</dc:creator>
<dc:creator>Ong, S. T.</dc:creator>
<dc:creator>Prabhakar, S.</dc:creator>
<dc:date>2021-02-17</dc:date>
<dc:identifier>doi:10.1101/2021.02.16.431527</dc:identifier>
<dc:title><![CDATA[Robust clustering and interpretation ofscRNA-seq data using reference componentanalysis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-02-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.03.08.434433v1?rss=1">
<title>
<![CDATA[
Systemic Tissue and Cellular Disruption from SARS-CoV-2 Infection revealed in COVID-19 Autopsies and Spatial Omics Tissue Maps 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.03.08.434433v1?rss=1"
</link>
<description><![CDATA[
The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus has infected over 115 million people and caused over 2.5 million deaths worldwide. Yet, the molecular mechanisms underlying the clinical manifestations of COVID-19, as well as what distinguishes them from common seasonal influenza virus and other lung injury states such as Acute Respiratory Distress Syndrome (ARDS), remains poorly understood. To address these challenges, we combined transcriptional profiling of 646 clinical nasopharyngeal swabs and 39 patient autopsy tissues, matched with spatial protein and expression profiling (GeoMx) across 357 tissue sections. These results define both body-wide and tissue-specific (heart, liver, lung, kidney, and lymph nodes) damage wrought by the SARS-CoV-2 infection, evident as a function of varying viral load (high vs. low) during the course of infection and specific, transcriptional dysregulation in splicing isoforms, T cell receptor expression, and cellular expression states. In particular, cardiac and lung tissues revealed the largest degree of splicing isoform switching and cell expression state loss. Overall, these findings reveal a systemic disruption of cellular and transcriptional pathways from COVID-19 across all tissues, which can inform subsequent studies to combat the mortality of COVID-19, as well to better understand the molecular dynamics of lethal SARS-CoV-2 infection and other viruses.
]]></description>
<dc:creator>Park, J.</dc:creator>
<dc:creator>Foox, J.</dc:creator>
<dc:creator>Hether, T.</dc:creator>
<dc:creator>Danko, D.</dc:creator>
<dc:creator>Warren, S.</dc:creator>
<dc:creator>Kim, Y.</dc:creator>
<dc:creator>Reeves, J.</dc:creator>
<dc:creator>Butler, D. J.</dc:creator>
<dc:creator>Mozsary, C.</dc:creator>
<dc:creator>Rosiene, J.</dc:creator>
<dc:creator>Shaiber, A.</dc:creator>
<dc:creator>Afshinnekoo, E.</dc:creator>
<dc:creator>MacKay, M.</dc:creator>
<dc:creator>Bram, Y.</dc:creator>
<dc:creator>Chandar, V.</dc:creator>
<dc:creator>Geiger, H.</dc:creator>
<dc:creator>Craney, A.</dc:creator>
<dc:creator>Velu, P.</dc:creator>
<dc:creator>Melnick, A. M.</dc:creator>
<dc:creator>Hajirasouliha, I.</dc:creator>
<dc:creator>Beheshti, A.</dc:creator>
<dc:creator>Taylor, D.</dc:creator>
<dc:creator>Saravia-Butler, A.</dc:creator>
<dc:creator>Singh, U.</dc:creator>
<dc:creator>Wurtele, E. S.</dc:creator>
<dc:creator>Schisler, J.</dc:creator>
<dc:creator>Fenessey, S.</dc:creator>
<dc:creator>Corvelo, A.</dc:creator>
<dc:creator>Zody, M. C.</dc:creator>
<dc:creator>Germer, S.</dc:creator>
<dc:creator>Salvatore, S.</dc:creator>
<dc:creator>Levy, S.</dc:creator>
<dc:creator>Wu, S.</dc:creator>
<dc:creator>Tatonetti, N.</dc:creator>
<dc:creator>Shapira, S.</dc:creator>
<dc:creator>Salvatore, M.</dc:creator>
<dc:creator>Loda, M.</dc:creator>
<dc:creator>Westblade, L. F.</dc:creator>
<dc:creator>Cushing, M.</dc:creator>
<dc:creator>Rennert, H.</dc:creator>
<dc:creator>Kriegel, A. J.</dc:creator>
<dc:creator>Elemento, O.</dc:creator>
<dc:creator>Imielinski, M.</dc:creator>
<dc:creator>Borczuk, A. C.</dc:creator>
<dc:creator>Meydan, C.</dc:creator>
<dc:creator>Schwar</dc:creator>
<dc:date>2021-03-09</dc:date>
<dc:identifier>doi:10.1101/2021.03.08.434433</dc:identifier>
<dc:title><![CDATA[Systemic Tissue and Cellular Disruption from SARS-CoV-2 Infection revealed in COVID-19 Autopsies and Spatial Omics Tissue Maps]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-03-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.02.25.430130v1?rss=1">
<title>
<![CDATA[
A single-cell and spatial atlas of autopsy tissues reveals pathology and cellular targets of SARS-CoV-2 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.02.25.430130v1?rss=1"
</link>
<description><![CDATA[
The SARS-CoV-2 pandemic has caused over 1 million deaths globally, mostly due to acute lung injury and acute respiratory distress syndrome, or direct complications resulting in multiple-organ failures. Little is known about the host tissue immune and cellular responses associated with COVID-19 infection, symptoms, and lethality. To address this, we collected tissues from 11 organs during the clinical autopsy of 17 individuals who succumbed to COVID-19, resulting in a tissue bank of approximately 420 specimens. We generated comprehensive cellular maps capturing COVID-19 biology related to patients demise through single-cell and single-nucleus RNA-Seq of lung, kidney, liver and heart tissues, and further contextualized our findings through spatial RNA profiling of distinct lung regions. We developed a computational framework that incorporates removal of ambient RNA and automated cell type annotation to facilitate comparison with other healthy and diseased tissue atlases. In the lung, we uncovered significantly altered transcriptional programs within the epithelial, immune, and stromal compartments and cell intrinsic changes in multiple cell types relative to lung tissue from healthy controls. We observed evidence of: alveolar type 2 (AT2) differentiation replacing depleted alveolar type 1 (AT1) lung epithelial cells, as previously seen in fibrosis; a concomitant increase in myofibroblasts reflective of defective tissue repair; and, putative TP63+ intrapulmonary basal-like progenitor (IPBLP) cells, similar to cells identified in H1N1 influenza, that may serve as an emergency cellular reserve for severely damaged alveoli. Together, these findings suggest the activation and failure of multiple avenues for regeneration of the epithelium in these terminal lungs. SARS-CoV-2 RNA reads were enriched in lung mononuclear phagocytic cells and endothelial cells, and these cells expressed distinct host response transcriptional programs. We corroborated the compositional and transcriptional changes in lung tissue through spatial analysis of RNA profiles in situ and distinguished unique tissue host responses between regions with and without viral RNA, and in COVID-19 donor tissues relative to healthy lung. Finally, we analyzed genetic regions implicated in COVID-19 GWAS with transcriptomic data to implicate specific cell types and genes associated with disease severity. Overall, our COVID-19 cell atlas is a foundational dataset to better understand the biological impact of SARS-CoV-2 infection across the human body and empowers the identification of new therapeutic interventions and prevention strategies.
]]></description>
<dc:creator>Delorey, T. M.</dc:creator>
<dc:creator>Ziegler, C. G. K.</dc:creator>
<dc:creator>Heimberg, G.</dc:creator>
<dc:creator>Normand, R.</dc:creator>
<dc:creator>Yang, Y.</dc:creator>
<dc:creator>Segerstolpe, A.</dc:creator>
<dc:creator>Abbondanza, D.</dc:creator>
<dc:creator>Fleming, S. J.</dc:creator>
<dc:creator>Subramanian, A.</dc:creator>
<dc:creator>Montoro, D. T.</dc:creator>
<dc:creator>Jagadeesh, K. A.</dc:creator>
<dc:creator>Dey, K.</dc:creator>
<dc:creator>Sen, P.</dc:creator>
<dc:creator>Slyper, M.</dc:creator>
<dc:creator>Pita-Juarez, Y.</dc:creator>
<dc:creator>Phillips, D.</dc:creator>
<dc:creator>Bloom-Ackermann, Z.</dc:creator>
<dc:creator>Barkas, N.</dc:creator>
<dc:creator>Ganna, A.</dc:creator>
<dc:creator>Gomez, J.</dc:creator>
<dc:creator>Normandin, E.</dc:creator>
<dc:creator>Naderi, P.</dc:creator>
<dc:creator>Popov, Y. V.</dc:creator>
<dc:creator>Raju, S. S.</dc:creator>
<dc:creator>Niezen, S.</dc:creator>
<dc:creator>Tsai, L. T.- Y.</dc:creator>
<dc:creator>Siddle, K. J.</dc:creator>
<dc:creator>Sud, M.</dc:creator>
<dc:creator>Tran, V. M.</dc:creator>
<dc:creator>Karuthedath Vellarikkal, S.</dc:creator>
<dc:creator>Amir-Zilberstein, L.</dc:creator>
<dc:creator>Atri, D. S.</dc:creator>
<dc:creator>Beechem, J. M.</dc:creator>
<dc:creator>Brook, O. R.</dc:creator>
<dc:creator>Chen, J.</dc:creator>
<dc:creator>Divakar, P.</dc:creator>
<dc:creator>Dorceus, P.</dc:creator>
<dc:creator>Engreitz, J. M.</dc:creator>
<dc:creator>Essene, A.</dc:creator>
<dc:creator>Fitzgerald, D. M.</dc:creator>
<dc:creator>Fropf, R.</dc:creator>
<dc:creator>Gaz</dc:creator>
<dc:date>2021-02-25</dc:date>
<dc:identifier>doi:10.1101/2021.02.25.430130</dc:identifier>
<dc:title><![CDATA[A single-cell and spatial atlas of autopsy tissues reveals pathology and cellular targets of SARS-CoV-2]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-02-25</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.04.06.438702v1?rss=1">
<title>
<![CDATA[
Single-cell atlas of human oral mucosa reveals a stromal-neutrophil axis regulating tissue immunity in health and inflammatory disease 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.04.06.438702v1?rss=1"
</link>
<description><![CDATA[
The oral mucosa remains an understudied barrier tissue rich in exposure to antigens, commensals and pathogens. Moreover, it is the tissue where one of the most prevalent human microbe-triggered inflammatory diseases, periodontitis, occurs. To understand this complex environment at the cellular level, we assemble herein a human single-cell transcriptome atlas of oral mucosal tissues in health and periodontitis. Our work reveals transcriptional diversity of stromal and immune cell populations, predicts intercellular communication and uncovers an altered immune responsiveness of stromal cells participating in tissue homeostasis and disease at the gingival mucosa. In health, we define unique populations of CXCL1,2,8-expressing epithelial cells and fibroblasts mediating immune homeostasis primarily through the recruitment of neutrophils. In disease, we further observe stromal, particularly fibroblast hyper-responsiveness linked to recruitment of leukocytes and neutrophil populations. Ultimately, a stromal-neutrophil axis emerges as a key regulator of mucosal immunity. Pursuant to these findings, most Mendelian forms of periodontitis were shown to be linked to genetic mutations in neutrophil and select fibroblast-expressed genes. Moreover, we document previously unappreciated expression of known pattern- and damage-recognition receptors on stromal cell populations in the setting of periodontitis, suggesting avenues for triggering stromal responses. This comprehensive atlas offers an important reference for in-depth understanding of oral mucosal homeostasis and inflammation and reveals unique stromal-immune interactions implicated in tissue immunity.
]]></description>
<dc:creator>Williams, D. W.</dc:creator>
<dc:creator>Greenwell-Wild, T.</dc:creator>
<dc:creator>Brenchley, L.</dc:creator>
<dc:creator>Dutzan, N.</dc:creator>
<dc:creator>Overmiller, A.</dc:creator>
<dc:creator>Sawaya, A. P.</dc:creator>
<dc:creator>Webb, S.</dc:creator>
<dc:creator>Martin, D.</dc:creator>
<dc:creator>Hajishengallis, G.</dc:creator>
<dc:creator>Divaris, K.</dc:creator>
<dc:creator>Morasso, M.</dc:creator>
<dc:creator>Haniffa, M.</dc:creator>
<dc:creator>Moutsopoulos, N. M.</dc:creator>
<dc:date>2021-04-07</dc:date>
<dc:identifier>doi:10.1101/2021.04.06.438702</dc:identifier>
<dc:title><![CDATA[Single-cell atlas of human oral mucosa reveals a stromal-neutrophil axis regulating tissue immunity in health and inflammatory disease]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-04-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.04.07.438755v1?rss=1">
<title>
<![CDATA[
Cells of the human intestinal tract mapped across space and time 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.04.07.438755v1?rss=1"
</link>
<description><![CDATA[
The cellular landscape of the human intestinal tract is dynamic throughout life, developing in utero and changing in response to functional requirements and environmental exposures. To comprehensively map cell lineages in the healthy developing, pediatric and adult human gut from ten distinct anatomical regions, as well as draining lymph nodes, we used singlecell RNA-seq and VDJ analysis of roughly one third of a million cells. This reveals the presence of BEST4+ absorptive cells throughout the human intestinal tract, demonstrating the existence of this cell type beyond the colon for the first time. Furthermore, we implicate IgG sensing as a novel function of intestinal tuft cells, and link these cells to the pathogenesis of inflammatory bowel disease. We define novel glial and neuronal cell populations in the developing enteric nervous system, and predict cell-type specific expression of Hirschsprungs disease-associated genes. Finally, using a systems approach, we identify key cell players across multiple cell lineages driving secondary lymphoid tissue formation in early human development. We show that these programs are adopted in inflammatory bowel disease to recruit and retain immune cells at the site of inflammation. These data provide an unprecedented catalogue of intestinal cells, and new insights into cellular programs in development, homeostasis and disease.
]]></description>
<dc:creator>Elmentaite, R.</dc:creator>
<dc:creator>Kumasaka, N.</dc:creator>
<dc:creator>King, H. W.</dc:creator>
<dc:creator>Roberts, K.</dc:creator>
<dc:creator>Dabrowska, M.</dc:creator>
<dc:creator>Pritchard, S.</dc:creator>
<dc:creator>Bolt, L.</dc:creator>
<dc:creator>Vieira, S. F.</dc:creator>
<dc:creator>Mamanova, L.</dc:creator>
<dc:creator>Huang, N.</dc:creator>
<dc:creator>Goh Kai'En, I.</dc:creator>
<dc:creator>Stephenson, E.</dc:creator>
<dc:creator>Engelbert, J.</dc:creator>
<dc:creator>Botting, R. A.</dc:creator>
<dc:creator>Fleming, A.</dc:creator>
<dc:creator>Dann, E.</dc:creator>
<dc:creator>Lisgo, S. N.</dc:creator>
<dc:creator>Katan, M.</dc:creator>
<dc:creator>Leonard, S.</dc:creator>
<dc:creator>Oliver, T. R.</dc:creator>
<dc:creator>Hook, L.</dc:creator>
<dc:creator>Nayak, K.</dc:creator>
<dc:creator>Perrone, F.</dc:creator>
<dc:creator>Campos, L. S.</dc:creator>
<dc:creator>Dominguez Conde, C.</dc:creator>
<dc:creator>Polanski, K.</dc:creator>
<dc:creator>van Dongen, S.</dc:creator>
<dc:creator>Patel, M.</dc:creator>
<dc:creator>Morgan, M.</dc:creator>
<dc:creator>Marioni, J.</dc:creator>
<dc:creator>Bayraktar, O.</dc:creator>
<dc:creator>Meyer, K. B.</dc:creator>
<dc:creator>Zilbauer, M.</dc:creator>
<dc:creator>Uhlig, H.</dc:creator>
<dc:creator>Clatworthy, M. R.</dc:creator>
<dc:creator>Mahbubani, K. T.</dc:creator>
<dc:creator>Saeb-Parsy, K.</dc:creator>
<dc:creator>Haniffa, M. A.</dc:creator>
<dc:creator>James, K. R.</dc:creator>
<dc:creator>Teichmann, S. A.</dc:creator>
<dc:date>2021-04-07</dc:date>
<dc:identifier>doi:10.1101/2021.04.07.438755</dc:identifier>
<dc:title><![CDATA[Cells of the human intestinal tract mapped across space and time]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-04-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.04.04.438388v1?rss=1">
<title>
<![CDATA[
Profiling of transcribed cis-regulatory elements in single cells 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.04.04.438388v1?rss=1"
</link>
<description><![CDATA[
Profiling of cis-regulatory elements (CREs, mostly promoters and enhancers) in single cells allows the interrogation of the cell-type and cell-state-specific contexts of gene regulation and genetic predisposition to diseases. Here we demonstrate single-cell RNA-5'end-sequencing (sc-end5-seq) methods can detect transcribed CREs (tCREs), enabling simultaneous quantification of gene expression and enhancer activities in a single assay at no extra cost. We showed enhancer RNAs can be detected using sc-end5-seq methods with either random or oligo(dT) priming. To analyze tCREs in single cells, we developed SCAFE (Single Cell Analysis of Five-prime Ends) to identify genuine tCREs and analyze their activities (https://github.com/chung-lab/scafe). As compared to accessible CRE (aCRE, based on chromatin accessibility), tCREs are more accurate in predicting CRE interactions by co-activity, more sensitive in detecting shifts in alternative promoter usage and more enriched in diseases heritability. Our results highlight additional dimensions within sc-end5-seq data which can be used for interrogating gene regulation and disease heritability.
]]></description>
<dc:creator>Moody, J.</dc:creator>
<dc:creator>Kouno, T.</dc:creator>
<dc:creator>Suzuki, A.</dc:creator>
<dc:creator>Shibayama, Y.</dc:creator>
<dc:creator>Terao, C.</dc:creator>
<dc:creator>Chang, J.-C.</dc:creator>
<dc:creator>Lopez-Redondo, F.</dc:creator>
<dc:creator>Yip, C. W.</dc:creator>
<dc:creator>Ando, Y.</dc:creator>
<dc:creator>Yamamoto, K.</dc:creator>
<dc:creator>Carninci, P.</dc:creator>
<dc:creator>Shin, J. W.</dc:creator>
<dc:creator>Hon, C.-C.</dc:creator>
<dc:date>2021-04-04</dc:date>
<dc:identifier>doi:10.1101/2021.04.04.438388</dc:identifier>
<dc:title><![CDATA[Profiling of transcribed cis-regulatory elements in single cells]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-04-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.07.09.195438v1?rss=1">
<title>
<![CDATA[
A single cell atlas of human cornea that defines its development, limbal stem and progenitor cells and the interactions with the limbal niche 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.07.09.195438v1?rss=1"
</link>
<description><![CDATA[
To study the development and composition of human ocular surface, we performed single cell (sc) RNA-Seq at key embryonic, fetal and adult stages and generated the first atlas of the corneal cell types from development to adulthood. Our data indicate that during development, the conjunctival epithelium is the first to be specified from the ocular surface epithelium, followed by the corneal epithelium and the establishment of proliferative epithelial progenitors, which predate the formation of limbal niche by a few weeks. Bioinformatic comparison of adult cell clusters identified GPHA2, a novel cell-surface marker for quiescent limbal stem cells (qLSCs), whose function is to maintain qLSCs self-renewal. Combining scRNA- and ATAC-Seq analysis, we identified multiple upstream regulators for qLSCs and transit amplifying (TA) cells and demonstrated a close interaction between the immune cells and epithelial stem and progenitor cells in the cornea. RNA-Seq analysis indicated loss of qLSCs and acquisition of proliferative limbal basal epithelial progenitor markers during ex vivo limbal epithelial cell expansion, independently of the culture method used. Extending the single cell analyses to keratoconus, we were able to reveal activation of collagenase in the corneal stroma and a reduced pool of TA cells in the limbal epithelium as two key changes underlying the disease phenotype. Our scRNA- and ATAC-Seq data of developing and adult cornea in steady state and disease conditions provide a unique resource for defining pathways/genes that can lead to improvement in ex vivo expansion and differentiation methods for cell based replacement therapies and better understanding and treatment of ocular surface disorders.

Key findingsO_LIscRNA-Seq of adult human cornea and conjunctiva reveals the signature of various ocular surface cell populations
C_LIO_LIscRNA-Seq of human developing cornea identifies stage-specific definitions of corneal epithelial, stromal and endothelial layers
C_LIO_LIscRNA-Seq analysis results in identification of novel markers for qLSCs and TA cells
C_LIO_LICombined scRNA- and ATAC-Seq analysis reveals key transcriptional networks in qLSCs and TA cells and close interactions with immune cells
C_LIO_LIExpansion of limbal epithelium results in downregulation of qLSCs and acquisition of proliferative limbal epithelial progenitor markers
C_LIO_LIscRNA-Seq of keratoconus corneas reveals activation of collagenase in the corneal stroma and a reduced pool of TA cells in the limbal epithelium
C_LI

Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY Schematic presentation of main techniques and findings presented in this manuscript.

C_FIG_DISPLAY
]]></description>
<dc:creator>Joseph Collin</dc:creator>
<dc:creator>Rachel Queen</dc:creator>
<dc:creator>Darin Zerti</dc:creator>
<dc:creator>Sanja Bojic</dc:creator>
<dc:creator>Nicky Moyse</dc:creator>
<dc:creator>Marina Moya-Molina</dc:creator>
<dc:creator>Chunbo Yang</dc:creator>
<dc:creator>Gary Reynolds</dc:creator>
<dc:creator>Rafiqul Hussain</dc:creator>
<dc:creator>Jonathan M Coxhead</dc:creator>
<dc:creator>Steven Lisgo</dc:creator>
<dc:creator>Deborah Henderson</dc:creator>
<dc:creator>Agatha Joseph</dc:creator>
<dc:creator>Paul Rooney</dc:creator>
<dc:creator>Saurabh Ghosh</dc:creator>
<dc:creator>Che Connon</dc:creator>
<dc:creator>Muzlifah Haniffa</dc:creator>
<dc:creator>Francisco Figueiredo</dc:creator>
<dc:creator>Lyle Armstrong</dc:creator>
<dc:creator>Majlinda Lako</dc:creator>
<dc:date>2020-07-10</dc:date>
<dc:identifier>doi:10.1101/2020.07.09.195438</dc:identifier>
<dc:title><![CDATA[A single cell atlas of human cornea that defines its development, limbal stem and progenitor cells and the interactions with the limbal niche]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-07-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.10.21.347914v1?rss=1">
<title>
<![CDATA[
Integrated Single Cell Atlas of Endothelial Cells of the Human Lung 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.10.21.347914v1?rss=1"
</link>
<description><![CDATA[
BackgroundDespite its importance in health and disease, the cellular diversity of the lung endothelium has not been systematically characterized in humans. Here we provide a reference atlas of human lung endothelial cells (ECs), to facilitate a better understanding of the phenotypic diversity and composition of cells comprising the lung endothelium, both in health and disease.

MethodsWe reprocessed control single cell RNA sequencing (scRNAseq) data from five datasets of whole lungs that were used for the analysis of pan-endothelial markers, we later included a sixth dataset of sorted control EC for the vascular subpopulation analysis. EC populations were characterized through iterative clustering with subsequent differential expression analysis. Marker genes were validated by immunohistochemistry and in situ hybridization. Signaling network between different lung cell types was studied using connectomic analysis. For cross species analysis we applied the same methods to scRNAseq data obtained from mouse lungs.

ResultsThe six lung scRNAseq datasets were reanalyzed and annotated to identify over 15,000 vascular EC cells from 73 individuals. Differential expression analysis of EC revealed signatures corresponding to endothelial lineage, including pan-endothelial, pan-vascular and subpopulation-specific marker gene sets. Beyond the broad cellular categories of lymphatic, capillary, arterial and venous ECs we found previously indistinguishable subpopulations; among venous EC we identified two previously indistinguishable populations, pulmonary-venous ECs (COL15A1neg) localized to the lung parenchyma and systemic-venous ECs (COL15A1pos) localized to the airways and the visceral pleura; among capillary EC we confirmed their subclassification into recently discovered aerocytes characterized by EDNRB, SOSTDC1 and TBX2 and general capillary EC. We confirmed that all six endothelial cell types, including the systemic-venous EC and aerocytes are present in mice and identified endothelial marker genes conserved in humans and mice. Ligand-Receptor connectome analysis revealed important homeostatic crosstalk of EC with other lung resident cell types. Our manuscript is accompanied by an online data mining tool (www.LungEndothelialCellAtlas.com).

ConclusionOur integrated analysis provides the comprehensive and well-crafted reference atlas of lung endothelial cells in the normal lung and confirms and describes in detail previously unrecognized endothelial populations across a large number of humans and mice.
]]></description>
<dc:creator>Schupp, J. C.</dc:creator>
<dc:creator>Adams, T. S.</dc:creator>
<dc:creator>Cosme, C.</dc:creator>
<dc:creator>Brickman Raredon, M. S.</dc:creator>
<dc:creator>Omote, N.</dc:creator>
<dc:creator>Poli, S.</dc:creator>
<dc:creator>Rose, K.-A.</dc:creator>
<dc:creator>Manning, E.</dc:creator>
<dc:creator>Sauler, M.</dc:creator>
<dc:creator>DeIuliis, G.</dc:creator>
<dc:creator>Ahangari, F.</dc:creator>
<dc:creator>Neumark, N.</dc:creator>
<dc:creator>Yuan, Y.</dc:creator>
<dc:creator>Habermann, A. C.</dc:creator>
<dc:creator>Gutierrez, A. J.</dc:creator>
<dc:creator>Bui, L. T.</dc:creator>
<dc:creator>Meyer, K. B.</dc:creator>
<dc:creator>Nawijn, M. C.</dc:creator>
<dc:creator>Teichmann, S. A.</dc:creator>
<dc:creator>Banovich, N.</dc:creator>
<dc:creator>Kropski, J. A.</dc:creator>
<dc:creator>Niklason, L. E.</dc:creator>
<dc:creator>Pe'er, D.</dc:creator>
<dc:creator>Yan, X.</dc:creator>
<dc:creator>Homer, R.</dc:creator>
<dc:creator>Rosas, I. O.</dc:creator>
<dc:creator>Kaminski, N.</dc:creator>
<dc:date>2020-10-22</dc:date>
<dc:identifier>doi:10.1101/2020.10.21.347914</dc:identifier>
<dc:title><![CDATA[Integrated Single Cell Atlas of Endothelial Cells of the Human Lung]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-10-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.01.02.425073v1?rss=1">
<title>
<![CDATA[
Mapping the temporal and spatial dynamics of the human endometrium in vivo and in vitro 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.01.02.425073v1?rss=1"
</link>
<description><![CDATA[
The endometrium, the mucosal lining of the uterus, undergoes dynamic changes throughout the menstrual cycle in response to ovarian hormones. We have generated single-cell and spatial reference maps of the human uterus and 3D endometrial organoid cultures. We dissect the signalling pathways that determine cell fate of the epithelial lineages in the lumenal and glandular microenvironments. Our benchmark of the endometrial organoids highlights common pathways regulating the differentiation of secretory and ciliated lineage in vivo and in vitro. We show in vitro that downregulation of WNT or NOTCH pathways increases the differentiation efficiency along the secretory and ciliated lineages, respectively. These mechanistic insights provide a platform for future development of treatments for a range of common endometrial disorders including endometriosis and carcinoma.
]]></description>
<dc:creator>Garcia-Alonso, L.</dc:creator>
<dc:creator>Handfield, L.-F.</dc:creator>
<dc:creator>Roberts, K.</dc:creator>
<dc:creator>Nikolakopoulou, K.</dc:creator>
<dc:creator>Fernando, R. C.</dc:creator>
<dc:creator>Gardner, L.</dc:creator>
<dc:creator>Woodhams, B.</dc:creator>
<dc:creator>Arutyunyan, A.</dc:creator>
<dc:creator>Polanski, K.</dc:creator>
<dc:creator>Hoo, R.</dc:creator>
<dc:creator>Sancho-Serra, C.</dc:creator>
<dc:creator>Li, T.</dc:creator>
<dc:creator>Kwakwa, K.</dc:creator>
<dc:creator>Tuck, E.</dc:creator>
<dc:creator>Kleshchevnikov, V.</dc:creator>
<dc:creator>Tarkowska, A.</dc:creator>
<dc:creator>Porter, T.</dc:creator>
<dc:creator>Mazzeo, C. I.</dc:creator>
<dc:creator>van Dongen, S.</dc:creator>
<dc:creator>Dabrowska, M.</dc:creator>
<dc:creator>Vaskivskyi, V.</dc:creator>
<dc:creator>Mahbubani, K. T.</dc:creator>
<dc:creator>PARK, J.-E.</dc:creator>
<dc:creator>Jimenez-Linan, M.</dc:creator>
<dc:creator>Campos, L.</dc:creator>
<dc:creator>Kiselev, V.</dc:creator>
<dc:creator>Lindskog, C.</dc:creator>
<dc:creator>Ayuk, P.</dc:creator>
<dc:creator>Prigmore, E.</dc:creator>
<dc:creator>Stratton, M. R.</dc:creator>
<dc:creator>Saeb-Parsy, K.</dc:creator>
<dc:creator>Moffett, A.</dc:creator>
<dc:creator>Moore, L.</dc:creator>
<dc:creator>Bayraktar, O. A.</dc:creator>
<dc:creator>Teichmann, S. A.</dc:creator>
<dc:creator>Turco, M. Y.</dc:creator>
<dc:creator>Vento-Tormo, R.</dc:creator>
<dc:date>2021-01-04</dc:date>
<dc:identifier>doi:10.1101/2021.01.02.425073</dc:identifier>
<dc:title><![CDATA[Mapping the temporal and spatial dynamics of the human endometrium in vivo and in vitro]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-01-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.06.25.449771v1?rss=1">
<title>
<![CDATA[
Intrinsic and extrinsic regulation of human fetal bone marrow haematopoiesis and perturbations in Down syndrome 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.06.25.449771v1?rss=1"
</link>
<description><![CDATA[
Throughout postnatal life, haematopoiesis in the bone marrow (BM) maintains blood and immune cell production. Haematopoiesis first emerges in human BM at 12 post conception weeks while fetal liver (FL) haematopoiesis is still expanding. Yet, almost nothing is known about how fetal BM evolves to meet the highly specialised needs of the fetus and newborn infant. Here, we detail the development of fetal BM including stroma using single cell RNA-sequencing. We find that the full blood and immune cell repertoire is established in fetal BM in a short time window of 6-7 weeks early in the second trimester. Fetal BM promotes rapid and extensive diversification of myeloid cells, with granulocytes, eosinophils and dendritic cell (DC) subsets emerging for the first time. B-lymphocyte expansion occurs, in contrast with erythroid predominance in FL at the same gestational age. We identify transcriptional and functional differences that underlie tissue-specific identity and cellular diversification in fetal BM and FL. Finally, we reveal selective disruption of B-lymphocyte, erythroid and myeloid development due to cell intrinsic differentiation bias as well as extrinsic regulation through an altered microenvironment in the fetal BM from constitutional chromosome anomaly Down syndrome during this crucial developmental time window.
]]></description>
<dc:creator>Jardine, L.</dc:creator>
<dc:creator>Webb, S.</dc:creator>
<dc:creator>Goh, I.</dc:creator>
<dc:creator>Quiroga Londono, M.</dc:creator>
<dc:creator>Reynolds, G.</dc:creator>
<dc:creator>Mather, M.</dc:creator>
<dc:creator>Olabi, B.</dc:creator>
<dc:creator>Stephenson, E.</dc:creator>
<dc:creator>Botting, R. A.</dc:creator>
<dc:creator>Horsfall, D.</dc:creator>
<dc:creator>Engelbert, J.</dc:creator>
<dc:creator>Maunder, D.</dc:creator>
<dc:creator>Mende, N.</dc:creator>
<dc:creator>Murnane, C.</dc:creator>
<dc:creator>Dann, E.</dc:creator>
<dc:creator>McGrath, J.</dc:creator>
<dc:creator>King, H.</dc:creator>
<dc:creator>Kucinski, I.</dc:creator>
<dc:creator>Queen, R.</dc:creator>
<dc:creator>Carey, C. D.</dc:creator>
<dc:creator>Shrubsole, C.</dc:creator>
<dc:creator>Poyner, E.</dc:creator>
<dc:creator>Acres, M.</dc:creator>
<dc:creator>Jones, C.</dc:creator>
<dc:creator>Ness, T.</dc:creator>
<dc:creator>Coultard, R.</dc:creator>
<dc:creator>Elliot, N.</dc:creator>
<dc:creator>O'Byrne, S.</dc:creator>
<dc:creator>Haltalli, M. L.</dc:creator>
<dc:creator>Lawrence, J. E.</dc:creator>
<dc:creator>Lisgo, S.</dc:creator>
<dc:creator>Balogh, P.</dc:creator>
<dc:creator>Meyer, K. B.</dc:creator>
<dc:creator>Prigmore, E.</dc:creator>
<dc:creator>Ambridge, K.</dc:creator>
<dc:creator>Sarkin Jain, M.</dc:creator>
<dc:creator>Efremova, M.</dc:creator>
<dc:creator>Pickard, K.</dc:creator>
<dc:creator>Creasey, T.</dc:creator>
<dc:creator>Bacardit, J.</dc:creator>
<dc:creator>Henderson, D.</dc:creator>
<dc:creator>Coxhead, J.</dc:creator>
<dc:creator>Filby, A.</dc:creator>
<dc:creator>Hussain, R.</dc:creator>
<dc:creator>Dixon, D.</dc:creator>
<dc:creator>McDonald,</dc:creator>
<dc:date>2021-06-25</dc:date>
<dc:identifier>doi:10.1101/2021.06.25.449771</dc:identifier>
<dc:title><![CDATA[Intrinsic and extrinsic regulation of human fetal bone marrow haematopoiesis and perturbations in Down syndrome]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-06-25</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.07.19.452954v1?rss=1">
<title>
<![CDATA[
Single-nucleus cross-tissue molecular reference maps to decipher disease gene function 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.07.19.452954v1?rss=1"
</link>
<description><![CDATA[
Understanding the function of genes and their regulation in tissue homeostasis and disease requires knowing the cellular context in which genes are expressed in tissues across the body. Single cell genomics allows the generation of detailed cellular atlases in human tissues, but most efforts are focused on single tissue types. Here, we establish a framework for profiling multiple tissues across the human body at single-cell resolution using single nucleus RNA-Seq (snRNA-seq), and apply it to 8 diverse, archived, frozen tissue types (three donors per tissue). We apply four snRNA-seq methods to each of 25 samples from 16 donors, generating a cross-tissue atlas of 209,126 nuclei profiles, and benchmark them vs. scRNA-seq of comparable fresh tissues. We use a conditional variational autoencoder (cVAE) to integrate an atlas across tissues, donors, and laboratory methods. We highlight shared and tissue-specific features of tissue-resident immune cells, identifying tissue-restricted and non-restricted resident myeloid populations. These include a cross-tissue conserved dichotomy between LYVE1- and HLA class II-expressing macrophages, and the broad presence of LAM-like macrophages across healthy tissues that is also observed in disease. For rare, monogenic muscle diseases, we identify cell types that likely underlie the neuromuscular, metabolic, and immune components of these diseases, and biological processes involved in their pathology. For common complex diseases and traits analyzed by GWAS, we identify the cell types and gene modules that potentially underlie disease mechanisms. The experimental and analytical frameworks we describe will enable the generation of large-scale studies of how cellular and molecular processes vary across individuals and populations.
]]></description>
<dc:creator>Eraslan, G.</dc:creator>
<dc:creator>Drokhlyansky, E.</dc:creator>
<dc:creator>Anand, S.</dc:creator>
<dc:creator>Subramanian, A.</dc:creator>
<dc:creator>Fiskin, E.</dc:creator>
<dc:creator>Slyper, M.</dc:creator>
<dc:creator>Wang, J.</dc:creator>
<dc:creator>Van Wittenberghe, N.</dc:creator>
<dc:creator>Rouhana, J. M.</dc:creator>
<dc:creator>Waldman, J.</dc:creator>
<dc:creator>Ashenberg, O.</dc:creator>
<dc:creator>Dionne, D.</dc:creator>
<dc:creator>Win, T. S.</dc:creator>
<dc:creator>Cuoco, M. S.</dc:creator>
<dc:creator>Kuksenko, O.</dc:creator>
<dc:creator>Branton, P. A.</dc:creator>
<dc:creator>Marshall, J. L.</dc:creator>
<dc:creator>Greka, A.</dc:creator>
<dc:creator>Getz, G.</dc:creator>
<dc:creator>Segre, A. V.</dc:creator>
<dc:creator>Aguet, F.</dc:creator>
<dc:creator>Rozenblatt-Rosen, O.</dc:creator>
<dc:creator>Ardlie, K. G.</dc:creator>
<dc:creator>Regev, A.</dc:creator>
<dc:date>2021-07-19</dc:date>
<dc:identifier>doi:10.1101/2021.07.19.452954</dc:identifier>
<dc:title><![CDATA[Single-nucleus cross-tissue molecular reference maps to decipher disease gene function]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-07-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.06.07.447287v1?rss=1">
<title>
<![CDATA[
A Web Portal and Workbench for Biological Dissection of Single Cell COVID-19 Host Responses 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.06.07.447287v1?rss=1"
</link>
<description><![CDATA[
Numerous studies have provided single-cell transcriptome profiles of host responses to SARS-CoV-2 infection. Critically lacking however is a datamine that allows users to compare and explore cell profiles to gain insights and develop new hypotheses. To accomplish this, we harmonized datasets from COVID-19 and other control condition blood, bronchoalveolar lavage, and tissue samples, and derived a compendium of gene signature modules per cell type, subtype, clinical condition, and compartment. We demonstrate approaches to probe these via a new interactive web portal (http://toppcell.cchmc.org/ COVID-19). As examples, we develop three hypotheses: (1) a multicellular signaling cascade among alternatively differentiated monocyte-derived macrophages whose tasks include T cell recruitment and activation; (2) novel platelet subtypes with drastically modulated expression of genes responsible for adhesion, coagulation and thrombosis; and (3) a multilineage cell activator network able to drive extrafollicular B maturation via an ensemble of genes strongly associated with risk for developing post-viral autoimmunity.
]]></description>
<dc:creator>Jin, K.</dc:creator>
<dc:creator>Bardes, E. E.</dc:creator>
<dc:creator>Mitelpunkt, A.</dc:creator>
<dc:creator>Wang, Y. J.</dc:creator>
<dc:creator>Bhatnagar, S.</dc:creator>
<dc:creator>Sengupta, S.</dc:creator>
<dc:creator>Krummel, D. P.</dc:creator>
<dc:creator>Rothenberg, M. E.</dc:creator>
<dc:creator>Aronow, B. J.</dc:creator>
<dc:date>2021-06-07</dc:date>
<dc:identifier>doi:10.1101/2021.06.07.447287</dc:identifier>
<dc:title><![CDATA[A Web Portal and Workbench for Biological Dissection of Single Cell COVID-19 Host Responses]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-06-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.05.31.446440v1?rss=1">
<title>
<![CDATA[
Anatomical Structures, Cell Types, and Biomarkers Tables Plus 3D Reference Organs in Support of a Human Reference Atlas 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.05.31.446440v1?rss=1"
</link>
<description><![CDATA[
1.This paper reviews efforts across 16 international consortia to construct human anatomical structures, cell types, and biomarkers (ASCT+B) tables and three-dimensional reference organs in support of a Human Reference Atlas. We detail the ontological descriptions and spatial three-dimensional anatomical representations together with user interfaces that support the registration and exploration of human tissue data. Four use cases are presented to demonstrate the utility of ASCT+B tables for advancing biomedical research and improving health.
]]></description>
<dc:creator>Borner, K.</dc:creator>
<dc:creator>Teichmann, S. A.</dc:creator>
<dc:creator>Quardokus, E. M.</dc:creator>
<dc:creator>Gee, J.</dc:creator>
<dc:creator>Browne, K.</dc:creator>
<dc:creator>Osumi-Sutherland, D.</dc:creator>
<dc:creator>Herr, B. W.</dc:creator>
<dc:creator>Bueckle, A.</dc:creator>
<dc:creator>Paul, H.</dc:creator>
<dc:creator>Haniffa, M. A.</dc:creator>
<dc:creator>Jardine, L.</dc:creator>
<dc:creator>Bernard, A.</dc:creator>
<dc:creator>Ding, S.-L.</dc:creator>
<dc:creator>Miller, J. A.</dc:creator>
<dc:creator>Lin, S.</dc:creator>
<dc:creator>Halushka, M.</dc:creator>
<dc:creator>Boppana, A.</dc:creator>
<dc:creator>Longacre, T. A.</dc:creator>
<dc:creator>Hickey, J.</dc:creator>
<dc:creator>Lin, Y.</dc:creator>
<dc:creator>Valerius, M. T.</dc:creator>
<dc:creator>He, Y.</dc:creator>
<dc:creator>Pryhuber, G.</dc:creator>
<dc:creator>Sun, X.</dc:creator>
<dc:creator>Jorgensen, M.</dc:creator>
<dc:creator>Radtke, A.</dc:creator>
<dc:creator>Wasserfall, C.</dc:creator>
<dc:creator>Ginty, F.</dc:creator>
<dc:creator>Ho, J.</dc:creator>
<dc:creator>Sunshine, J.</dc:creator>
<dc:creator>Beuschel, R. T.</dc:creator>
<dc:creator>Brusko, M.</dc:creator>
<dc:creator>Lee, S.</dc:creator>
<dc:creator>Malhotra, R.</dc:creator>
<dc:creator>Jain, S.</dc:creator>
<dc:creator>Weber, G.</dc:creator>
<dc:date>2021-06-01</dc:date>
<dc:identifier>doi:10.1101/2021.05.31.446440</dc:identifier>
<dc:title><![CDATA[Anatomical Structures, Cell Types, and Biomarkers Tables Plus 3D Reference Organs in Support of a Human Reference Atlas]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-06-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.11.26.470108v1?rss=1">
<title>
<![CDATA[
A spatial multi-omics atlas of the human lung reveals a novel immune cell survival niche 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.11.26.470108v1?rss=1"
</link>
<description><![CDATA[
Multiple distinct cell types of the human lung and airways have been defined by single cell RNA sequencing (scRNAseq). Here we present a multi-omics spatial lung atlas to define novel cell types which we map back into the macro- and micro-anatomical tissue context to define functional tissue microenvironments. Firstly, we have generated single cell and nuclei RNA sequencing, VDJ-sequencing and Visium Spatial Transcriptomics data sets from 5 different locations of the human lung and airways. Secondly, we define additional cell types/states, as well as spatially map novel and known human airway cell types, such as adult lung chondrocytes, submucosal gland (SMG) duct cells, distinct pericyte and smooth muscle subtypes, immune-recruiting fibroblasts, peribronchial and perichondrial fibroblasts, peripheral nerve associated fibroblasts and Schwann cells. Finally, we define a survival niche for IgA-secreting plasma cells at the SMG, comprising the newly defined epithelial SMG-Duct cells, and B and T lineage immune cells. Using our transcriptomic data for cell-cell interaction analysis, we propose a signalling circuit that establishes and supports this niche. Overall, we provide a transcriptional and spatial lung atlas with multiple novel cell types that allows for the study of specific tissue microenvironments such as the newly defined gland-associated lymphoid niche (GALN).
]]></description>
<dc:creator>Madissoon, E.</dc:creator>
<dc:creator>Oliver, A. J.</dc:creator>
<dc:creator>Kleshchevnikov, V.</dc:creator>
<dc:creator>Wilbrey-Clark, A.</dc:creator>
<dc:creator>Polanski, K.</dc:creator>
<dc:creator>Ribeiro Orsi, A. E.</dc:creator>
<dc:creator>Mamanova, L.</dc:creator>
<dc:creator>Bolt, L.</dc:creator>
<dc:creator>Pett, J. P.</dc:creator>
<dc:creator>Huang, N.</dc:creator>
<dc:creator>Elmentaite, R.</dc:creator>
<dc:creator>Richoz, N.</dc:creator>
<dc:creator>He, P.</dc:creator>
<dc:creator>Dabrowska, M.</dc:creator>
<dc:creator>Tuck, E.</dc:creator>
<dc:creator>Prigmore, E.</dc:creator>
<dc:creator>Knights, A.</dc:creator>
<dc:creator>Oszlanczi, A.</dc:creator>
<dc:creator>Hunter, A.</dc:creator>
<dc:creator>Pritchard, S.</dc:creator>
<dc:creator>Vieira, S. F.</dc:creator>
<dc:creator>Patel, M.</dc:creator>
<dc:creator>Mahbubani, K.</dc:creator>
<dc:creator>Georgakopoulos, N.</dc:creator>
<dc:creator>Clatworthy, M.</dc:creator>
<dc:creator>Stegle, O.</dc:creator>
<dc:creator>Bayraktar, O. A.</dc:creator>
<dc:creator>Saeb-Parsy, K.</dc:creator>
<dc:creator>Kumasaka, N.</dc:creator>
<dc:creator>Teichmann, S. A.</dc:creator>
<dc:creator>Meyer, K. B.</dc:creator>
<dc:date>2021-11-27</dc:date>
<dc:identifier>doi:10.1101/2021.11.26.470108</dc:identifier>
<dc:title><![CDATA[A spatial multi-omics atlas of the human lung reveals a novel immune cell survival niche]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-11-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.11.02.466852v1?rss=1">
<title>
<![CDATA[
Comprehensive identification of fetal cis-regulatory elements in the human genome by single-cell multi-omics analysis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.11.02.466852v1?rss=1"
</link>
<description><![CDATA[
The regulatory programs driving early organogenesis in human is complex and still poorly understood. We performed parallel profiling of gene expression and chromatin accessibility to 28 human fetal tissue samples representing 14 organs in the first trimester. Collectively, we have generated 415,793 single-cell profiles. By integration analysis of transcriptome and chromatin accessibility, we detected 225 distinct cell types and 848,475 candidate accessible cis-regulatory elements (aCREs). By linking regulatory elements to their putative target genes, we identified not only 108,699 enhancers, but also 23,392 silencers elements. We uncovered thousands of genes regulated by both enhancers and silencers in an organ or cell-type-specific manner. Furthermore, our unique approach revealed a substantial proportion of distal DNA elements are transcribed CREs (tCREs), which show both open chromatin signal and transcription initiation activity of non-coding transcript. The landscape of fetal cis-regulatory elements facilitates the interpretation of the genetic variant of complex disease and infer the cell type of origin for cancer. Overall, our data provide a comprehensive map of the fetal cis-regulatory elements at single-cell resolution and a valuable resource for future study of human development and disease.
]]></description>
<dc:creator>Yu, H.</dc:creator>
<dc:creator>Ai, N.</dc:creator>
<dc:creator>Peng, P.</dc:creator>
<dc:creator>Ke, Y. w.</dc:creator>
<dc:creator>Chen, X. p.</dc:creator>
<dc:creator>Li, Y.</dc:creator>
<dc:creator>Zhao, T.</dc:creator>
<dc:creator>Jiang, S.</dc:creator>
<dc:creator>Liu, J.</dc:creator>
<dc:creator>Jiang, L.</dc:creator>
<dc:date>2021-11-03</dc:date>
<dc:identifier>doi:10.1101/2021.11.02.466852</dc:identifier>
<dc:title><![CDATA[Comprehensive identification of fetal cis-regulatory elements in the human genome by single-cell multi-omics analysis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-11-03</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.12.20.473453v1?rss=1">
<title>
<![CDATA[
Single-Cell Atlas of Common Variable Immunodeficiency reveals germinal center-associated epigenetic dysregulation in B cell responses 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.12.20.473453v1?rss=1"
</link>
<description><![CDATA[
Common variable immunodeficiency (CVID), the most prevalent symptomatic primary immunodeficiency, is characterized by impaired terminal B-cell differentiation and defective antibody responses. Incomplete genetic penetrance and a wide range of phenotypic expressivity in CVID suggest the participation of additional pathogenic mechanisms. Monozygotic (MZ) twins discordant for CVID are uniquely valuable for studying the contribution of epigenetics to the disease. We used single-cell epigenomics and transcriptomics to create a cell census of naive-to-memory B cell differentiation in a pair of CVID-discordant MZ twins. Our analysis identifies DNA methylation, chromatin accessibility and transcriptional defects in memory B cells that mirror defective cell-cell communication defects following activation. These findings were validated in a cohort of CVID patients and healthy donors. Our findings provide a comprehensive multi-omics map of alterations in naive-to-memory B-cell transition in CVID and reveal links between the epigenome and immune cell cross-talk. Our resource, publicly available at the Human Cell Atlas, paves the way for future diagnosis and treatments of CVID patients.
]]></description>
<dc:creator>Rodriguez-Ubreva, J.</dc:creator>
<dc:creator>Arutyunyan, A.</dc:creator>
<dc:creator>Bonder, M. J.</dc:creator>
<dc:creator>Del Pino-Molina, L.</dc:creator>
<dc:creator>Clark, S.</dc:creator>
<dc:creator>de la Calle-Fabregat, C.</dc:creator>
<dc:creator>Garcia-Alonso, L.</dc:creator>
<dc:creator>Handfield, L.-F.</dc:creator>
<dc:creator>Ciudad, L.</dc:creator>
<dc:creator>Andres-Leon, E.</dc:creator>
<dc:creator>Krueger, F.</dc:creator>
<dc:creator>Catala-Moll, F.</dc:creator>
<dc:creator>Rodriguez-Cortez, V. C.</dc:creator>
<dc:creator>Polanski, K.</dc:creator>
<dc:creator>Mamanova, L.</dc:creator>
<dc:creator>van Dongen, S.</dc:creator>
<dc:creator>Kiselev, V. Y.</dc:creator>
<dc:creator>Martinez-Saavedra, M. T.</dc:creator>
<dc:creator>Heyn, H.</dc:creator>
<dc:creator>Martin, J.</dc:creator>
<dc:creator>Warnatz, K.</dc:creator>
<dc:creator>Lopez-Granados, E.</dc:creator>
<dc:creator>Rodriguez-Gallego, C.</dc:creator>
<dc:creator>Stegle, O.</dc:creator>
<dc:creator>Kelsey, G. D.</dc:creator>
<dc:creator>Vento-Tormo, R.</dc:creator>
<dc:creator>Ballestar, E.</dc:creator>
<dc:date>2021-12-21</dc:date>
<dc:identifier>doi:10.1101/2021.12.20.473453</dc:identifier>
<dc:title><![CDATA[Single-Cell Atlas of Common Variable Immunodeficiency reveals germinal center-associated epigenetic dysregulation in B cell responses]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-12-21</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.01.11.474933v1?rss=1">
<title>
<![CDATA[
A human fetal lung cell atlas uncovers proximal-distal gradients of differentiation and key regulators of epithelial fates 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.01.11.474933v1?rss=1"
</link>
<description><![CDATA[
We present a multiomic cell atlas of human lung development that combines single cell RNA and ATAC sequencing, high throughput spatial transcriptomics and single cell imaging. Coupling single cell methods with spatial analysis has allowed a comprehensive cellular survey of the epithelial, mesenchymal, endothelial and erythrocyte/leukocyte compartments from 5-22 post conception weeks. We identify new cell states in all compartments. These include developmental-specific secretory progenitors and a new subtype of neuroendocrine cell related to human small cell lung cancer. Our datasets are available through our web interface (https://lungcellatlas.org). Finally, to illustrate its general utility, we use our cell atlas to generate predictions about cell-cell signalling and transcription factor hierarchies which we test using organoid models.

HighlightsO_LISpatiotemporal atlas of human lung development from 5-22 post conception weeks identifies 144 cell types/states.
C_LIO_LITracking the developmental origins of multiple cell compartments, including new progenitor states.
C_LIO_LIFunctional diversity of fibroblasts in distinct anatomical signalling niches.
C_LIO_LIResource applied to interrogate and experimentally test the transcription factor code controlling neuroendocrine cell heterogeneity and the origins of small cell lung cancer.
C_LI
]]></description>
<dc:creator>He, P.</dc:creator>
<dc:creator>Lim, K.</dc:creator>
<dc:creator>Sun, D.</dc:creator>
<dc:creator>Pett, J. P.</dc:creator>
<dc:creator>Jeng, Q.</dc:creator>
<dc:creator>Polanski, K.</dc:creator>
<dc:creator>Dong, Z.</dc:creator>
<dc:creator>Bolt, L.</dc:creator>
<dc:creator>Richardson, L.</dc:creator>
<dc:creator>Mamanova, L.</dc:creator>
<dc:creator>Dabrowska, M.</dc:creator>
<dc:creator>Wilbrey-Clark, A. L.</dc:creator>
<dc:creator>Madissoon, E.</dc:creator>
<dc:creator>Tuong, K. Z.</dc:creator>
<dc:creator>Dann, E.</dc:creator>
<dc:creator>Suo, C.</dc:creator>
<dc:creator>Goh, I.</dc:creator>
<dc:creator>He, X.</dc:creator>
<dc:creator>Barker, R. A.</dc:creator>
<dc:creator>Teichmann, S.</dc:creator>
<dc:creator>Marioni, J.</dc:creator>
<dc:creator>Meyer, K. B.</dc:creator>
<dc:creator>Rawlins, E. L.</dc:creator>
<dc:date>2022-01-11</dc:date>
<dc:identifier>doi:10.1101/2022.01.11.474933</dc:identifier>
<dc:title><![CDATA[A human fetal lung cell atlas uncovers proximal-distal gradients of differentiation and key regulators of epithelial fates]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-01-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.01.17.476665v1?rss=1">
<title>
<![CDATA[
Mapping the developing human immune system across organs 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.01.17.476665v1?rss=1"
</link>
<description><![CDATA[
Recent advances in single cell genomics technologies have facilitated studies on the developing immune system at unprecedented scale and resolution. However, these studies have focused on one or a few organs and were thus limited in understanding the developing immune system as a distributed network across tissues. Here, we profiled prenatal haematopoietic organs, lymphoid organs and non-lymphoid tissues using a combination of single-cell RNA sequencing, paired antigen-receptor sequencing and spatial transcriptomics to reconstruct the developing human immune system. Our analysis revealed the acquisition of immune effector transcriptome profiles in macrophages, mast cells and NK cells from the second trimester, and the transcriptomic changes accompanying the late-stage maturation of developing monocytes and T cells that extended from their organ of origin to peripheral tissues. We uncovered system-wide blood and immune cell development beyond the conventional primary haematopoietic organs. We further identified, extensively characterised and functionally validated the human prenatal B1 cells. Finally, we provide evidence for thymocyte-thymocyte selection origin for {beta}TCR- expressing unconventional T cells based on TCR gene usage and an in vitro artificial thymic organoid culture model. Our comprehensive atlas of the developing human immune system provides both valuable data resources and biological insights that will facilitate cell engineering, regenerative medicine and disease understanding.

One-Sentence SummaryBy performing a comprehensive single-cell RNA sequencing atlas of human developing immune system together with antigen-receptor sequencing and spatial transcriptomics, we explored the cross-gestation and cross-organ variability in immune cells, discovered system-wide blood and immune cell development, identified, characterised and functionally validated the properties of human prenatal B1 cells and the origin of unconventional T cells.
]]></description>
<dc:creator>Suo, C.</dc:creator>
<dc:creator>Dann, E.</dc:creator>
<dc:creator>Goh, I.</dc:creator>
<dc:creator>Jardine, L.</dc:creator>
<dc:creator>Kleshchevnikov, V.</dc:creator>
<dc:creator>Park, J.-E.</dc:creator>
<dc:creator>Botting, R. A.</dc:creator>
<dc:creator>Stephenson, E.</dc:creator>
<dc:creator>Engelbert, J.</dc:creator>
<dc:creator>Tuong, Z. K.</dc:creator>
<dc:creator>Polanski, K.</dc:creator>
<dc:creator>Yayon, N.</dc:creator>
<dc:creator>Xu, C.</dc:creator>
<dc:creator>Suchanek, O.</dc:creator>
<dc:creator>Elmentaite, R.</dc:creator>
<dc:creator>Dominguez Conde, C.</dc:creator>
<dc:creator>He, P.</dc:creator>
<dc:creator>Pritchard, S.</dc:creator>
<dc:creator>Miah, M.</dc:creator>
<dc:creator>Moldovan, C.</dc:creator>
<dc:creator>Steemers, A. S.</dc:creator>
<dc:creator>Prete, M.</dc:creator>
<dc:creator>Marioni, J. C.</dc:creator>
<dc:creator>Clatworthy, M. R.</dc:creator>
<dc:creator>Haniffa, M.</dc:creator>
<dc:creator>Teichmann, S. A.</dc:creator>
<dc:date>2022-01-18</dc:date>
<dc:identifier>doi:10.1101/2022.01.17.476665</dc:identifier>
<dc:title><![CDATA[Mapping the developing human immune system across organs]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-01-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.01.11.475631v1?rss=1">
<title>
<![CDATA[
Developmental origins of cell heterogeneity in the human lung 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.01.11.475631v1?rss=1"
</link>
<description><![CDATA[
The lung contains numerous specialized cell-types with distinct roles in tissue function and integrity. To clarify the origins and mechanisms generating cell heterogeneity, we created a first comprehensive topographic atlas of early human lung development. We report 83 cell states, several spatially-resolved developmental trajectories and predict cell interactions within defined tissue niches. We integrated scRNA-Seq and spatial transcriptomics into a web-based, open platform for interactive exploration. To illustrate the utility of our approach we show distinct states of secretory and neuroendocrine cells, largely overlapping with the programs activated either during lung fibrosis or small cell lung cancer progression. We define the origin of uncharacterized airway fibroblasts associated with airway smooth muscle in bronchovascular bundles, and describe a trajectory of Schwann cell progenitors to intrinsic parasympathetic neurons controlling bronchoconstriction. Our atlas provides a rich resource for further research and a reference for defining deviations from homeostatic and repair mechanisms leading to pulmonary diseases.
]]></description>
<dc:creator>Sountoulidis, A.</dc:creator>
<dc:creator>Marco Salas, S.</dc:creator>
<dc:creator>Braun, E.</dc:creator>
<dc:creator>Avenel, C.</dc:creator>
<dc:creator>Bergenstrahle, J.</dc:creator>
<dc:creator>Vicari, M.</dc:creator>
<dc:creator>Czarnewski Barenco, P. V.</dc:creator>
<dc:creator>Theelke, J.</dc:creator>
<dc:creator>Liontos, A.</dc:creator>
<dc:creator>Abalo, X.</dc:creator>
<dc:creator>Andrusivova, Z.</dc:creator>
<dc:creator>Asp, M.</dc:creator>
<dc:creator>Li, X.</dc:creator>
<dc:creator>Hu, L.</dc:creator>
<dc:creator>Sariyar, S.</dc:creator>
<dc:creator>Martinez Casals, A.</dc:creator>
<dc:creator>Ayoglu, B.</dc:creator>
<dc:creator>Firsova, A. B.</dc:creator>
<dc:creator>Michaelsson, J.</dc:creator>
<dc:creator>Lundberg, E.</dc:creator>
<dc:creator>Wählby, C.</dc:creator>
<dc:creator>Sundström, E.</dc:creator>
<dc:creator>Linnarsson, S.</dc:creator>
<dc:creator>Lundeberg, J.</dc:creator>
<dc:creator>Nilsson, M.</dc:creator>
<dc:creator>Samakovlis, C.</dc:creator>
<dc:date>2022-01-12</dc:date>
<dc:identifier>doi:10.1101/2022.01.11.475631</dc:identifier>
<dc:title><![CDATA[Developmental origins of cell heterogeneity in the human lung]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-01-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.10.05.460818v1?rss=1">
<title>
<![CDATA[
A proximal-to-distal survey of healthy adult human small intestine and colon epithelium by single-cell transcriptomics 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.10.05.460818v1?rss=1"
</link>
<description><![CDATA[
Background and AimsSingle-cell transcriptomics offer unprecedented resolution of tissue function at the cellular level, yet studies analyzing healthy adult human small intestine and colon are sparse. Here, we present single-cell transcriptomics covering the duodenum, jejunum, ileum, and ascending, transverse, and descending colon from 3 humans.

Methods12,590 single epithelial cells from three independently processed organ donors were evaluated for organ-specific lineage biomarkers, differentially regulated genes, receptors, and drug targets. Analyses focused on intrinsic cell properties and capacity for response to extrinsic signals along the gut axis across different humans.

ResultCells were assigned to 25 epithelial lineage clusters. Human intestinal stem cells (ISCs) are not specifically marked by many murine ISC markers. Lysozyme expression is not unique to human Paneth cells (PCs), and PCs lack expression of expected niche-factors. BEST4+ cells express NPY and show maturational differences between SI and colon. Tuft cells possess a broad ability to interact with the innate and adaptive immune systems through previously unreported receptors. Some classes of mucins, hormones, cell-junction, and nutrient absorption genes show unappreciated regional expression differences across lineages. Differential expression of receptors and drug targets across lineages reveals biological variation and potential for variegated responses.

ConclusionsOur study identifies novel lineage marker genes; covers regional differences; shows important differences between mouse and human gut epithelium; and reveals insight into how the epithelium responds to the environment and drugs. This comprehensive cell atlas of the healthy adult human intestinal epithelium resolves likely functional differences across anatomical regions along the gastrointestinal tract and advances our understanding of human intestinal physiology.
]]></description>
<dc:creator>Burclaff, J.</dc:creator>
<dc:creator>Bliton, R. J.</dc:creator>
<dc:creator>Breau, K. A.</dc:creator>
<dc:creator>Ok, M. T.</dc:creator>
<dc:creator>Gomez-Martinez, I.</dc:creator>
<dc:creator>Ranek, J. S.</dc:creator>
<dc:creator>Bhatt, A. P.</dc:creator>
<dc:creator>Purvis, J. E.</dc:creator>
<dc:creator>Woosley, J. T.</dc:creator>
<dc:creator>Magness, S. T.</dc:creator>
<dc:date>2021-10-06</dc:date>
<dc:identifier>doi:10.1101/2021.10.05.460818</dc:identifier>
<dc:title><![CDATA[A proximal-to-distal survey of healthy adult human small intestine and colon epithelium by single-cell transcriptomics]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-10-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.07.19.452956v1?rss=1">
<title>
<![CDATA[
The Tabula Sapiens: a single cell transcriptomic atlas of multiple organs from individual human donors 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.07.19.452956v1?rss=1"
</link>
<description><![CDATA[
Molecular characterization of cell types using single cell transcriptome sequencing is revolutionizing cell biology and enabling new insights into the physiology of human organs. We created a human reference atlas comprising nearly 500,000 cells from 24 different tissues and organs, many from the same donor. This atlas enabled molecular characterization of more than 400 cell types, their distribution across tissues and tissue specific variation in gene expression. Using multiple tissues from a single donor enabled identification of the clonal distribution of T cells between tissues, the tissue specific mutation rate in B cells, and analysis of the cell cycle state and proliferative potential of shared cell types across tissues. Cell type specific RNA splicing was discovered and analyzed across tissues within an individual.
]]></description>
<dc:creator>The Tabula Sapiens Consortium,</dc:creator>
<dc:creator>Quake, S. R.</dc:creator>
<dc:date>2021-07-20</dc:date>
<dc:identifier>doi:10.1101/2021.07.19.452956</dc:identifier>
<dc:title><![CDATA[The Tabula Sapiens: a single cell transcriptomic atlas of multiple organs from individual human donors]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-07-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.11.17.469005v1?rss=1">
<title>
<![CDATA[
Robust temporal map of human in vitro myelopoiesis using single-cell genomics 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.11.17.469005v1?rss=1"
</link>
<description><![CDATA[
Myeloid cells have a central role in homeostasis and tissue defence. Characterising the current in vitro protocols of myelopoiesis is imperative for their use in research and immunotherapy as well as for understanding the early stages of myeloid differentiation in humans. Here, we profiled the transcriptome of more than 400k cells and generated a robust molecular map of the differentiation of human induced pluripotent stem cells (iPSC) into macrophages. By integrating our in vitro datasets with in vivo single-cell developmental atlases, we found that in vitro macrophage differentiation recapitulates features of in vivo yolk sac hematopoiesis, which happens prior to the appearance of definitive hematopoietic stem cells (HSC). During in vitro myelopoiesis, a wide range of myeloid cells are generated, including erythrocytes, mast cells and monocytes, suggesting that, during early human development, the HSC-independent immune wave gives rise to multiple myeloid cell lineages. We leveraged this model to characterize the transition of hemogenic endothelium into myeloid cells, uncovering poorly described myeloid progenitors and regulatory programs. Taking advantage of the variety of myeloid cells produced, we developed a new protocol to produce type 2 conventional dendritic cells (cDC2) in vitro. We found that the underlying regulatory networks coding for myeloid identity are conserved in vivo and in vitro. Using genetic engineering techniques, we validated the effects of key transcription factors important for cDC2 and macrophage identity and ontogeny. This roadmap of early myeloid differentiation will serve as an important resource for investigating the initial stages of hematopoiesis, which are largely unexplored in humans, and will open up new therapeutic opportunities.
]]></description>
<dc:creator>Alsinet, C.</dc:creator>
<dc:creator>Primo, M.</dc:creator>
<dc:creator>Lorenzi, V.</dc:creator>
<dc:creator>Knights, A. J.</dc:creator>
<dc:creator>Sancho-Serra, C.</dc:creator>
<dc:creator>Park, J.-E.</dc:creator>
<dc:creator>Wyspianska, B. S.</dc:creator>
<dc:creator>Tough, D. F.</dc:creator>
<dc:creator>Alvarez, D.</dc:creator>
<dc:creator>Gaffney, D. J.</dc:creator>
<dc:creator>Vento-Tormo, R.</dc:creator>
<dc:date>2021-11-19</dc:date>
<dc:identifier>doi:10.1101/2021.11.17.469005</dc:identifier>
<dc:title><![CDATA[Robust temporal map of human in vitro myelopoiesis using single-cell genomics]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-11-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.04.28.441762v1?rss=1">
<title>
<![CDATA[
Cross-tissue immune cell analysis reveals tissue-specific adaptations and clonal architecture across the human body 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.04.28.441762v1?rss=1"
</link>
<description><![CDATA[
Despite their crucial role in health and disease, our knowledge of immune cells within human tissues remains limited. Here, we surveyed the immune compartment of 15 tissues of six deceased adult donors by single-cell RNA sequencing and paired VDJ sequencing. To systematically resolve immune cell heterogeneity across tissues, we developed CellTypist, a machine learning tool for rapid and precise cell type annotation. Using this approach, combined with detailed curation, we determined the tissue distribution of 45 finely phenotyped immune cell types and states, revealing hitherto unappreciated tissue-specific features and clonal architecture of T and B cells. In summary, our multi-tissue approach lays the foundation for identifying highly resolved immune cell types by leveraging a common reference dataset, tissue-integrated expression analysis and antigen receptor sequencing.

One Sentence SummaryWe provide an immune cell atlas, including antigen receptor repertoire profiling, across lymphoid and non-lymphoid human tissues.
]]></description>
<dc:creator>Dominguez Conde, C.</dc:creator>
<dc:creator>Gomes, T.</dc:creator>
<dc:creator>Jarvis, L. B.</dc:creator>
<dc:creator>Xu, C.</dc:creator>
<dc:creator>Howlett, S.</dc:creator>
<dc:creator>Rainbow, D.</dc:creator>
<dc:creator>Suchanek, O.</dc:creator>
<dc:creator>King, H.</dc:creator>
<dc:creator>Mamanova, L.</dc:creator>
<dc:creator>Polanski, K.</dc:creator>
<dc:creator>Huang, N.</dc:creator>
<dc:creator>Fasouli, E.</dc:creator>
<dc:creator>Mahbubani, K.</dc:creator>
<dc:creator>Prete, M.</dc:creator>
<dc:creator>Campos, L.</dc:creator>
<dc:creator>Mousa, H.</dc:creator>
<dc:creator>Needham, E.</dc:creator>
<dc:creator>Pritchard, S.</dc:creator>
<dc:creator>Li, T.</dc:creator>
<dc:creator>Elmentaite, R.</dc:creator>
<dc:creator>Park, J.</dc:creator>
<dc:creator>Menon, D.</dc:creator>
<dc:creator>Bayraktar, O.</dc:creator>
<dc:creator>James, L.</dc:creator>
<dc:creator>Meyer, K.</dc:creator>
<dc:creator>Clatworthy, M.</dc:creator>
<dc:creator>Saeb-Parsy, K.</dc:creator>
<dc:creator>Jones, J. L.</dc:creator>
<dc:creator>Teichmann, S. A.</dc:creator>
<dc:date>2021-04-28</dc:date>
<dc:identifier>doi:10.1101/2021.04.28.441762</dc:identifier>
<dc:title><![CDATA[Cross-tissue immune cell analysis reveals tissue-specific adaptations and clonal architecture across the human body]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-04-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.05.18.492475v1?rss=1">
<title>
<![CDATA[
Spatial Transcriptomics-correlated Electron Microscopy 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.05.18.492475v1?rss=1"
</link>
<description><![CDATA[
Current spatial transcriptomics methods identify cell states in a spatial context but lack morphological information. Scanning electron microscopy, in contrast, provides structural details at nanometer resolution but lacks molecular decoding of the diverse cellular states. To address this, we correlated MERFISH spatial transcriptomics with large area volume electron microscopy using adjacent tissue sections. We applied our technology to characterize the damage-associated microglial identities in mouse brain, allowing us, for the first time, to link the morphology of foamy microglia and interferon-response microglia with their transcriptional signatures.
]]></description>
<dc:creator>Androvic, P.</dc:creator>
<dc:creator>Schifferer, M.</dc:creator>
<dc:creator>Perez Anderson, K.</dc:creator>
<dc:creator>Cantuti-Castelvetri, L.</dc:creator>
<dc:creator>Ji, H.</dc:creator>
<dc:creator>Liu, L.</dc:creator>
<dc:creator>Besson-Girard, S.</dc:creator>
<dc:creator>Knoferle, J.</dc:creator>
<dc:creator>Simons, M.</dc:creator>
<dc:creator>Gokce, O.</dc:creator>
<dc:date>2022-05-20</dc:date>
<dc:identifier>doi:10.1101/2022.05.18.492475</dc:identifier>
<dc:title><![CDATA[Spatial Transcriptomics-correlated Electron Microscopy]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-05-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.05.22.493006v1?rss=1">
<title>
<![CDATA[
Single-Cell Transcriptome Analyses Reveal the Cell Diversity and Developmental Features of Human Gastric and Metaplastic Mucosa 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.05.22.493006v1?rss=1"
</link>
<description><![CDATA[
The stomach is an important digestive organ with a variety of biological functions. However, due to the complexity of its cellular and glandular composition, the precise cellular biology has yet to be elucidated. In this study, we conducted single-cell RNA sequence analysis of the human stomach and constructed a 137,610-cell dataset, the largest cell atlas reported to date. By integrating this single-cell analysis with spatial cellular distribution analysis, we were able to clarify novel aspects of the developmental and tissue homeostatic ecosystems in the human stomach. We identified LEFTY1+ as a potential stem cell marker in both gastric and intestinal metaplastic glands. We also revealed skewed distribution patterns for PDGFRA+BMP4+WNT5A+ fibroblasts that play pivotal roles in, or even precede, the phenotypic changes from gastric to metaplastic mucosa. Our extensive dataset will function as a fundamental resource in investigations of the stomach, including studies on development, aging, and carcinogenesis.
]]></description>
<dc:creator>Tsubosaka, A.</dc:creator>
<dc:creator>Komura, D.</dc:creator>
<dc:creator>Katoh, H.</dc:creator>
<dc:creator>Kakiuchi, M.</dc:creator>
<dc:creator>Onoyama, T.</dc:creator>
<dc:creator>Yamamoto, A.</dc:creator>
<dc:creator>Abe, H.</dc:creator>
<dc:creator>Seto, Y.</dc:creator>
<dc:creator>Ushiku, T.</dc:creator>
<dc:creator>Ishikawa, S.</dc:creator>
<dc:date>2022-05-23</dc:date>
<dc:identifier>doi:10.1101/2022.05.22.493006</dc:identifier>
<dc:title><![CDATA[Single-Cell Transcriptome Analyses Reveal the Cell Diversity and Developmental Features of Human Gastric and Metaplastic Mucosa]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-05-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.06.18.449034v1?rss=1">
<title>
<![CDATA[
Single nucleus pituitary transcriptomic and epigenetic landscape reveals human stem cell heterogeneity with diverse regulatory mechanisms 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.06.18.449034v1?rss=1"
</link>
<description><![CDATA[
Despite their importance in tissue homeostasis and renewal, human pituitary stem cells (PSCs) are incompletely characterized. We describe a human single nucleus (sn) RNAseq and ATACseq resource from pediatric, adult, and aged pituitaries (snpituitaryatlas.princeton.edu) and characterize cell type-specific gene expression and chromatin accessibility programs for all major pituitary cell lineages. We identify uncommitted PSCs, committing progenitor cells, and sex differences. Pseudotime trajectory analysis indicates that early life PSCs are distinct from the other age groups. Linear modeling of same-cell multiome data identifies regulatory domain accessibility sites and transcription factors (TFs) that are significantly associated with gene expression in PSCs compared to other cell types and within PSCs. Modeling the heterogeneous expression of two markers for committing cell lineages among PSCs shows significant correlation with regulatory domain accessibility for GATA3, but with TF expression for POMC. These findings characterize human stem cell lineages and reveal diverse mechanisms regulating key PSC genes.
]]></description>
<dc:creator>Zhang, Z.</dc:creator>
<dc:creator>Zamojski, M.</dc:creator>
<dc:creator>Smith, G. R.</dc:creator>
<dc:creator>Willis, T. L.</dc:creator>
<dc:creator>Yianni, V.</dc:creator>
<dc:creator>Mendelev, N.</dc:creator>
<dc:creator>Pincase, H.</dc:creator>
<dc:creator>Seenarine, N.</dc:creator>
<dc:creator>Amper, M. A. S.</dc:creator>
<dc:creator>Vasoya, M.</dc:creator>
<dc:creator>Nair, V.</dc:creator>
<dc:creator>Turgeon, J. L.</dc:creator>
<dc:creator>Bernard, D. J.</dc:creator>
<dc:creator>Troyanskaya, O. G.</dc:creator>
<dc:creator>Andoniadou, C. L.</dc:creator>
<dc:creator>Sealfon, S. C.</dc:creator>
<dc:creator>Ruf-Zamojski, F. M.</dc:creator>
<dc:date>2021-06-18</dc:date>
<dc:identifier>doi:10.1101/2021.06.18.449034</dc:identifier>
<dc:title><![CDATA[Single nucleus pituitary transcriptomic and epigenetic landscape reveals human stem cell heterogeneity with diverse regulatory mechanisms]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-06-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.06.06.138024v1?rss=1">
<title>
<![CDATA[
Single nucleus multi-omics regulatory atlas of the murine pituitary 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.06.06.138024v1?rss=1"
</link>
<description><![CDATA[
The pituitary regulates growth, reproduction and other endocrine systems. To investigate transcriptional network epigenetic mechanisms, we generated paired single nucleus (sn) transcriptome and chromatin accessibility profiles in single mouse pituitaries and genome-wide sn methylation datasets. Our analysis provided insight into cell type epigenetics, regulatory circuit and gene control mechanisms. Latent variable pathway analysis detected corresponding transcriptome and chromatin accessibility programs showing both inter-sexual and inter-individual variation. Multi-omics analysis of gene regulatory networks identified cell type-specific regulons whose composition and function were shaped by the promoter accessibility state of target genes. Co-accessibility analysis comprehensively identified putative cis-regulatory regions, including a domain 17kb upstream of Fshb that overlapped the fertility-linked rs11031006 human polymorphism. In vitro CRISPR-deletion at this locus increased Fshb levels, supporting this domains inferred regulatory role. The sn pituitary multi-omics atlas (snpituitaryatlas.princeton.edu) is a public resource for elucidating cell type-specific gene regulatory mechanisms and principles of transcription circuit control.
]]></description>
<dc:creator>Ruf-Zamojski, F. M.</dc:creator>
<dc:creator>Zhang, Z.</dc:creator>
<dc:creator>Zamojski, M.</dc:creator>
<dc:creator>Smith, G. R.</dc:creator>
<dc:creator>Mendelev, N.</dc:creator>
<dc:creator>Liu, H.</dc:creator>
<dc:creator>Nudelman, G.</dc:creator>
<dc:creator>Moriwaki, M.</dc:creator>
<dc:creator>Pincas, H.</dc:creator>
<dc:creator>Gomez Castanon, R.</dc:creator>
<dc:creator>Nair, V. D.</dc:creator>
<dc:creator>Seenarine, N.</dc:creator>
<dc:creator>Amper, M. A. S.</dc:creator>
<dc:creator>Zhou, X.</dc:creator>
<dc:creator>Ongaro, L.</dc:creator>
<dc:creator>Toufaily, C.</dc:creator>
<dc:creator>Schang, G.</dc:creator>
<dc:creator>Nery, J. R.</dc:creator>
<dc:creator>Bartlett, A.</dc:creator>
<dc:creator>Aldridge, A.</dc:creator>
<dc:creator>Jain, N.</dc:creator>
<dc:creator>Childs, G. V.</dc:creator>
<dc:creator>Troyanskaya, O. G.</dc:creator>
<dc:creator>Ecker, J. R.</dc:creator>
<dc:creator>Turgeon, J. L.</dc:creator>
<dc:creator>Welt, C. K.</dc:creator>
<dc:creator>Bernard, D. J.</dc:creator>
<dc:creator>Sealfon, S. C.</dc:creator>
<dc:date>2020-06-07</dc:date>
<dc:identifier>doi:10.1101/2020.06.06.138024</dc:identifier>
<dc:title><![CDATA[Single nucleus multi-omics regulatory atlas of the murine pituitary]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-06-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.10.31.514182v1?rss=1">
<title>
<![CDATA[
Singletrome: A method to analyze and enhance the transcriptome with long noncoding RNAs for single cell analysis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.10.31.514182v1?rss=1"
</link>
<description><![CDATA[
Single cell RNA sequencing (scRNA-seq) has revolutionized the study of gene expression in individual cell types, but scRNA-seq studies have focused primarily on expression of protein-coding genes. Long noncoding RNAs (lncRNAs) are more diverse than protein-coding genes, yet remain underexplored in part because they are under-represented in reference annotations applied to scRNA-seq. Merging annotations containing protein-coding and lncRNA genes is not sufficient, because the addition of lncRNA genes that overlap in sense and antisense with protein-coding genes will affect how reads are counted for both protein-coding and lncRNA genes. Here, we introduce Singletrome, a Singularity image that integrates protein-coding and lncRNA gene transfer format (GTF) annotations to generate enhanced annotations that take into account the sense and antisense overlap of annotated genes, maps scRNA-seq data, and produces files for downstream analysis and visualization. With Singletrome, we observed an increase in the number of reads mapped to exons, detected thousands of lncRNAs not included in GENCODE, and observed a decrease in uniquely mapped reads, indicating improved mapping specificity. Moreover, we were able to cluster cell types based solely on lncRNAs expression, and lncRNAs alone were able to predict cell types and human disease pathology through machine learning. This comprehensive annotation will allow mapping of lncRNA expression across cell types of the human body, facilitating the development of an atlas of human lncRNAs in health and disease with the ability to integrate new lncRNA annotations as they become available.
]]></description>
<dc:creator>Rahman, R.-U. U.</dc:creator>
<dc:creator>Ahmad, I.</dc:creator>
<dc:creator>Sparks, R.</dc:creator>
<dc:creator>Saad, A. B.</dc:creator>
<dc:creator>Mullen, A.</dc:creator>
<dc:date>2022-11-02</dc:date>
<dc:identifier>doi:10.1101/2022.10.31.514182</dc:identifier>
<dc:title><![CDATA[Singletrome: A method to analyze and enhance the transcriptome with long noncoding RNAs for single cell analysis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-11-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.11.18.517068v1?rss=1">
<title>
<![CDATA[
Single cell antigen receptor analysis reveals lymphocyte developmental origins 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.11.18.517068v1?rss=1"
</link>
<description><![CDATA[
Assessment of single-cell gene expression (scRNA-seq) and adaptive immune receptor sequencing (scVDJ-seq) has been invaluable in studying lymphocyte biology. Here, we introduce Dandelion, a computational pipeline for scVDJ-seq analysis. It enables the application of standard V(D)J analysis workflows to single-cell datasets, delivering improved V(D)J contig annotation and the identification of non-productive and partially spliced contigs. We devised a novel strategy to create an adaptive immune receptor feature space that can be used for both differential V(D)J usage analysis and pseudotime trajectory inference. The application of Dandelion improved the alignment of human thymic development trajectories of double positive T cells to mature single-positive CD4/CD8 T cells, with important new predictions of factors regulating lineage commitment. Dandelion analysis of other cell compartments provided novel insights into the origins of human B1 cells and ILC/NK cell development, illustrating the power of our approach. Dandelion is an open access resource (https://www.github.com/zktuong/dandelion) that will enable future discoveries.
]]></description>
<dc:creator>Suo, C.</dc:creator>
<dc:creator>Polanski, K.</dc:creator>
<dc:creator>Dann, E.</dc:creator>
<dc:creator>Lindeboom, R. G. H.</dc:creator>
<dc:creator>Vilarrasa Blasi, R.</dc:creator>
<dc:creator>Vento-Tormo, R.</dc:creator>
<dc:creator>Haniffa, M.</dc:creator>
<dc:creator>Meyer, K. B.</dc:creator>
<dc:creator>Tuong, Z. K.</dc:creator>
<dc:creator>Clatworthy, M. R.</dc:creator>
<dc:creator>Teichmann, S.</dc:creator>
<dc:date>2022-11-19</dc:date>
<dc:identifier>doi:10.1101/2022.11.18.517068</dc:identifier>
<dc:title><![CDATA[Single cell antigen receptor analysis reveals lymphocyte developmental origins]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-11-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.10.12.511898v1?rss=1">
<title>
<![CDATA[
Transcriptomic diversity of cell types across the adult human brain 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.10.12.511898v1?rss=1"
</link>
<description><![CDATA[
The human brain directs a wide range of complex behaviors ranging from fine motor skills to abstract intelligence and emotion. However, the diversity of cell types that support these skills has not been fully described. Here we used high-throughput single-nucleus RNA sequencing to systematically survey cells across the entire adult human brain in three postmortem donors. We sampled over three million nuclei from approximately 100 dissections across the forebrain, midbrain, and hindbrain. Our analysis identified 461 clusters and 3313 subclusters organized largely according to developmental origins. We found area-specific cortical neurons, as well as an unexpectedly high diversity of midbrain and hindbrain neurons. Astrocytes also exhibited regional diversity at multiple scales, comprising subtypes specific to the telencephalon and to more precise anatomical locations. Oligodendrocyte precursors comprised two distinct major types specific to the telencephalon and to the rest of the brain. Together, these findings demonstrate the unique cellular composition of the telencephalon with respect to all major brain cell types. As the first single-cell transcriptomic census of the entire human brain, we provide a resource for understanding the molecular diversity of the human brain in health and disease.
]]></description>
<dc:creator>Siletti, K.</dc:creator>
<dc:creator>Hodge, R. D.</dc:creator>
<dc:creator>Mossi Albiach, A.</dc:creator>
<dc:creator>Hu, L.</dc:creator>
<dc:creator>Lee, K. W.</dc:creator>
<dc:creator>Lönnerberg, P.</dc:creator>
<dc:creator>Bakken, T. E.</dc:creator>
<dc:creator>Ding, S.-L.</dc:creator>
<dc:creator>Clark, M.</dc:creator>
<dc:creator>Casper, T.</dc:creator>
<dc:creator>Dee, N.</dc:creator>
<dc:creator>Gloe, J.</dc:creator>
<dc:creator>Keene, C. D.</dc:creator>
<dc:creator>Nyhus, J.</dc:creator>
<dc:creator>Tung, H.</dc:creator>
<dc:creator>Yanny, A. M.</dc:creator>
<dc:creator>Arenas, E.</dc:creator>
<dc:creator>Lein, E. S.</dc:creator>
<dc:creator>Linnarsson, S.</dc:creator>
<dc:date>2022-10-14</dc:date>
<dc:identifier>doi:10.1101/2022.10.12.511898</dc:identifier>
<dc:title><![CDATA[Transcriptomic diversity of cell types across the adult human brain]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-10-14</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.10.24.513487v1?rss=1">
<title>
<![CDATA[
Comprehensive cell atlas of the first-trimester developing human brain 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.10.24.513487v1?rss=1"
</link>
<description><![CDATA[
The adult human brain likely comprises more than a thousand kinds of neurons, and an unknown number of glial cell types, but how cellular diversity arises during early brain development is not known. Here, in order to reveal the precise sequence of events during early brain development, we used single-cell RNA sequencing and spatial transcriptomics to uncover cell states and trajectories in human brains at 5 - 14 post-conceptional weeks (p.c.w.). We identified twelve major classes and over 600 distinct cell states, which mapped to precise spatial anatomical domains at 5 p.c.w. We uncovered detailed differentiation trajectories of the human forebrain, and a surprisingly large number of region-specific glioblasts maturing into distinct pre-astrocytes and pre-oligodendrocyte precursor cells (pre-OPCs). Our findings reveal the emergence of cell types during the critical first trimester of human brain development.
]]></description>
<dc:creator>Braun, E.</dc:creator>
<dc:creator>Danan-Gotthold, M.</dc:creator>
<dc:creator>Borm, L. E.</dc:creator>
<dc:creator>Vinsland, E.</dc:creator>
<dc:creator>Lee, K. W.</dc:creator>
<dc:creator>Lönnerberg, P.</dc:creator>
<dc:creator>Hu, L.</dc:creator>
<dc:creator>Li, X.</dc:creator>
<dc:creator>He, X.</dc:creator>
<dc:creator>Andrusivova, Z.</dc:creator>
<dc:creator>Lundeberg, J.</dc:creator>
<dc:creator>Arenas, E.</dc:creator>
<dc:creator>Barker, R. A.</dc:creator>
<dc:creator>Sundström, E.</dc:creator>
<dc:creator>Linnarsson, S.</dc:creator>
<dc:date>2022-10-25</dc:date>
<dc:identifier>doi:10.1101/2022.10.24.513487</dc:identifier>
<dc:title><![CDATA[Comprehensive cell atlas of the first-trimester developing human brain]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-10-25</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.10.02.508471v1?rss=1">
<title>
<![CDATA[
TACCO: Unified annotation transfer and decomposition of cell identities for single-cell and spatial omics 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.10.02.508471v1?rss=1"
</link>
<description><![CDATA[
Rapid advances in single-cell-, spatial-, and multi-omics, allow us to profile cellular ecosystems in tissues at unprecedented resolution, scale, and depth. However, both technical limitations, such as low spatial resolution and biological variations, such as continuous spectra of cell states, often render these data imperfect representations of cellular systems, best captured as continuous mixtures over cells or molecules. Based on this conceptual insight, we build a versatile framework, TACCO (Transfer of Annotations to Cells and their COmbinations) that extends an Optimal Transport-based core by different wrappers or boosters to annotate a wide variety of data. We apply TACCO to identify cell types and states, decipher spatio-molecular tissue structure at the cell and molecular level, and resolve differentiation trajectories. TACCO excels in speed, scalability, and adaptability, while successfully outperforming benchmarks across diverse synthetic and biological datasets. Along with highly optimized visualization and analysis functions, TACCO forms a comprehensive integrated framework for studies of high-dimensional, high-resolution biology.
]]></description>
<dc:creator>Mages, S.</dc:creator>
<dc:creator>Moriel, N.</dc:creator>
<dc:creator>Avraham-Davidi, I.</dc:creator>
<dc:creator>Murray, E.</dc:creator>
<dc:creator>Chen, F.</dc:creator>
<dc:creator>Rozenblatt-Rosen, O.</dc:creator>
<dc:creator>Klughammer, J.</dc:creator>
<dc:creator>Regev, A.</dc:creator>
<dc:creator>Nitzan, M.</dc:creator>
<dc:date>2022-10-05</dc:date>
<dc:identifier>doi:10.1101/2022.10.02.508471</dc:identifier>
<dc:title><![CDATA[TACCO: Unified annotation transfer and decomposition of cell identities for single-cell and spatial omics]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-10-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.01.16.524211v1?rss=1">
<title>
<![CDATA[
The emergence of goblet inflammatory or ITGB6hi nasal progenitor cells determines age-associated SARS-CoV-2 pathogenesis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.01.16.524211v1?rss=1"
</link>
<description><![CDATA[
Children infected with SARS-CoV-2 rarely progress to respiratory failure, but the risk of mortality in infected people over 85 years of age remains high, despite vaccination and improving treatment options. Here, we take a comprehensive, multidisciplinary approach to investigate differences in the cellular landscape and function of paediatric (<11y), adult (30- 50y) and elderly (>70y) nasal epithelial cells experimentally infected with SARS-CoV-2. Our data reveal that nasal epithelial cell subtypes show different tropism to SARS-CoV-2, correlating with age, ACE2 and TMPRSS2 expression. Ciliated cells are a viral replication centre across all age groups, but a distinct goblet inflammatory subtype emerges in infected paediatric cultures, identifiable by high expression of interferon stimulated genes and truncated viral genomes. In contrast, infected elderly cultures show a proportional increase in ITGB6hi progenitors, which facilitate viral spread and are associated with dysfunctional epithelial repair pathways.

Graphical Abstract

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=155 SRC="FIGDIR/small/524211v2_ufig1.gif" ALT="Figure 1">
View larger version (61K):
org.highwire.dtl.DTLVardef@dab12aorg.highwire.dtl.DTLVardef@1a57334org.highwire.dtl.DTLVardef@12e7983org.highwire.dtl.DTLVardef@2bbe6e_HPS_FORMAT_FIGEXP  M_FIG C_FIG
]]></description>
<dc:creator>Woodall, M.</dc:creator>
<dc:creator>Cujba, A.-M.</dc:creator>
<dc:creator>Worlock, K. B.</dc:creator>
<dc:creator>Case, K.-M.</dc:creator>
<dc:creator>Masonou, T.</dc:creator>
<dc:creator>Yoshida, M.</dc:creator>
<dc:creator>Polanski, K.</dc:creator>
<dc:creator>Huang, N.</dc:creator>
<dc:creator>Lindeboom, R. G.</dc:creator>
<dc:creator>Mamanova, L.</dc:creator>
<dc:creator>Bolt, L.</dc:creator>
<dc:creator>Richardson, L.</dc:creator>
<dc:creator>Ellis, S.</dc:creator>
<dc:creator>Palor, M.</dc:creator>
<dc:creator>Burgoyne, T.</dc:creator>
<dc:creator>Pinto, A.</dc:creator>
<dc:creator>Moulding, D. A.</dc:creator>
<dc:creator>McHugh, T. D.</dc:creator>
<dc:creator>Saleh, A.</dc:creator>
<dc:creator>Kilich, E.</dc:creator>
<dc:creator>Mehta, P.</dc:creator>
<dc:creator>O'Callaghan, C.</dc:creator>
<dc:creator>Zhou, J.</dc:creator>
<dc:creator>Barclay, W.</dc:creator>
<dc:creator>De Coppi, P.</dc:creator>
<dc:creator>Butler, C. R.</dc:creator>
<dc:creator>Vinette, H.</dc:creator>
<dc:creator>Roy, S.</dc:creator>
<dc:creator>Breuer, J.</dc:creator>
<dc:creator>Chambers, R. C.</dc:creator>
<dc:creator>Heywood, W. E.</dc:creator>
<dc:creator>Mills, K.</dc:creator>
<dc:creator>Hynds, R. E.</dc:creator>
<dc:creator>Teichmann, S. A.</dc:creator>
<dc:creator>Meyer, K. B.</dc:creator>
<dc:creator>Nikolic, M. Z.</dc:creator>
<dc:creator>Smith, C. M.</dc:creator>
<dc:date>2023-01-17</dc:date>
<dc:identifier>doi:10.1101/2023.01.16.524211</dc:identifier>
<dc:title><![CDATA[The emergence of goblet inflammatory or ITGB6hi nasal progenitor cells determines age-associated SARS-CoV-2 pathogenesis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-01-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.03.08.531713v1?rss=1">
<title>
<![CDATA[
Gene-level alignment of single cell trajectories informs the progression of in vitro T cell differentiation 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.03.08.531713v1?rss=1"
</link>
<description><![CDATA[
Single-cell data analysis can infer dynamic changes in cell populations, for example across time, space or in response to perturbation. To compare these dynamics between two conditions, trajectory alignment via dynamic programming (DP) optimization is frequently used, but is limited by assumptions such as a definite existence of a match. Here we describe Genes2Genes, a Bayesian information-theoretic DP framework for aligning single-cell trajectories. Genes2Genes overcomes current limitations and is able to capture sequential matches and mismatches between a reference and a query at single gene resolution, highlighting distinct clusters of genes with varying patterns of expression dynamics. Across both real world and simulated datasets, Genes2Genes accurately captured different alignment patterns, demonstrated its utility in disease cell state trajectory analysis, and revealed that T cells differentiated in vitro matched to an immature in vivo state while lacking expression of genes associated with TNF[a] signaling. This use case demonstrates that precise trajectory alignment can pinpoint divergence from the in vivo system, thus guiding the optimization of in vitro culture conditions.
]]></description>
<dc:creator>Sumanaweera, D.</dc:creator>
<dc:creator>Suo, C.</dc:creator>
<dc:creator>Muraro, D.</dc:creator>
<dc:creator>Dann, E.</dc:creator>
<dc:creator>Polanski, K.</dc:creator>
<dc:creator>Steemers, A.</dc:creator>
<dc:creator>Park, J.-E.</dc:creator>
<dc:creator>Dumitrascu, B.</dc:creator>
<dc:creator>Teichmann, S.</dc:creator>
<dc:date>2023-03-10</dc:date>
<dc:identifier>doi:10.1101/2023.03.08.531713</dc:identifier>
<dc:title><![CDATA[Gene-level alignment of single cell trajectories informs the progression of in vitro T cell differentiation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-03-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.11.30.470655v1?rss=1">
<title>
<![CDATA[
Raman2RNA: Live-cell label-free prediction of single-cell RNA expression profiles by Raman microscopy 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.11.30.470655v1?rss=1"
</link>
<description><![CDATA[
Single cell RNA-Seq (scRNA-seq) and other profiling assays have opened new windows into understanding the properties, regulation, dynamics, and function of cells at unprecedented resolution and scale. However, these assays are inherently destructive, precluding us from tracking the temporal dynamics of live cells, in cell culture or whole organisms. Raman microscopy offers a unique opportunity to comprehensively report on the vibrational energy levels of molecules in a label-free and non-destructive manner at a subcellular spatial resolution, but it lacks in genetic and molecular interpretability. Here, we developed Raman2RNA (R2R), an experimental and computational framework to infer single-cell expression profiles in live cells through label-free hyperspectral Raman microscopy images and multi-modal data integration and domain translation. We used spatially resolved single-molecule RNA-FISH (smFISH) data as anchors to link scRNA-seq profiles to the paired spatial hyperspectral Raman images, and trained machine learning models to infer expression profiles from Raman spectra at the single-cell level. In reprogramming of mouse fibroblasts into induced pluripotent stem cells (iPSCs), R2R accurately (r>0.96) inferred from Raman images the expression profiles of various cell states and fates, including iPSCs, mesenchymal-epithelial transition (MET) cells, stromal cells, epithelial cells, and fibroblasts. R2R outperformed inference from brightfield images, showing the importance of spectroscopic content afforded by Raman microscopy. Raman2RNA lays a foundation for future investigations into exploring single-cell genome-wide molecular dynamics through imaging data, in vitro and in vivo.
]]></description>
<dc:creator>Kobayashi-Kirschvink, K. J.</dc:creator>
<dc:creator>Gaddam, S.</dc:creator>
<dc:creator>James-Sorenson, T.</dc:creator>
<dc:creator>Grody, E.</dc:creator>
<dc:creator>Ounadjela, J. R.</dc:creator>
<dc:creator>Ge, B.</dc:creator>
<dc:creator>Zhang, K.</dc:creator>
<dc:creator>Kang, J. W.</dc:creator>
<dc:creator>Xavier, R.</dc:creator>
<dc:creator>So, P. T. C.</dc:creator>
<dc:creator>Biancalani, T.</dc:creator>
<dc:creator>Shu, J.</dc:creator>
<dc:creator>Regev, A.</dc:creator>
<dc:date>2021-12-01</dc:date>
<dc:identifier>doi:10.1101/2021.11.30.470655</dc:identifier>
<dc:title><![CDATA[Raman2RNA: Live-cell label-free prediction of single-cell RNA expression profiles by Raman microscopy]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-12-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.11.06.515326v1?rss=1">
<title>
<![CDATA[
Spatially resolved single-cell multiomics map of human trophoblast differentiation in early pregnancy 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.11.06.515326v1?rss=1"
</link>
<description><![CDATA[
The relationship between the human placenta, the extraembryonic organ built by the fetus, and the decidua, the mucosal layer of the uterus, is essential to nurture and protect the fetus during pregnancy. Extravillous trophoblast cells (EVTs) anchor the placenta and infiltrate the decidua, transforming the maternal arteries into high conductance vessels. Defects in trophoblast invasion and arterial transformation established during early pregnancy underlie common pregnancy disorders such as pre-eclampsia. Despite its importance, how EVT invasion is regulated in humans is still unclear due the inaccessibility of the entire pregnant uterus and, until recently, a lack of reliable in vitro models. Here, we have generated a spatially-resolved multiomics single-cell atlas of the entire maternal-fetal interface including the myometrium, allowing us to resolve the full trajectory of trophoblast differentiation. We have used this cellular map to elucidate the main regulatory programmes mediating EVT invasion and show that they are preserved in trophoblast organoids. We define the transcriptomes of the final cell states of trophoblast invasion: placental bed giant cells (fused multinucleated EVTs) and endovascular EVTs (which form plugs inside the maternal arteries). We reconstruct the cell-cell communication events contributing to trophoblast invasion and GC formation, and define the dual role of interstitial EVTs and endovascular EVTs in mediating arterial transformation during early pregnancy. Together, our data provides a comprehensive analysis of postimplantation trophoblast differentiation in humans that can be used as a blueprint to design accurate multilineage placental in vitro models.
]]></description>
<dc:creator>Arutyunyan, A.</dc:creator>
<dc:creator>Roberts, K.</dc:creator>
<dc:creator>Sheridan, M. A.</dc:creator>
<dc:creator>Kats, I.</dc:creator>
<dc:creator>Garcia-Alonso, L.</dc:creator>
<dc:creator>Velten, B.</dc:creator>
<dc:creator>Troule-Lozano, K.</dc:creator>
<dc:creator>Hoo, R.</dc:creator>
<dc:creator>Marconato, L.</dc:creator>
<dc:creator>Handfield, L.-F.</dc:creator>
<dc:creator>Tuck, L.</dc:creator>
<dc:creator>Gardner, L.</dc:creator>
<dc:creator>Mazzeo, C. I.</dc:creator>
<dc:creator>Kelava, I.</dc:creator>
<dc:creator>Prigmore, E.</dc:creator>
<dc:creator>Teichmann, S.</dc:creator>
<dc:creator>Bayraktar, O.</dc:creator>
<dc:creator>Moffett, A.</dc:creator>
<dc:creator>Stegle, O.</dc:creator>
<dc:creator>Turco, M. Y.</dc:creator>
<dc:creator>Vento-Tormo, R.</dc:creator>
<dc:date>2022-11-06</dc:date>
<dc:identifier>doi:10.1101/2022.11.06.515326</dc:identifier>
<dc:title><![CDATA[Spatially resolved single-cell multiomics map of human trophoblast differentiation in early pregnancy]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-11-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.03.10.483747v1?rss=1">
<title>
<![CDATA[
An integrated cell atlas of the human lung in health and disease 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.03.10.483747v1?rss=1"
</link>
<description><![CDATA[
Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. The growth in both size and number of single-cell datasets, combined with recent advances in computational techniques, for the first time makes it possible to generate such comprehensive large-scale atlases through integration of multiple datasets. Here, we present the integrated Human Lung Cell Atlas (HLCA) combining 46 datasets of the human respiratory system into a single atlas spanning over 2.2 million cells from 444 individuals across health and disease. The HLCA contains a consensus re-annotation of published and newly generated datasets, resolving under- or misannotation of 59% of cells in the original datasets. The HLCA enables recovery of rare cell types, provides consensus marker genes for each cell type, and uncovers gene modules associated with demographic covariates and anatomical location within the respiratory system. To facilitate the use of the HLCA as a reference for single-cell lung research and allow rapid analysis of new data, we provide an interactive web portal to project datasets onto the HLCA. Finally, we demonstrate the value of the HLCA reference for interpreting disease-associated changes. Thus, the HLCA outlines a roadmap for the development and use of organ-scale cell atlases within the Human Cell Atlas.
]]></description>
<dc:creator>Sikkema, L.</dc:creator>
<dc:creator>Strobl, D. C.</dc:creator>
<dc:creator>Zappia, L.</dc:creator>
<dc:creator>Madissoon, E.</dc:creator>
<dc:creator>Markov, N. S.</dc:creator>
<dc:creator>Zaragosi, L.-E.</dc:creator>
<dc:creator>Ansari, M.</dc:creator>
<dc:creator>Arguel, M.-J.</dc:creator>
<dc:creator>Apperloo, L.</dc:creator>
<dc:creator>Becavin, C.</dc:creator>
<dc:creator>Berg, M.</dc:creator>
<dc:creator>Chichelnitskiy, E.</dc:creator>
<dc:creator>Chung, M.-i.</dc:creator>
<dc:creator>Collin, A.</dc:creator>
<dc:creator>Gay, A. C.</dc:creator>
<dc:creator>Hooshiar Kashani, B.</dc:creator>
<dc:creator>Jain, M.</dc:creator>
<dc:creator>Kapellos, T.</dc:creator>
<dc:creator>Kole, T. M.</dc:creator>
<dc:creator>Mayr, C. H.</dc:creator>
<dc:creator>Papen, von, M.</dc:creator>
<dc:creator>Peter, L.</dc:creator>
<dc:creator>Ramirez-Suastegui, C.</dc:creator>
<dc:creator>Schniering, J.</dc:creator>
<dc:creator>Taylor, C. J.</dc:creator>
<dc:creator>Walzthoeni, T.</dc:creator>
<dc:creator>Xu, C.</dc:creator>
<dc:creator>Bui, L. T.</dc:creator>
<dc:creator>Donno, de, C.</dc:creator>
<dc:creator>Dony, L.</dc:creator>
<dc:creator>Guo, M.</dc:creator>
<dc:creator>Gutierrez, A. J.</dc:creator>
<dc:creator>Heumos, L.</dc:creator>
<dc:creator>Huang, N.</dc:creator>
<dc:creator>Ibarra, I. L.</dc:creator>
<dc:creator>Jackson, N. D.</dc:creator>
<dc:creator>Kadur Lakshminarasimha Murthy, P.</dc:creator>
<dc:creator>Lotfollahi, M.</dc:creator>
<dc:creator>Tabib, T.</dc:creator>
<dc:creator>Talavera-Lopez, C.</dc:creator>
<dc:creator>Travaglini, K.</dc:creator>
<dc:date>2022-03-11</dc:date>
<dc:identifier>doi:10.1101/2022.03.10.483747</dc:identifier>
<dc:title><![CDATA[An integrated cell atlas of the human lung in health and disease]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-03-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.01.30.526202v1?rss=1">
<title>
<![CDATA[
Spatially resolved multiomics of human cardiac niches 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.01.30.526202v1?rss=1"
</link>
<description><![CDATA[
A cells function is defined by its intrinsic characteristics and its niche: the tissue microenvironment in which it dwells. Here, we combine single-cell and spatial transcriptomic data to discover cellular niches within eight regions of the human heart. We map cells to micro-anatomic locations and integrate knowledge-based and unsupervised structural annotations. For the first time, we profile the cells of the human cardiac conduction system, revealing their distinctive repertoire of ion channels, G-protein coupled receptors, and cell interactions using a custom CellPhoneDB.org module. We show that the sinoatrial node is compartmentalised, with a core of pacemaker cells, fibroblasts and glial cells supporting paracrine glutamatergic signalling. We introduce a druggable target prediction tool, drug2cell, which leverages single-cell profiles and drug-target interactions, providing unexpected mechanistic insights into the chronotropic effects of drugs, including GLP-1 analogues. In the epicardium, we show enrichment of both IgG+ and IgA+ plasma cells forming immune niches which may contribute to infection defence. We define a ventricular myocardial-stress niche enriched for activated fibroblasts and stressed cardiomyocytes, cell states that are expanded in cardiomyopathies. Overall, we provide new clarity to cardiac electro-anatomy and immunology, and our suite of computational approaches can be deployed to other tissues and organs.
]]></description>
<dc:creator>Kanemaru, K.</dc:creator>
<dc:creator>Cranley, J.</dc:creator>
<dc:creator>Muraro, D.</dc:creator>
<dc:creator>Miranda, A. M. A.</dc:creator>
<dc:creator>Pett, J. P.</dc:creator>
<dc:creator>Litvinukova, M.</dc:creator>
<dc:creator>Kumasaka, N.</dc:creator>
<dc:creator>Ho, S. Y.</dc:creator>
<dc:creator>Polanski, K.</dc:creator>
<dc:creator>Richardson, L.</dc:creator>
<dc:creator>Mach, L.</dc:creator>
<dc:creator>Dabrowska, M.</dc:creator>
<dc:creator>Richoz, N.</dc:creator>
<dc:creator>Barnett, S. N.</dc:creator>
<dc:creator>Perera, S.</dc:creator>
<dc:creator>Wilbrey-Clark, A. L.</dc:creator>
<dc:creator>Talavera-Lopez, C.</dc:creator>
<dc:creator>Mulas, I.</dc:creator>
<dc:creator>Mahbubani, K. T.</dc:creator>
<dc:creator>Bolt, L.</dc:creator>
<dc:creator>Mamanova, L.</dc:creator>
<dc:creator>Tuck, L.</dc:creator>
<dc:creator>Wang, L.</dc:creator>
<dc:creator>Huang, M. M.</dc:creator>
<dc:creator>Prete, M.</dc:creator>
<dc:creator>Pritchard, S.</dc:creator>
<dc:creator>Dark, J.</dc:creator>
<dc:creator>Saeb-Parsy, K.</dc:creator>
<dc:creator>Patel, M.</dc:creator>
<dc:creator>Clatworthy, M. R.</dc:creator>
<dc:creator>Chowdhury, R. A.</dc:creator>
<dc:creator>Noseda, M.</dc:creator>
<dc:creator>Teichmann, S. A.</dc:creator>
<dc:date>2023-02-01</dc:date>
<dc:identifier>doi:10.1101/2023.01.30.526202</dc:identifier>
<dc:title><![CDATA[Spatially resolved multiomics of human cardiac niches]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-02-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.04.21.537845v1?rss=1">
<title>
<![CDATA[
A Human Breast Cell Atlas Mapping the Homeostatic Cellular Shifts in the Adult Breast 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.04.21.537845v1?rss=1"
</link>
<description><![CDATA[
One of the barriers for breast cancer prevention and treatment is our poor understanding of the dynamic cellular shifts that naturally occur within the breast and how these changes contribute to tumour initiation. In this study we report the use of single cell RNA sequencing (scRNAseq) to compile a Human Breast Cell Atlas (HBCA) assembled from 55 donors that had undergone reduction mammoplasties or risk reduction mammoplasties. The data from more than 800,000 cells identified 41 cell subclusters distributed across the epithelial, immune, and stromal compartments. We found that the contribution of these different clusters varied according to the natural history of the tissue. Breast cancer risk modulating factors such as age, parity, and germline mutation affected the homeostatic cellular state of the breast in different ways however, none of the changes observed were restricted to any one cell type. Remarkably, we also found that immune cells from BRCA1/2 carriers had a distinct gene expression signature indicative of potential immune exhaustion. This suggests that immune escape mechanisms could manifest in non-cancerous tissues during very early stages of tumour initiation. Therefore, the Atlas presented here provides the research community with a rich resource that can be used as a reference for studies on the origins of breast cancer which could inform novel approaches for early detection and prevention.
]]></description>
<dc:creator>Khaled, W. T.</dc:creator>
<dc:creator>Reed, A. D.</dc:creator>
<dc:creator>Pensa, S.</dc:creator>
<dc:creator>Steif, A.</dc:creator>
<dc:creator>Stenning, J.</dc:creator>
<dc:creator>Kunz, D.</dc:creator>
<dc:creator>He, P.</dc:creator>
<dc:creator>Twigger, A.-J.</dc:creator>
<dc:creator>Kania, K.</dc:creator>
<dc:creator>Barrow, R.</dc:creator>
<dc:creator>Goulding, I.</dc:creator>
<dc:creator>Gomm, J.</dc:creator>
<dc:creator>Jones, L.</dc:creator>
<dc:creator>Marioni, J.</dc:creator>
<dc:date>2023-04-21</dc:date>
<dc:identifier>doi:10.1101/2023.04.21.537845</dc:identifier>
<dc:title><![CDATA[A Human Breast Cell Atlas Mapping the Homeostatic Cellular Shifts in the Adult Breast]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-04-21</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.04.22.537946v1?rss=1">
<title>
<![CDATA[
A spatially resolved single cell genomic atlas of the adult human breast 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.04.22.537946v1?rss=1"
</link>
<description><![CDATA[
The adult human breast comprises an intricate network of epithelial ducts and lobules that are embedded in connective and adipose tissue. While previous studies have mainly focused on the breast epithelial system, many of the non-epithelial cell types remain understudied. Here, we constructed a comprehensive Human Breast Cell Atlas (HBCA) at single-cell and spatial resolution. Our single-cell transcriptomics data profiled 535,941 cells from 62 women, and 120,024 nuclei from 20 women, identifying 11 major cell types and 53 cell states. These data revealed abundant pericyte, endothelial and immune cell populations, and highly diverse luminal epithelial cell states. Our spatial mapping using three technologies revealed an unexpectedly rich ecosystem of tissue-resident immune cells in the ducts and lobules, as well as distinct molecular differences between ductal and lobular regions. Collectively, these data provide an unprecedented reference of adult normal breast tissue for studying mammary biology and disease states such as breast cancer.
]]></description>
<dc:creator>Kumar, T.</dc:creator>
<dc:creator>Nee, K.</dc:creator>
<dc:creator>Wei, R.</dc:creator>
<dc:creator>He, S.</dc:creator>
<dc:creator>Nguyen, Q.</dc:creator>
<dc:creator>Bai, S.</dc:creator>
<dc:creator>Blake, K.</dc:creator>
<dc:creator>Pein, M.</dc:creator>
<dc:creator>Gong, Y.</dc:creator>
<dc:creator>Sei, E.</dc:creator>
<dc:creator>Hu, M.</dc:creator>
<dc:creator>Casasent, A.</dc:creator>
<dc:creator>Thennavan, A.</dc:creator>
<dc:creator>Li, J.</dc:creator>
<dc:creator>Tran, T.</dc:creator>
<dc:creator>Chen, K.</dc:creator>
<dc:creator>Nilges, B.</dc:creator>
<dc:creator>Kashikar, N.</dc:creator>
<dc:creator>Braubach, O.</dc:creator>
<dc:creator>Cheikh, B.</dc:creator>
<dc:creator>Nikulina, N.</dc:creator>
<dc:creator>Chen, H.</dc:creator>
<dc:creator>Teshome, M.</dc:creator>
<dc:creator>Menegaz, B.</dc:creator>
<dc:creator>Javaid, H.</dc:creator>
<dc:creator>Nagi, C.</dc:creator>
<dc:creator>Montalvan, J.</dc:creator>
<dc:creator>Lev, T.</dc:creator>
<dc:creator>Tifrea, D.</dc:creator>
<dc:creator>Edwards, R.</dc:creator>
<dc:creator>Lin, E.</dc:creator>
<dc:creator>Parajuli, R.</dc:creator>
<dc:creator>Hanson, S.</dc:creator>
<dc:creator>Winocour, S.</dc:creator>
<dc:creator>Thompson, A.</dc:creator>
<dc:creator>Lim, B.</dc:creator>
<dc:creator>Lawson, D.</dc:creator>
<dc:creator>Kessenbrock, K.</dc:creator>
<dc:creator>Navin, N.</dc:creator>
<dc:date>2023-04-24</dc:date>
<dc:identifier>doi:10.1101/2023.04.22.537946</dc:identifier>
<dc:title><![CDATA[A spatially resolved single cell genomic atlas of the adult human breast]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-04-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.07.28.454201v1?rss=1">
<title>
<![CDATA[
An atlas of healthy and injured cell states and niches in the human kidney 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.07.28.454201v1?rss=1"
</link>
<description><![CDATA[
Understanding kidney disease relies upon defining the complexity of cell types and states, their associated molecular profiles, and interactions within tissue neighborhoods. We have applied multiple single-cell or -nucleus assays (>400,000 nuclei/cells) and spatial imaging technologies to a broad spectrum of healthy reference (n = 42) and disease (n = 42) kidneys. This has provided a high resolution cellular atlas of 100 cell types that include rare and novel cell populations. The multi-omic approach provides detailed transcriptomic profiles, epigenomic regulatory factors, and spatial localizations for major cell types spanning the entire kidney. We further identify and define cellular states altered in kidney injury, encompassing cycling, adaptive or maladaptive repair, transitioning and degenerative states affecting several segments. Molecular signatures of these states permitted their localization within injury neighborhoods using spatial transcriptomics, and large-scale 3D imaging analysis of [~]1.2 million neighborhoods provided linkages to active immune responses. These analyses further defined biological pathways relevant to injury niches, including signatures underlying the transition from reference to predicted maladaptive states that were associated with a decline in kidney function during chronic kidney disease. This human kidney cell atlas, including injury cell states and neighborhoods, will be a valuable resource for future studies.
]]></description>
<dc:creator>Lake, B. B.</dc:creator>
<dc:creator>Menon, R.</dc:creator>
<dc:creator>Winfree, S.</dc:creator>
<dc:creator>Hu, Q.</dc:creator>
<dc:creator>Ferreira, R. M.</dc:creator>
<dc:creator>Kalhor, K.</dc:creator>
<dc:creator>Barwinska, D.</dc:creator>
<dc:creator>Otto, E. A.</dc:creator>
<dc:creator>Ferkowicz, M.</dc:creator>
<dc:creator>Diep, D.</dc:creator>
<dc:creator>Plongthongkum, N.</dc:creator>
<dc:creator>Knoten, A.</dc:creator>
<dc:creator>Urata, S.</dc:creator>
<dc:creator>Naik, A. S.</dc:creator>
<dc:creator>Eddy, S.</dc:creator>
<dc:creator>Zhang, B.</dc:creator>
<dc:creator>Wu, Y.</dc:creator>
<dc:creator>Salamon, D.</dc:creator>
<dc:creator>Williams, J. C.</dc:creator>
<dc:creator>Wang, X.</dc:creator>
<dc:creator>Balderrama, K. S.</dc:creator>
<dc:creator>Hoover, P.</dc:creator>
<dc:creator>Murray, E.</dc:creator>
<dc:creator>Vijayan, A.</dc:creator>
<dc:creator>Chen, F.</dc:creator>
<dc:creator>Waikar, S. S.</dc:creator>
<dc:creator>Rosas, S.</dc:creator>
<dc:creator>Wilson, F. P.</dc:creator>
<dc:creator>Palevsky, P. M.</dc:creator>
<dc:creator>Kiryluk, K.</dc:creator>
<dc:creator>Sedor, J. R.</dc:creator>
<dc:creator>Toto, R. D.</dc:creator>
<dc:creator>Parikh, C.</dc:creator>
<dc:creator>Kim, E. H.</dc:creator>
<dc:creator>Macosko, E. Z.</dc:creator>
<dc:creator>Kharchenko, P. V.</dc:creator>
<dc:creator>Gaut, J. P.</dc:creator>
<dc:creator>Hodgin, J. B.</dc:creator>
<dc:creator>Eadon, M. T.</dc:creator>
<dc:creator>Dagher, P. C.</dc:creator>
<dc:creator>El-Achkar, T. M.</dc:creator>
<dc:creator>Zhang, K.</dc:creator>
<dc:creator>Kretzler, M.</dc:creator>
<dc:creator>Jain, S.</dc:creator>
<dc:creator>KPMP Consortium,</dc:creator>
<dc:date>2021-07-29</dc:date>
<dc:identifier>doi:10.1101/2021.07.28.454201</dc:identifier>
<dc:title><![CDATA[An atlas of healthy and injured cell states and niches in the human kidney]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-07-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.07.19.549507v1?rss=1">
<title>
<![CDATA[
Deciphering the spatio-temporal transcriptional and chromatin accessibility of human retinal organoid development at the single cell level 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.07.19.549507v1?rss=1"
</link>
<description><![CDATA[
Molecular information on the early stages of human retinal development remains scarce due to limitations in obtaining early human eye samples. Pluripotent stem cell-derived retinal organoids provide an unprecedented opportunity for studying early retinogenesis. Using a combination of single cell RNA-Seq and spatial transcriptomics we present for the first-time a single cell spatio-temporal transcriptome of retinal organoid development. Our data demonstrate that retinal organoids recapitulate key events of retinogenesis including optic vesicle/cup formation, formation of a putative ciliary margin zone, emergence of retinal progenitor cells and their orderly differentiation to retinal neurons. Combining the scRNA-with scATAC-Seq data, we were able to reveal cell-type specific transcription factor binding motifs on accessible chromatin at each stage of organoid development and to show that chromatin accessibility is highly correlated to the developing human retina, but with some differences in the temporal emergence and abundance of some of the retinal neurons.
]]></description>
<dc:creator>Dorgau, B.</dc:creator>
<dc:creator>Collin, J.</dc:creator>
<dc:creator>Rozanska, A.</dc:creator>
<dc:creator>Boczonadi, V.</dc:creator>
<dc:creator>MoyaMolina, M.</dc:creator>
<dc:creator>Hussain, R.</dc:creator>
<dc:creator>Coxhead, J.</dc:creator>
<dc:creator>Dhanaseelan, T.</dc:creator>
<dc:creator>Armstrong, L.</dc:creator>
<dc:creator>Queen, R.</dc:creator>
<dc:creator>Lako, M.</dc:creator>
<dc:date>2023-07-19</dc:date>
<dc:identifier>doi:10.1101/2023.07.19.549507</dc:identifier>
<dc:title><![CDATA[Deciphering the spatio-temporal transcriptional and chromatin accessibility of human retinal organoid development at the single cell level]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-07-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.03.21.533680v1?rss=1">
<title>
<![CDATA[
Inference of single cell profiles from histology stains with the Single-Cell omics from Histology Analysis Framework (SCHAF) 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.03.21.533680v1?rss=1"
</link>
<description><![CDATA[
Tissue biology involves an intricate balance between cell-intrinsic processes and interactions between cells organized in specific spatial patterns, which can be respectively captured by single cell profiling methods, such as single cell RNA-seq (scRNA-seq) and spatial transcriptomics, and histology imaging data, such as Hematoxylin-and-Eosin (H&E) stains. While single cell profiles provide rich molecular information, they can be challenging to collect routinely in the clinic and either lack spatial resolution or high gene throughput. Conversely, histological H&E assays have been a cornerstone of tissue pathology for decades, but do not directly report on molecular details, although the observed structure they capture arises from molecules and cells. Here, we leverage vision transformers and adversarial deep learning to develop the Single Cell omics from Histology Analysis Framework (SCHAF), which generates a tissue samples spatially-resolved whole transcriptome single cell omics dataset from its H&E histology image. We demonstrate SCHAF on a variety of tissues-- including lung cancer, metastatic breast cancer, placentae, and whole mouse pups--training with matched samples analyzed by sc/snRNA-seq, H&E staining, and, when available, spatial transcriptomics. SCHAF generated appropriate single cell profiles from histology images in test data, related them spatially, and compared well to ground-truth scRNA-Seq, expert pathologist annotations, or direct spatial transcriptomic measurements, with some limitations. SCHAF opens the way to next-generation H&E analyses and an integrated understanding of cell and tissue biology in health and disease.
]]></description>
<dc:creator>Comiter, C.</dc:creator>
<dc:creator>Vaishnav, E. D.</dc:creator>
<dc:creator>Ciapmricotti, M.</dc:creator>
<dc:creator>Li, B.</dc:creator>
<dc:creator>Yang, Y.</dc:creator>
<dc:creator>Rodig, S. J.</dc:creator>
<dc:creator>Turner, M.</dc:creator>
<dc:creator>Pfaff, K. L.</dc:creator>
<dc:creator>Jane-Valbuena, J.</dc:creator>
<dc:creator>Slyper, M.</dc:creator>
<dc:creator>Waldman, J.</dc:creator>
<dc:creator>Vigneau, S.</dc:creator>
<dc:creator>Wu, J.</dc:creator>
<dc:creator>Blosser, T. R.</dc:creator>
<dc:creator>Segerstolpe, A.</dc:creator>
<dc:creator>Abravanel, D.</dc:creator>
<dc:creator>Wagle, N.</dc:creator>
<dc:creator>Zhuang, X.</dc:creator>
<dc:creator>Rudin, C. M.</dc:creator>
<dc:creator>Klughammer, J.</dc:creator>
<dc:creator>Rozenblatt-Rosen, O.</dc:creator>
<dc:creator>Kobayash-Kirschvink, K. J.</dc:creator>
<dc:creator>Shu, J.</dc:creator>
<dc:creator>Regev, A.</dc:creator>
<dc:date>2023-03-23</dc:date>
<dc:identifier>doi:10.1101/2023.03.21.533680</dc:identifier>
<dc:title><![CDATA[Inference of single cell profiles from histology stains with the Single-Cell omics from Histology Analysis Framework (SCHAF)]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-03-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.10.08.332080v1?rss=1">
<title>
<![CDATA[
Batch-Corrected Distance Mitigates Temporal and Spatial Variability for Clustering and Visualization of Single-Cell Gene Expression Data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.10.08.332080v1?rss=1"
</link>
<description><![CDATA[
Clustering and visualization are essential parts of single-cell gene expression data analysis. The Euclidean distance used in most distance-based methods is not optimal. Batch effect, i.e., the variability among samples gathered from different times, tissues, and patients, introduces large between-group distance and obscures the true identities of cells. To solve this problem, we introduce Batch-Corrected Distance (BCD), a metric using temporal/spatial locality of the batch effect to control for such factors. We validate BCD on a simulated data as well as applied it to a mouse retina development dataset and a lung dataset. We also found the utility of our approach in understanding the progression of the Coronavirus Disease 2019 (COVID-19). BCD achieves more accurate clusters and better visualizations than state-of-the-art batch correction methods on longitudinal datasets. BCD can be directly integrated with most clustering and visualization methods to enable more scientific findings.
]]></description>
<dc:creator>Liang, S.</dc:creator>
<dc:creator>Dou, J.</dc:creator>
<dc:creator>Iqbal, R.</dc:creator>
<dc:creator>Chen, K.</dc:creator>
<dc:date>2020-10-09</dc:date>
<dc:identifier>doi:10.1101/2020.10.08.332080</dc:identifier>
<dc:title><![CDATA[Batch-Corrected Distance Mitigates Temporal and Spatial Variability for Clustering and Visualization of Single-Cell Gene Expression Data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-10-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.06.16.544954v1?rss=1">
<title>
<![CDATA[
TooManyCellsInteractive: a visualization tool for dynamic exploration of single-cell data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.06.16.544954v1?rss=1"
</link>
<description><![CDATA[
As single-cell sequencing data sets grow in size, visualizations of large cellular populations become difficult to parse and require extensive processing to identify subpopulations of cells. Managing many of these charts is laborious for technical users and unintuitive for non-technical users. To address this issue, we developed TooManyCellsInteractive (TMCI), a browser-based JavaScript application for visualizing hierarchical cellular populations as an interactive radial tree. TMCI allows users to explore, filter, and manipulate hierarchical data structures through an intuitive interface while also enabling batch export of high-quality custom graphics. Here we describe the software architecture and illustrate how TMCI has identified unique survival pathways among drug-tolerant persister cells in a pan-cancer analysis. TMCI will help guide increasingly large data visualizations and facilitate multi-resolution data exploration in a user-friendly way.
]]></description>
<dc:creator>Klamann, C.</dc:creator>
<dc:creator>Lau, C.</dc:creator>
<dc:creator>Schwartz, G. W.</dc:creator>
<dc:date>2023-06-18</dc:date>
<dc:identifier>doi:10.1101/2023.06.16.544954</dc:identifier>
<dc:title><![CDATA[TooManyCellsInteractive: a visualization tool for dynamic exploration of single-cell data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-06-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.09.20.508736v1?rss=1">
<title>
<![CDATA[
Conserved coexpression at single cell resolution across primate brains 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.09.20.508736v1?rss=1"
</link>
<description><![CDATA[
Enhanced cognitive function in humans is hypothesized to result from cortical expansion and increased cellular diversity. However, the mechanisms that drive these phenotypic differences remain poorly understood, in part due to the lack of high-quality cellular resolution data in human and non-human primates. Here, we take advantage of single cell expression data from the middle temporal gyrus of five primates (human, chimp, gorilla, macaque and marmoset) to identify 57 homologous cell types and generate cell-type specific gene coexpression networks for comparative analysis. While ortholog expression patterns are generally well conserved, we find 24% of genes with extensive differences between human and non-human primates (3383/14,131), which are also associated with multiple brain disorders. To validate these observations, we perform a meta-analysis of coexpression networks across 19 animals, and find that a subset of these genes have deeply conserved coexpression across all non-human animals, and strongly divergent coexpression relationships in humans (139/3383, <1% of primate orthologs). Genes with human-specific cellular expression and coexpression networks (like NHEJ1, GTF2H2, C2 and BBS5) typically evolve under relaxed selective constraints and may drive rapid evolutionary change in brain function.

One Sentence SummaryCross-primate middle temporal gyrus single cell expression data reveals patterns of conservation and divergence that can be validated with population coexpression networks.
]]></description>
<dc:creator>Suresh, H.</dc:creator>
<dc:creator>Crow, M.</dc:creator>
<dc:creator>Jorstad, N.</dc:creator>
<dc:creator>Hodge, R.</dc:creator>
<dc:creator>Lein, E.</dc:creator>
<dc:creator>Dobin, A.</dc:creator>
<dc:creator>Bakken, T.</dc:creator>
<dc:creator>Gillis, J.</dc:creator>
<dc:date>2022-09-22</dc:date>
<dc:identifier>doi:10.1101/2022.09.20.508736</dc:identifier>
<dc:title><![CDATA[Conserved coexpression at single cell resolution across primate brains]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-09-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.07.25.550225v1?rss=1">
<title>
<![CDATA[
GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.07.25.550225v1?rss=1"
</link>
<description><![CDATA[
We introduce GRouNdGAN, a gene regulatory network (GRN)-guided causal implicit generative model for simulating single-cell RNA-seq data, in-silico perturbation experiments, and benchmarking GRN inference methods. Through the imposition of a user-defined GRN in its architecture, GRouNdGAN simulates steady-state and transient-state single-cell datasets where genes are causally expressed under the control of their regulating transcription factors (TFs). Training on three experimental datasets, we show that our model captures non-linear TF-gene dependences and preserves gene identities, cell trajectories, pseudo-time ordering, and technical and biological noise, with no user manipulation and only implicit parameterization. Despite imposing rigid causality constraints, it outperforms state-of-the-art simulators in generating realistic cells. GRouNdGAN learns meaningful causal regulatory dynamics, allowing sampling from both observational and interventional distributions. This enables it to synthesize cells under conditions that do not occur in the dataset at inference time, allowing to perform in-silico TF knockout experiments. Our results show that in-silico knockout of cell type-specific TFs significantly reduces cells of that type being generated. Interactions imposed through the GRN are emphasized in the simulated datasets, resulting in GRN inference algorithms assigning them much higher scores than interactions not imposed but of equal importance in the experimental training dataset. Benchmarking various GRN inference algorithms reveals that GRouNdGAN effectively bridges the existing gap between simulated and biological data benchmarks of GRN inference algorithms, providing gold standard ground truth GRNs and realistic cells corresponding to the biological system of interest. Our results show that GRouNdGAN is a stable, realistic, and effective simulator with various applications in single-cell RNA-seq analysis.
]]></description>
<dc:creator>Zinati, Y.</dc:creator>
<dc:creator>Takiddeen, A.</dc:creator>
<dc:creator>Emad, A.</dc:creator>
<dc:date>2023-07-27</dc:date>
<dc:identifier>doi:10.1101/2023.07.25.550225</dc:identifier>
<dc:title><![CDATA[GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-07-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.03.16.532356v1?rss=1">
<title>
<![CDATA[
Airway epithelial response to RSV is impaired in multiciliated and goblet cells in asthma 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.03.16.532356v1?rss=1"
</link>
<description><![CDATA[
In patients with asthma, respiratory syncytial virus (RSV) infections can cause disease exacerbations by infecting the epithelial layer of the airways, inducing an innate and adaptive immune response. The type-I interferon antiviral response of epithelial cells upon RSV infection is found to be reduced in asthma in most -but not all-studies. Moreover, the molecular mechanisms that cause the differences in the asthmatic bronchial epithelium in response to viral infection are poorly understood.

Here, we investigated the transcriptional response to RSV infection of primary bronchial epithelial cells (pBECs) from asthma patients(n=8) and healthy donors(n=8). The pBECs obtained from bronchial brushes were differentiated in air-liquid interface conditions and infected with RSV. After three days, cells were processed for single-cell RNA sequencing.

A strong antiviral response to RSV was observed for all cell types present, from both asthma patients and healthy donors. Most differentially regulated genes following RSV infection were found in cells transitioning from basal to secretory. Goblet cells from asthma patients showed lower expression of genes involved in the interferon response. In multiciliated cells, an impairment of the signaling pathways involved in the response to RSV in asthma was observed, including no enrichment of the type-III interferon response.

Our results highlight that the response to RSV infection of the bronchial epithelium in asthma and healthy airways was largely similar. However, in asthma, the response of goblet and the multiciliated cells was impaired, highlighting the need for studying airway epithelial cells at high resolution in the context of asthma exacerbations.

What is already know on this topicThe airway epithelium response to RSV is altered in asthma. However, literature remains conflicted about the exact changes in the antiviral response, and the mechanisms causing these changes are yet to be found.

What this study addsThis study describes extensively the response of the bronchial epithelial cells (BECs) to RSV for both healthy subjects and asthma patients, at a single-cell resolution. It highlights the major overlap between healthy and asthma in the antiviral response to RSV. It allows the identification of specific genes and cell types that show a different behavior in response to RSV in asthma compared to healthy.

How this study might affect research, practice or policyOur study indicates that goblet and multiciliated cells are the most relevant BECs to further investigate in the context of drug development for RSV-induced asthma exacerbation. It also suggests that focusing research on the cross-talk between the epithelial and the immune cells, or into investigating a potential delayed response in asthma would be the best way forward into understanding the mechanisms involved in the asthma response to RSV.
]]></description>
<dc:creator>Gay, A. C. A.</dc:creator>
<dc:creator>Banchero, M.</dc:creator>
<dc:creator>Carpaij, O. A.</dc:creator>
<dc:creator>Kole, T.</dc:creator>
<dc:creator>Apperloo, L.</dc:creator>
<dc:creator>van Gosliga, D.</dc:creator>
<dc:creator>Fajar, P. A.</dc:creator>
<dc:creator>Koppelman, G. H.</dc:creator>
<dc:creator>Bont, L.</dc:creator>
<dc:creator>Hendriks, R. W.</dc:creator>
<dc:creator>van den Berge, M.</dc:creator>
<dc:creator>Nawijn, M. C.</dc:creator>
<dc:date>2023-03-17</dc:date>
<dc:identifier>doi:10.1101/2023.03.16.532356</dc:identifier>
<dc:title><![CDATA[Airway epithelial response to RSV is impaired in multiciliated and goblet cells in asthma]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-03-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.08.03.502475v1?rss=1">
<title>
<![CDATA[
Multi-organ functions of yolk sac during human early development 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.08.03.502475v1?rss=1"
</link>
<description><![CDATA[
The yolk sac (YS) represents an evolutionarily-conserved extraembryonic structure that ensures timely delivery of nutritional support and oxygen to the developing embryo. However, the YS remains ill-defined in humans. We therefore assemble a complete single cell 3D map of human YS from 3-8 post conception weeks by integrating multiomic protein and gene expression data. We reveal the YS as a site of primitive and definitive haematopoiesis including a YS-specific accelerated route to macrophage production, a source of nutritional/metabolic support and a regulator of oxygen-carrying capacity. We reconstruct the emergence of primitive haematopoietic stem and progenitor cells from YS hemogenic endothelium and their decline upon stromal support modulation as intraembryonic organs specialise to assume these functions. The YS therefore functions as  three organs in one revealing a multifaceted relay of vital organismal functions as pregnancy proceeds.

One Sentence SummaryHuman yolk sac is a key staging post in a relay of vital organismal functions during human pregnancy.
]]></description>
<dc:creator>Botting, R. A.</dc:creator>
<dc:creator>Goh, I.</dc:creator>
<dc:creator>Rose, A.</dc:creator>
<dc:creator>Webb, S.</dc:creator>
<dc:creator>Engelbert, J.</dc:creator>
<dc:creator>Gitton, Y.</dc:creator>
<dc:creator>Stephenson, E.</dc:creator>
<dc:creator>Quiroga Londono, M.</dc:creator>
<dc:creator>Mather, M.</dc:creator>
<dc:creator>Mende, N.</dc:creator>
<dc:creator>Imaz-Rosshandler, I.</dc:creator>
<dc:creator>Horsfall, D.</dc:creator>
<dc:creator>Basurto-Lozada, D.</dc:creator>
<dc:creator>Chipampe, N.-J.</dc:creator>
<dc:creator>Rook, V.</dc:creator>
<dc:creator>Mazin, P.</dc:creator>
<dc:creator>Vijayabaskar, M.</dc:creator>
<dc:creator>Hannah, R.</dc:creator>
<dc:creator>Gambardella, L.</dc:creator>
<dc:creator>Green, K.</dc:creator>
<dc:creator>Ballereau, S.</dc:creator>
<dc:creator>Inoue, M.</dc:creator>
<dc:creator>Tuck, L.</dc:creator>
<dc:creator>Lorenzi, V.</dc:creator>
<dc:creator>Kwakwa, K.</dc:creator>
<dc:creator>Alsinet, C.</dc:creator>
<dc:creator>Olabi, B.</dc:creator>
<dc:creator>Miah, M.</dc:creator>
<dc:creator>Admane, C.</dc:creator>
<dc:creator>Popescu, D.-M.</dc:creator>
<dc:creator>Acres, M.</dc:creator>
<dc:creator>Dixon, D.</dc:creator>
<dc:creator>Coulthard, R.</dc:creator>
<dc:creator>Lisgo, S.</dc:creator>
<dc:creator>Henderson, D. J.</dc:creator>
<dc:creator>Dann, E.</dc:creator>
<dc:creator>Su, C.</dc:creator>
<dc:creator>Kinston, S. J.</dc:creator>
<dc:creator>Park, J.-e.</dc:creator>
<dc:creator>Polanski, K.</dc:creator>
<dc:creator>Van Dongen, S.</dc:creator>
<dc:creator>Meyer, K. B.</dc:creator>
<dc:creator>de Bruijn, M.</dc:creator>
<dc:creator>Palis, J.</dc:creator>
<dc:creator>Behjati, S</dc:creator>
<dc:date>2022-08-05</dc:date>
<dc:identifier>doi:10.1101/2022.08.03.502475</dc:identifier>
<dc:title><![CDATA[Multi-organ functions of yolk sac during human early development]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-08-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.12.04.519058v1?rss=1">
<title>
<![CDATA[
Monopogen: single nucleotide variant calling from single cell sequencing 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.12.04.519058v1?rss=1"
</link>
<description><![CDATA[
Distinguishing how genetics impact cellular processes can improve our understanding of variable risk for diseases. Although single-cell omics have provided molecular characterization of cell types and states on diverse tissue samples, their genetic ancestry and effects on cellular molecular traits are largely understudied. Here, we developed Monopogen, a computational tool enabling researchers to detect single nucleotide variants (SNVs) from a variety of single cell transcriptomic and epigenomic sequencing data. It leverages linkage disequilibrium from external reference panels to identify germline SNVs from sparse sequencing data and uses Monovar to identify novel SNVs at cluster (or cell type) levels. Monopogen can identify 100K~3M germline SNVs from various single cell sequencing platforms (scRNA-seq, snRNA-seq, snATAC-seq etc), with genotyping accuracy higher than 95%, when compared against matched whole genome sequencing data. We applied Monopogen on human retina, normal breast and Asian immune diversity atlases, showing that that derived genotypes enable accurate global and local ancestry inference and identification of admixed samples from ancestrally diverse donors. In addition, we applied Monopogen on ~4M cells from 65 human heart left ventricle single cell samples and identified novel variants associated with cardiomyocyte metabolic levels and epigenomic programs. In summary, Monopogen provides a novel computational framework that brings together population genetics and single cell omics to uncover genetic determinants of cellular quantitative traits.
]]></description>
<dc:creator>Dou, J.</dc:creator>
<dc:creator>Tan, Y.</dc:creator>
<dc:creator>Kock, k. H.</dc:creator>
<dc:creator>Wang, J.</dc:creator>
<dc:creator>Cheng, X.</dc:creator>
<dc:creator>Tan, L. M.</dc:creator>
<dc:creator>Han, K. Y.</dc:creator>
<dc:creator>Hon, C.-C.</dc:creator>
<dc:creator>Park, W. Y.</dc:creator>
<dc:creator>Shin, J. W.</dc:creator>
<dc:creator>Chen, H.</dc:creator>
<dc:creator>Prabhakar, S.</dc:creator>
<dc:creator>Navin, N.</dc:creator>
<dc:creator>Chen, R.</dc:creator>
<dc:creator>Chen, K.</dc:creator>
<dc:date>2022-12-07</dc:date>
<dc:identifier>doi:10.1101/2022.12.04.519058</dc:identifier>
<dc:title><![CDATA[Monopogen: single nucleotide variant calling from single cell sequencing]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-12-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.01.04.522727v1?rss=1">
<title>
<![CDATA[
A multi-tissue single-cell tumor microenvironment atlas reveals myeloid-derived cell states with significant impact on clinical outcome 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.01.04.522727v1?rss=1"
</link>
<description><![CDATA[
Tumor-associated myeloid-derived cells (MDCs) significantly impact cancer prognosis and treatment response due to their remarkable plasticity and tumorigenic behaviors. We integrated single-cell RNA-Sequencing datasets from seven different cancers, resulting in a comprehensive collection of 29 MDC subpopulations in the tumor microenvironment (TME). Distinguishing resident-tissue from monocyte-derived macrophages, we discovered a resident-tissue-like subpopulation within monocyte-derived macrophages. Additionally, hypoxia-driven macrophages emerged as a prominent TME component. Deconvolution of these profiles revealed five subpopulations as independent prognostic markers across various cancer types. Validation in large cohorts confirmed the FOLR2-expressing macrophage association with poor clinical outcomes in ovarian and triple-negative breast cancer. Moreover, the marker TREM2, commonly used to define immunosuppressive tumor-associated macrophages, cannot solely predict cancer prognosis, as different polarization states of macrophages express this marker in a context-dependent manner. This comprehensive MDC atlas offers valuable insights and a foundation for novel analyses, advancing strategies for treating solid cancers.
]]></description>
<dc:creator>Maklouf, G. R.</dc:creator>
<dc:creator>Guimaraes, G. R.</dc:creator>
<dc:creator>Teixeira, C. E.</dc:creator>
<dc:creator>Pretti, M. A. M.</dc:creator>
<dc:creator>de Oliveira Santos, L.</dc:creator>
<dc:creator>Tessarollo, N. G.</dc:creator>
<dc:creator>Toledo, N. E.</dc:creator>
<dc:creator>Falchetti, M.</dc:creator>
<dc:creator>Dimas, M. M.</dc:creator>
<dc:creator>Serain, A. F.</dc:creator>
<dc:creator>Bastos, N. C.</dc:creator>
<dc:creator>de Macedo, F. C.</dc:creator>
<dc:creator>Rodrigues, F. R.</dc:creator>
<dc:creator>da Silva, J. L.</dc:creator>
<dc:creator>Lummertz-Rocha, E.</dc:creator>
<dc:creator>Chaves, C. B. P.</dc:creator>
<dc:creator>de Melo, A. C.</dc:creator>
<dc:creator>Moraes-Vieira, P. M. M.</dc:creator>
<dc:creator>Mori, M. A.</dc:creator>
<dc:creator>Boroni, M.</dc:creator>
<dc:date>2023-01-04</dc:date>
<dc:identifier>doi:10.1101/2023.01.04.522727</dc:identifier>
<dc:title><![CDATA[A multi-tissue single-cell tumor microenvironment atlas reveals myeloid-derived cell states with significant impact on clinical outcome]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-01-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.08.14.553168v1?rss=1">
<title>
<![CDATA[
SuperCellCyto: enabling efficient analysis of large scale cytometry datasets 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.08.14.553168v1?rss=1"
</link>
<description><![CDATA[
Advancements in cytometry technologies have enabled quantification of up to 50 proteins across millions of cells at single cell resolution. Analysis of cytometry data routinely involves tasks such as data integration, clustering, and dimensionality reduction. While numerous tools exist, many require extensive run times when processing large cytometry data containing millions of cells. Existing solutions, such as random subsampling, are inadequate as they risk excluding rare cell subsets. To address this, we propose SuperCellCyto, an R package that builds on the SuperCell tool which groups highly similar cells into supercells. SuperCellCyto is available on GitHub (https://github.com/phipsonlab/SuperCellCyto).
]]></description>
<dc:creator>Putri, G. H.</dc:creator>
<dc:creator>Howitt, G.</dc:creator>
<dc:creator>Marsh-Wakefield, F.</dc:creator>
<dc:creator>Ashhurst, T. M.</dc:creator>
<dc:creator>Phipson, B.</dc:creator>
<dc:date>2023-08-14</dc:date>
<dc:identifier>doi:10.1101/2023.08.14.553168</dc:identifier>
<dc:title><![CDATA[SuperCellCyto: enabling efficient analysis of large scale cytometry datasets]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-08-14</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.04.27.489800v1?rss=1">
<title>
<![CDATA[
A human embryonic limb cell atlas resolved in space and time 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.04.27.489800v1?rss=1"
</link>
<description><![CDATA[
Human limbs emerge during the fourth post-conception week as mesenchymal buds which develop into fully-formed limbs over the subsequent months. Limb development is orchestrated by numerous temporally and spatially restricted gene expression programmes, making congenital alterations in phenotype common. Decades of work with model organisms has outlined the fundamental processes underlying vertebrate limb development, but an in-depth characterisation of this process in humans has yet to be performed. Here we detail the development of the human embryonic limb across space and time, using both single-cell and spatial transcriptomics. We demonstrate extensive diversification of cells, progressing from a restricted number of multipotent progenitors to myriad mature cell states, and identify several novel cell populations, including neural fibroblasts and multiple distinct mesenchymal states. We uncover two waves of human muscle development, each characterised by different cell states regulated by separate gene expression programmes. We identify musculin (MSC) as a key transcriptional repressor maintaining muscle stem cell identity and validate this by performing MSC knock down in human embryonic myoblasts, which results in significant upregulation of late myogenic genes. Through integration of multiple anatomically continuous spatial transcriptomic samples, we spatially map single-cell clusters across a sagittal section of a whole fetal hindlimb. We reveal a clear anatomical segregation between genes linked to brachydactyly and polysyndactyly, and uncover transcriptionally and spatially distinct populations of mesenchyme in the autopod. Finally, we perform scRNA-seq on murine embryonic limbs to facilitate cross-species developmental comparison at single-cell resolution, finding substantial homology between the two species.
]]></description>
<dc:creator>Zhang, B.</dc:creator>
<dc:creator>He, P.</dc:creator>
<dc:creator>Lawrence, J. E.</dc:creator>
<dc:creator>Wang, S.</dc:creator>
<dc:creator>Tuck, E.</dc:creator>
<dc:creator>Williams, B. A.</dc:creator>
<dc:creator>Roberts, K.</dc:creator>
<dc:creator>Kleshchevnikov, V.</dc:creator>
<dc:creator>Mamanova, L.</dc:creator>
<dc:creator>Bolt, L.</dc:creator>
<dc:creator>Polanski, K.</dc:creator>
<dc:creator>Elmentaite, R.</dc:creator>
<dc:creator>Fasouli, E. S.</dc:creator>
<dc:creator>Prete, M.</dc:creator>
<dc:creator>He, X.</dc:creator>
<dc:creator>Yayon, N.</dc:creator>
<dc:creator>Fu, Y.</dc:creator>
<dc:creator>Yang, H.</dc:creator>
<dc:creator>Liang, C.</dc:creator>
<dc:creator>Zhang, H.</dc:creator>
<dc:creator>Fitzpatrick, D.</dc:creator>
<dc:creator>Firth, H.</dc:creator>
<dc:creator>Dean, A.</dc:creator>
<dc:creator>Marioni, J.</dc:creator>
<dc:creator>Barker, R. A.</dc:creator>
<dc:creator>Storer, M. A.</dc:creator>
<dc:creator>Wold, B.</dc:creator>
<dc:creator>Zhang, H.</dc:creator>
<dc:creator>Teichmann, S.</dc:creator>
<dc:date>2022-04-28</dc:date>
<dc:identifier>doi:10.1101/2022.04.27.489800</dc:identifier>
<dc:title><![CDATA[A human embryonic limb cell atlas resolved in space and time]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-04-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.07.18.549537v1?rss=1">
<title>
<![CDATA[
Scalable querying of human cell atlases via a foundational model reveals commonalities across fibrosis-associated macrophages 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.07.18.549537v1?rss=1"
</link>
<description><![CDATA[
Single-cell RNA-seq (scRNA-seq) studies have profiled over 100 million human cells across diseases, developmental stages, and perturbations to date. A singular view of this vast and growing expression landscape could help reveal novel associations between cell states and diseases, discover cell states in unexpected tissue contexts, and relate in vivo cells to in vitro models. However, these require a common, scalable representation of cell profiles from across the body, a general measure of their similarity, and an efficient way to query these data. Here, we present SCimilarity, a metric learning framework to learn and search a unified and interpretable representation that annotates cell types and instantaneously queries for a cell state across tens of millions of profiles. We demonstrate SCimilarity on a 22.7 million cell corpus assembled across 399 published scRNA-seq studies, showing accurate integration, annotation and querying. We experimentally validated SCimilarity by querying across tissues for a macrophage subset originally identified in interstitial lung disease, and showing that cells with similar profiles are found in other fibrotic diseases, tissues, and a 3D hydrogel system, which we then repurposed to yield this cell state in vitro. SCimilarity serves as a foundational model for single cell gene expression data and enables researchers to query for similar cellular states across the entire human body, providing a powerful tool for generating novel biological insights from the growing Human Cell Atlas.
]]></description>
<dc:creator>Heimberg, G.</dc:creator>
<dc:creator>Kuo, T. C.</dc:creator>
<dc:creator>DePianto, D.</dc:creator>
<dc:creator>Heigl, T.</dc:creator>
<dc:creator>Diamant, N.</dc:creator>
<dc:creator>Salem, O.</dc:creator>
<dc:creator>Scalia, G.</dc:creator>
<dc:creator>Biancalani, T.</dc:creator>
<dc:creator>Rock, J.</dc:creator>
<dc:creator>Turley, S.</dc:creator>
<dc:creator>Bravo, H. C.</dc:creator>
<dc:creator>Kaminker, J.</dc:creator>
<dc:creator>Vander Heiden, J. A.</dc:creator>
<dc:creator>Regev, A.</dc:creator>
<dc:date>2023-07-19</dc:date>
<dc:identifier>doi:10.1101/2023.07.18.549537</dc:identifier>
<dc:title><![CDATA[Scalable querying of human cell atlases via a foundational model reveals commonalities across fibrosis-associated macrophages]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-07-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.08.23.554420v1?rss=1">
<title>
<![CDATA[
multiDGD: A versatile deep generative model for multi-omics data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.08.23.554420v1?rss=1"
</link>
<description><![CDATA[
Recent technological advancements in single-cell genomics have enabled joint profiling of gene expression and alternative modalities at unprecedented scale. Consequently, the complexity of multi-omics data sets is increasing massively. Existing models for multi-modal data are typically limited in functionality or scalability, making data integration and downstream analysis cumbersome. We present multiDGD, a scalable deep generative model providing a probabilistic framework to learn shared representations of transcriptome and chromatin accessibility. It shows outstanding performance on data reconstruction without feature selection. We demonstrate on several data sets from human and mouse that multiDGD learns well-clustered joint representations. We further find that probabilistic modelling of sample covatiates enables post-hoc data integration without the need for fine-tuning. Additionally, we show that multiDGD can detect statistical associations between genes and regulatory regions conditioned on the learned representations. multiDGD is available as an scverse-compatible package (https://github.com/Center-for-Health-Data-Science/multiDGD).
]]></description>
<dc:creator>Schuster, V.</dc:creator>
<dc:creator>Dann, E.</dc:creator>
<dc:creator>Krogh, A.</dc:creator>
<dc:creator>Teichmann, S.</dc:creator>
<dc:date>2023-08-23</dc:date>
<dc:identifier>doi:10.1101/2023.08.23.554420</dc:identifier>
<dc:title><![CDATA[multiDGD: A versatile deep generative model for multi-omics data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-08-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.08.23.554343v1?rss=1">
<title>
<![CDATA[
Polybacterial intracellular coinfection of epithelial stem cells in periodontitis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.08.23.554343v1?rss=1"
</link>
<description><![CDATA[
Periodontitis affects billions of people worldwide. To address interkingdom relationships of microbes and niche on periodontitis, we generated the first sin-gle-cell meta-atlas of human periodontium (34-sample, 105918-cell), harmo-nizing 32 annotations across 4 studies1-4. Highly multiplexed immunofluores-cence (32-antibody; 113910-cell) revealed spatial innate and adaptive immune foci segregation around tooth-adjacent epithelial cells. Sulcular and junctional keratinocytes (SK/JKs) within epithelia skewed toward proinflammatory phe-notypes; diseased JK stem/progenitors displayed altered differentiation states and chemotactic cytokines for innate immune cells. Single-cell metagenomics utilizing unmapped reads revealed 37 bacterial species. 16S and rRNA probes detected polybacterial intracellular pathogenesis ("co-infection") of 4 species within single cells for the first time in vivo. Challenging coinfected primary human SK/JKs with lipopolysaccharide revealed solitary and synergistic ef-fects. Coinfected single-cell analysis independently displayed proinflammatory phenotypes in situ. Here, we demonstrate the first evidence of polybacterial intracellular pathogenesis in human tissues and cells--potentially influencing chronic diseases at distant sites.
]]></description>
<dc:creator>Easter, Q. T.</dc:creator>
<dc:creator>Matuck, B. F.</dc:creator>
<dc:creator>Stark, G. B.</dc:creator>
<dc:creator>Worth, C. L.</dc:creator>
<dc:creator>Predeus, A. V.</dc:creator>
<dc:creator>Fremin, B.</dc:creator>
<dc:creator>Huynh, K. T.</dc:creator>
<dc:creator>Ranganathan, V.</dc:creator>
<dc:creator>Pereira, D.</dc:creator>
<dc:creator>Weaver, T.</dc:creator>
<dc:creator>Miller, K.</dc:creator>
<dc:creator>Perez, P.</dc:creator>
<dc:creator>Hasuike, A.</dc:creator>
<dc:creator>Chen, Z.</dc:creator>
<dc:creator>Bush, M.</dc:creator>
<dc:creator>Qu, X.</dc:creator>
<dc:creator>Warner, B. M.</dc:creator>
<dc:creator>Lee, J.</dc:creator>
<dc:creator>Wallet, S. M.</dc:creator>
<dc:creator>Sequeira, I.</dc:creator>
<dc:creator>Tyc, K. M.</dc:creator>
<dc:creator>Liu, J.</dc:creator>
<dc:creator>Ko, K. I.</dc:creator>
<dc:creator>Teichmann, S. A.</dc:creator>
<dc:creator>Byrd, K. M.</dc:creator>
<dc:date>2023-08-24</dc:date>
<dc:identifier>doi:10.1101/2023.08.23.554343</dc:identifier>
<dc:title><![CDATA[Polybacterial intracellular coinfection of epithelial stem cells in periodontitis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-08-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.09.04.555678v1?rss=1">
<title>
<![CDATA[
Human subcutaneous and visceral adipocyte atlases uncover classical and specialized adipocytes and depot-specific patterns 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.09.04.555678v1?rss=1"
</link>
<description><![CDATA[
Human adipose depots are functionally distinct. Yet, recent single-nucleus RNA-sequencing (snRNA-seq) analyses largely uncovered overlapping/similar cell-type landscapes. We hypothesized that adipocytes subtypes, differentiation trajectories, and/or intercellular communication patterns could illuminate this depot similarity-difference gap. For this, we performed snRNA-seq of human subcutaneous and visceral adipose tissue. Whereas the majority of adipocytes in both depots were  classical, namely enriched in lipid metabolism pathways, we also observed  specialized adipocyte subtypes that were enriched in immune-related, extracellular matrix deposition (fibrosis), vascularization/angiogenesis, or ribosomal processes. Pseudo-temporal analysis suggested a developmental trajectory from adipose progenitor cells to classical adipocytes via specialized adipocytes, suggesting that the classical state stems from loss, rather than gain, of specialized functions. Lastly, intercellular communication routes were consistent with the different inflammatory tone of the two depots. Jointly, these findings provide a high-resolution view into the contribution of cellular composition, differentiation, and intercellular communication patterns to human fat depot differences.
]]></description>
<dc:creator>Lazarescu, O.</dc:creator>
<dc:creator>Ziv Agam, M.</dc:creator>
<dc:creator>Haim, Y.</dc:creator>
<dc:creator>Hekselman, I.</dc:creator>
<dc:creator>Jubran, J.</dc:creator>
<dc:creator>Shneyour, A.</dc:creator>
<dc:creator>Kitsberg, D.</dc:creator>
<dc:creator>Levin, L.</dc:creator>
<dc:creator>Liberty, I. F.</dc:creator>
<dc:creator>Yoel, U.</dc:creator>
<dc:creator>Dukhno, O.</dc:creator>
<dc:creator>Adam, M.</dc:creator>
<dc:creator>Korner, A.</dc:creator>
<dc:creator>Murphy, R.</dc:creator>
<dc:creator>Bluher, M.</dc:creator>
<dc:creator>Habib, N.</dc:creator>
<dc:creator>Rudich, A.</dc:creator>
<dc:creator>Yeger-Lotem, E.</dc:creator>
<dc:date>2023-09-04</dc:date>
<dc:identifier>doi:10.1101/2023.09.04.555678</dc:identifier>
<dc:title><![CDATA[Human subcutaneous and visceral adipocyte atlases uncover classical and specialized adipocytes and depot-specific patterns]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-09-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.08.18.553912v1?rss=1">
<title>
<![CDATA[
Consensus prediction of cell type labels with popV 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.08.18.553912v1?rss=1"
</link>
<description><![CDATA[
Cell-type classification is a crucial step in single-cell analysis. To facilitate this, several methods have been proposed for the task of transferring a cell-type label from an annotated reference atlas to unannotated query data sets. Existing methods for transferring cell-type labels lack proper uncertainty estimation for the resulting annotations, limiting interpretability and usefulness. To address this, we propose popular Vote (popV, https://github.com/YosefLab/popV), an ensemble of prediction models with an ontology-based voting scheme. PopV achieves accurate cell-type labeling and provides effective uncertainty scores. In multiple case studies, popV confidently annotates the majority of cells while highlighting cell populations that are challenging to annotate. This additional step helps to reduce the load of manual inspection, which is often a necessary component of the annotation process, and enables one to focus on the most problematic parts of the annotation, streamlining the overall annotation process.
]]></description>
<dc:creator>Ergen, C.</dc:creator>
<dc:creator>Xing, G.</dc:creator>
<dc:creator>Xu, C.</dc:creator>
<dc:creator>Jayasuriya, M.</dc:creator>
<dc:creator>McGeever, E.</dc:creator>
<dc:creator>Pisco, A. O.</dc:creator>
<dc:creator>Streets, A. M.</dc:creator>
<dc:creator>Yosef, N.</dc:creator>
<dc:date>2023-08-21</dc:date>
<dc:identifier>doi:10.1101/2023.08.18.553912</dc:identifier>
<dc:title><![CDATA[Consensus prediction of cell type labels with popV]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-08-21</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.09.29.560114v1?rss=1">
<title>
<![CDATA[
Cell type-specific gene expression dynamics during human brain maturation 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.09.29.560114v1?rss=1"
</link>
<description><![CDATA[
The human brain undergoes protracted post-natal maturation, guided by dynamic changes in gene expression. Most studies exploring these processes have used bulk tissue analyses, which mask cell type-specific gene expression dynamics. Here, using single nucleus (sn)RNA-seq on temporal lobe tissue, including samples of African ancestry, we build a joint paediatric and adult atlas of 75 cell subtypes, which we verify with spatial transcriptomics. We explore the differences between paediatric and adult cell types, revealing the genes and pathways that change during brain maturation. Our results highlight excitatory neuron subtypes, including the LTK and FREM subtypes, that show elevated expression of genes associated with cognition and synaptic plasticity in paediatric tissue. The new resources we present here improve our understanding of the brain during its development and contribute to global efforts to build an inclusive brain cell map.
]]></description>
<dc:creator>Steyn, C.</dc:creator>
<dc:creator>Mishi, R.</dc:creator>
<dc:creator>Fillmore, S.</dc:creator>
<dc:creator>Verhoog, M. B.</dc:creator>
<dc:creator>More, J.</dc:creator>
<dc:creator>Rohlwink, U. K.</dc:creator>
<dc:creator>Melvill, R.</dc:creator>
<dc:creator>Butler, J.</dc:creator>
<dc:creator>Enslin, J. M. N.</dc:creator>
<dc:creator>Jacobs, M.</dc:creator>
<dc:creator>Quinones, S.</dc:creator>
<dc:creator>Dulla, C. G.</dc:creator>
<dc:creator>Raimondo, J. V.</dc:creator>
<dc:creator>Figaji, A.</dc:creator>
<dc:creator>Hockman, D.</dc:creator>
<dc:date>2023-09-29</dc:date>
<dc:identifier>doi:10.1101/2023.09.29.560114</dc:identifier>
<dc:title><![CDATA[Cell type-specific gene expression dynamics during human brain maturation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-09-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.10.24.513555v1?rss=1">
<title>
<![CDATA[
Single-cell analysis of prenatal and postnatal human cortical development 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.10.24.513555v1?rss=1"
</link>
<description><![CDATA[
We analyze more than 700,000 single-nucleus RNA-seq profiles from 106 donors during prenatal and postnatal developmental stages and identify lineage-specific programs that underlie the development of specific subtypes of excitatory cortical neurons, interneurons, glial cell types and brain vasculature. By leveraging single-nucleus chromatin accessibility data, we delineate enhancer-gene regulatory networks and transcription factors that control commitment of specific cortical lineages. By intersecting our results with genetic risk factors for human brain diseases, we identify the cortical cell types and lineages most vulnerable to genetic insults of different brain disorders, especially autism. We find that lineage-specific gene expression programs upregulated in female cells are especially enriched for the genetic risk factors of autism. Our study captures the molecular progression of cortical lineages across human development.

One Sentence SummarySingle-cell transcriptomic atlas of human cortical development identifies lineage and sex-specific programs and their implication in brain disorders.
]]></description>
<dc:creator>Velmeshev, D.</dc:creator>
<dc:creator>Perez, Y.</dc:creator>
<dc:creator>Yan, Z.</dc:creator>
<dc:creator>Valencia, J. E.</dc:creator>
<dc:creator>Castaneda-Castellanos, D. R.</dc:creator>
<dc:creator>Schirmer, L.</dc:creator>
<dc:creator>Mayer, S.</dc:creator>
<dc:creator>Wick, B.</dc:creator>
<dc:creator>Wang, S.</dc:creator>
<dc:creator>Nowakowski, T. J.</dc:creator>
<dc:creator>Paredes, M.</dc:creator>
<dc:creator>Huang, E. J. J.</dc:creator>
<dc:creator>Kriegstein, A.</dc:creator>
<dc:date>2022-10-25</dc:date>
<dc:identifier>doi:10.1101/2022.10.24.513555</dc:identifier>
<dc:title><![CDATA[Single-cell analysis of prenatal and postnatal human cortical development]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-10-25</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.08.21.554174v1?rss=1">
<title>
<![CDATA[
Spatiotemporal molecular dynamics of the developing human thalamus 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.08.21.554174v1?rss=1"
</link>
<description><![CDATA[
The thalamus plays a central coordinating role in the brain. Thalamic neurons are organized into spatially-distinct nuclei, but the molecular architecture of thalamic development is poorly understood, especially in humans. To begin to delineate the molecular trajectories of cell fate specification and organization in the developing human thalamus, we used single cell and multiplexed spatial transcriptomics. Here we show that molecularly-defined thalamic neurons differentiate in the second trimester of human development, and that these neurons organize into spatially and molecularly distinct nuclei. We identify major subtypes of glutamatergic neuron subtypes that are differentially enriched in anatomically distinct nuclei. In addition, we identify six subtypes of GABAergic neurons that are shared and distinct across thalamic nuclei.

One-Sentence SummarySingle cell and spatial profiling of the developing thalamus in the first and second trimester yields molecular mechanisms of thalamic nuclei development.
]]></description>
<dc:creator>Kim, C.</dc:creator>
<dc:creator>Shin, D.</dc:creator>
<dc:creator>Wang, A.</dc:creator>
<dc:creator>Nowakowski, T.</dc:creator>
<dc:date>2023-08-22</dc:date>
<dc:identifier>doi:10.1101/2023.08.21.554174</dc:identifier>
<dc:title><![CDATA[Spatiotemporal molecular dynamics of the developing human thalamus]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-08-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.10.07.511366v1?rss=1">
<title>
<![CDATA[
Inter-individual variation in human cortical cell type abundance and expression 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.10.07.511366v1?rss=1"
</link>
<description><![CDATA[
Single cell transcriptomic studies have identified a conserved set of neocortical cell types from small post-mortem cohorts. We extend these efforts by assessing cell type variation across 75 adult individuals undergoing epilepsy and tumor surgeries. Nearly all nuclei map to one of 125 robust cell types identified in middle temporal gyrus, but with varied abundances and gene expression signatures across donors, particularly in deep layer glutamatergic neurons. A minority of variance is explainable by known factors including donor identity and small contributions from age, sex, ancestry, and disease state. Genomic variation was significantly associated with variable expression of 150-250 genes for most cell types. Thus, human individuals display a highly consistent cellular makeup, but with significant variation reflecting donor characteristics, disease condition, and genetic regulation.

One-Sentence SummaryInter-individual variation in human cortex is greatest for deep layer excitatory neurons and largely unexplainable by known factors.
]]></description>
<dc:creator>Johansen, N.</dc:creator>
<dc:creator>Somasundaram, S.</dc:creator>
<dc:creator>Travaglini, K. J.</dc:creator>
<dc:creator>Yanny, A. M.</dc:creator>
<dc:creator>Shumyatcher, M.</dc:creator>
<dc:creator>Casper, T.</dc:creator>
<dc:creator>Cobbs, C.</dc:creator>
<dc:creator>Dee, N.</dc:creator>
<dc:creator>Ellenbogen, R.</dc:creator>
<dc:creator>Ferreira, M.</dc:creator>
<dc:creator>Goldy, J.</dc:creator>
<dc:creator>Guzman, J.</dc:creator>
<dc:creator>Gwinn, R.</dc:creator>
<dc:creator>Hirschstein, D.</dc:creator>
<dc:creator>Jorstad, N. L.</dc:creator>
<dc:creator>Keene, C. D.</dc:creator>
<dc:creator>Ko, A.</dc:creator>
<dc:creator>Levi, B. P.</dc:creator>
<dc:creator>Ojemann, J. G.</dc:creator>
<dc:creator>Pham, T.</dc:creator>
<dc:creator>Shapovalova, N.</dc:creator>
<dc:creator>Silbergeld, D.</dc:creator>
<dc:creator>Sulc, J.</dc:creator>
<dc:creator>Torkelson, A.</dc:creator>
<dc:creator>Tung, H.</dc:creator>
<dc:creator>Smith, K.</dc:creator>
<dc:creator>Lein, E. S.</dc:creator>
<dc:creator>Bakken, T. E.</dc:creator>
<dc:creator>Hodge, R. D.</dc:creator>
<dc:creator>Miller, J. A.</dc:creator>
<dc:date>2022-10-07</dc:date>
<dc:identifier>doi:10.1101/2022.10.07.511366</dc:identifier>
<dc:title><![CDATA[Inter-individual variation in human cortical cell type abundance and expression]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-10-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.11.06.515349v1?rss=1">
<title>
<![CDATA[
Transcriptomic cytoarchitecture reveals principles of human neocortex organization 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.11.06.515349v1?rss=1"
</link>
<description><![CDATA[
Variation in cortical cytoarchitecture is the basis for histology-based definition of cortical areas, such as Brodmann areas. Single cell transcriptomics enables higher-resolution characterization of cell types in human cortex, which we used to revisit the idea of the canonical cortical microcircuit and to understand functional areal specialization. Deeply sampled single nucleus RNA-sequencing of eight cortical areas spanning cortical structural variation showed highly consistent cellular makeup for 24 coarse cell subclasses. However, proportions of excitatory neuron subclasses varied strikingly, reflecting differences in intra- and extracortical connectivity across primary sensorimotor and association cortices. Astrocytes and oligodendrocytes also showed differences in laminar organization across areas. Primary visual cortex showed dramatically different organization, including major differences in the ratios of excitatory to inhibitory neurons, expansion of layer 4 excitatory neuron types and specialized inhibitory neurons. Finally, gene expression variation in conserved neuron subclasses predicts differences in synaptic function across areas. Together these results provide a refined cellular and molecular characterization of human cortical cytoarchitecture that reflects functional connectivity and predicts areal specialization.
]]></description>
<dc:creator>Jorstad, N. L.</dc:creator>
<dc:creator>Close, J.</dc:creator>
<dc:creator>Johansen, N.</dc:creator>
<dc:creator>Yanny, A. M.</dc:creator>
<dc:creator>Barkan, E. R.</dc:creator>
<dc:creator>Travaglini, K. J.</dc:creator>
<dc:creator>Bertagnolli, D.</dc:creator>
<dc:creator>Campos, J.</dc:creator>
<dc:creator>Casper, T.</dc:creator>
<dc:creator>Crichton, K.</dc:creator>
<dc:creator>Dee, N.</dc:creator>
<dc:creator>Ding, S.-L.</dc:creator>
<dc:creator>Gelfand, E.</dc:creator>
<dc:creator>Goldy, J.</dc:creator>
<dc:creator>Hirschstein, D.</dc:creator>
<dc:creator>Kroll, M.</dc:creator>
<dc:creator>Kunst, M.</dc:creator>
<dc:creator>Lathia, K.</dc:creator>
<dc:creator>Long, B.</dc:creator>
<dc:creator>Martin, N.</dc:creator>
<dc:creator>McMillen, D.</dc:creator>
<dc:creator>Pham, T.</dc:creator>
<dc:creator>Rimorin, C.</dc:creator>
<dc:creator>Ruiz, A.</dc:creator>
<dc:creator>Shapovalova, N.</dc:creator>
<dc:creator>Shehata, S.</dc:creator>
<dc:creator>Siletti, K.</dc:creator>
<dc:creator>Somasundaram, S.</dc:creator>
<dc:creator>Sulc, J.</dc:creator>
<dc:creator>Tieu, M.</dc:creator>
<dc:creator>Torkelson, A.</dc:creator>
<dc:creator>Tung, H.</dc:creator>
<dc:creator>Ward, K.</dc:creator>
<dc:creator>Callaway, E. M.</dc:creator>
<dc:creator>Hof, P. R.</dc:creator>
<dc:creator>Keene, C. D.</dc:creator>
<dc:creator>Levi, B. P.</dc:creator>
<dc:creator>Linnarsson, S.</dc:creator>
<dc:creator>Mitra, P. P.</dc:creator>
<dc:creator>Smith, K.</dc:creator>
<dc:creator>Hodge, R. D.</dc:creator>
<dc:creator>Bakken, T. E.</dc:creator>
<dc:creator>Lein, E. S.</dc:creator>
<dc:date>2022-11-06</dc:date>
<dc:identifier>doi:10.1101/2022.11.06.515349</dc:identifier>
<dc:title><![CDATA[Transcriptomic cytoarchitecture reveals principles of human neocortex organization]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-11-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.11.08.515739v1?rss=1">
<title>
<![CDATA[
Signature morpho-electric properties of diverse GABAergic interneurons in the human neocortex 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.11.08.515739v1?rss=1"
</link>
<description><![CDATA[
Human cortical interneurons have been challenging to study due to high diversity and lack of mature brain tissue platforms and genetic targeting tools. We employed rapid GABAergic neuron viral labeling plus unbiased Patch-seq sampling in brain slices to define the signature morpho-electric properties of GABAergic neurons in the human neocortex. Viral targeting greatly facilitated sampling of the SST subclass, including primate specialized double bouquet cells which mapped to two SST transcriptomic types. Multimodal analysis uncovered an SST neuron type with properties inconsistent with original subclass assignment; we instead propose reclassification into PVALB subclass. Our findings provide novel insights about functional properties of human cortical GABAergic neuron subclasses and types and highlight the essential role of multimodal annotation for refinement of emerging transcriptomic cell type taxonomies.

One Sentence SummaryViral genetic labeling of GABAergic neurons in human ex vivo brain slices paired with Patch-seq recording yields an in-depth functional annotation of human cortical interneuron subclasses and types and highlights the essential role of multimodal functional annotation for refinement of emerging transcriptomic cell type taxonomies.
]]></description>
<dc:creator>Lee, B.</dc:creator>
<dc:creator>Dalley, R.</dc:creator>
<dc:creator>Miller, J. A.</dc:creator>
<dc:creator>Chartrand, T.</dc:creator>
<dc:creator>Close, J.</dc:creator>
<dc:creator>Mann, R.</dc:creator>
<dc:creator>Mukora, A.</dc:creator>
<dc:creator>Ng, L.</dc:creator>
<dc:creator>Alfiler, L.</dc:creator>
<dc:creator>Baker, K.</dc:creator>
<dc:creator>Bertagnolli, D.</dc:creator>
<dc:creator>Brouner, K.</dc:creator>
<dc:creator>Casper, T.</dc:creator>
<dc:creator>Csajbok, E.</dc:creator>
<dc:creator>Dee, N.</dc:creator>
<dc:creator>Donadio, N.</dc:creator>
<dc:creator>Driessens, S. L. W.</dc:creator>
<dc:creator>Egdorf, T.</dc:creator>
<dc:creator>Enstrom, R.</dc:creator>
<dc:creator>Galakhova, A. A.</dc:creator>
<dc:creator>Gary, A.</dc:creator>
<dc:creator>Gelfand, E.</dc:creator>
<dc:creator>Goldy, J.</dc:creator>
<dc:creator>Hadley, K.</dc:creator>
<dc:creator>Heistek, T. S.</dc:creator>
<dc:creator>Hill, D.</dc:creator>
<dc:creator>Johansen, N.</dc:creator>
<dc:creator>Jorstad, N.</dc:creator>
<dc:creator>Kim, L.</dc:creator>
<dc:creator>Kocsis, A. K.</dc:creator>
<dc:creator>Kruse, L.</dc:creator>
<dc:creator>Kunst, M.</dc:creator>
<dc:creator>Leon, G.</dc:creator>
<dc:creator>Long, B.</dc:creator>
<dc:creator>Mallory, M.</dc:creator>
<dc:creator>Maxwell, M.</dc:creator>
<dc:creator>McGraw, M.</dc:creator>
<dc:creator>McMillen, D.</dc:creator>
<dc:creator>Melief, E. J.</dc:creator>
<dc:creator>Molnar, G.</dc:creator>
<dc:creator>Mortrud, M. T.</dc:creator>
<dc:creator>Newman, D.</dc:creator>
<dc:creator>Nyhus, J.</dc:creator>
<dc:creator>Opitz-Araya, X.</dc:creator>
<dc:creator>Pham, T.</dc:creator>
<dc:creator>Pom, A.</dc:creator>
<dc:creator>Potekhina, L.</dc:creator>
<dc:creator>Rajanbabu</dc:creator>
<dc:date>2022-11-09</dc:date>
<dc:identifier>doi:10.1101/2022.11.08.515739</dc:identifier>
<dc:title><![CDATA[Signature morpho-electric properties of diverse GABAergic interneurons in the human neocortex]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-11-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.10.24.511199v1?rss=1">
<title>
<![CDATA[
Morpho-electric and transcriptomic divergence of the layer 1 interneuron repertoire in human versus mouse neocortex 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.10.24.511199v1?rss=1"
</link>
<description><![CDATA[
Neocortical layer 1 (L1) is a site of convergence between pyramidal neuron dendrites and feedback axons where local inhibitory signaling can profoundly shape cortical processing. Evolutionary expansion of human neocortex is marked by distinctive pyramidal neuron types with extensive branching in L1, but whether L1 interneurons are similarly diverse is underexplored. Using patch-seq recordings from human neurosurgically resected tissues, we identified four transcriptomically defined subclasses, unique subtypes within those subclasses and additional types with no mouse L1 homologue. Compared with mouse, human subclasses were more strongly distinct from each other across all modalities. Accompanied by higher neuron density and more variable cell sizes compared with mouse, these findings suggest L1 is an evolutionary hotspot, reflecting the increasing demands of regulating the expanding human neocortical circuit.

One Sentence SummaryUsing transcriptomics and morpho-electric analyses, we describe innovations in human neocortical layer 1 interneurons.
]]></description>
<dc:creator>Chartrand, T.</dc:creator>
<dc:creator>Dalley, R.</dc:creator>
<dc:creator>Close, J.</dc:creator>
<dc:creator>Goriounova, N. A.</dc:creator>
<dc:creator>Lee, B. R.</dc:creator>
<dc:creator>Mann, R.</dc:creator>
<dc:creator>Miller, J. A.</dc:creator>
<dc:creator>Molnar, G.</dc:creator>
<dc:creator>Mukora, A.</dc:creator>
<dc:creator>Alfiler, L.</dc:creator>
<dc:creator>Baker, K.</dc:creator>
<dc:creator>Bakken, T. E.</dc:creator>
<dc:creator>Berg, J.</dc:creator>
<dc:creator>Bertagnolli, D.</dc:creator>
<dc:creator>Braun, T.</dc:creator>
<dc:creator>Brouner, K.</dc:creator>
<dc:creator>Casper, T.</dc:creator>
<dc:creator>Csajbok, E. A.</dc:creator>
<dc:creator>Dee, N.</dc:creator>
<dc:creator>Egdorf, T.</dc:creator>
<dc:creator>Enstrom, R.</dc:creator>
<dc:creator>Galakhova, A. A.</dc:creator>
<dc:creator>Gary, A.</dc:creator>
<dc:creator>Gelfand, E.</dc:creator>
<dc:creator>Goldy, J.</dc:creator>
<dc:creator>Hadley, K.</dc:creator>
<dc:creator>Heistek, T. S.</dc:creator>
<dc:creator>Hill, D.</dc:creator>
<dc:creator>Jorstad, N.</dc:creator>
<dc:creator>Kim, L.</dc:creator>
<dc:creator>Kocsis, A. K.</dc:creator>
<dc:creator>Kruse, L.</dc:creator>
<dc:creator>Leon, G.</dc:creator>
<dc:creator>Long, B.</dc:creator>
<dc:creator>Mallory, M.</dc:creator>
<dc:creator>McGraw, M.</dc:creator>
<dc:creator>McMillen, D.</dc:creator>
<dc:creator>Melief, E. J.</dc:creator>
<dc:creator>Mihut, N.</dc:creator>
<dc:creator>Ng, L.</dc:creator>
<dc:creator>Nyhus, J.</dc:creator>
<dc:creator>Omstead, V.</dc:creator>
<dc:creator>Peterfi, Z.</dc:creator>
<dc:creator>Pom, A.</dc:creator>
<dc:creator>Potekhina, L.</dc:creator>
<dc:creator>Rajanbabu, R.</dc:creator>
<dc:creator>Rozsa, M.</dc:creator>
<dc:creator>Ruiz, A.</dc:creator>
<dc:creator>Sa</dc:creator>
<dc:date>2022-10-25</dc:date>
<dc:identifier>doi:10.1101/2022.10.24.511199</dc:identifier>
<dc:title><![CDATA[Morpho-electric and transcriptomic divergence of the layer 1 interneuron repertoire in human versus mouse neocortex]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-10-25</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.09.19.508480v1?rss=1">
<title>
<![CDATA[
Comparative transcriptomics reveals human-specific cortical features 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.09.19.508480v1?rss=1"
</link>
<description><![CDATA[
Humans have unique cognitive abilities among primates, including language, but their molecular, cellular, and circuit substrates are poorly understood. We used comparative single nucleus transcriptomics in adult humans, chimpanzees, gorillas, rhesus macaques, and common marmosets from the middle temporal gyrus (MTG) to understand human-specific features of cellular and molecular organization. Human, chimpanzee, and gorilla MTG showed highly similar cell type composition and laminar organization, and a large shift in proportions of deep layer intratelencephalic-projecting neurons compared to macaque and marmoset. Species differences in gene expression generally mirrored evolutionary distance and were seen in all cell types, although chimpanzees were more similar to gorillas than humans, consistent with faster divergence along the human lineage. Microglia, astrocytes, and oligodendrocytes showed accelerated gene expression changes compared to neurons or oligodendrocyte precursor cells, indicating either relaxed evolutionary constraints or positive selection in these cell types. Only a few hundred genes showed human-specific patterning in all or specific cell types, and were significantly enriched near human accelerated regions (HARs) and conserved deletions (hCONDELS) and in cell adhesion and intercellular signaling pathways. These results suggest that relatively few cellular and molecular changes uniquely define adult human cortical structure, particularly by affecting circuit connectivity and glial cell function.
]]></description>
<dc:creator>Jorstad, N. L.</dc:creator>
<dc:creator>Song, J. H. T.</dc:creator>
<dc:creator>Exposito-Alonso, D.</dc:creator>
<dc:creator>Suresh, H.</dc:creator>
<dc:creator>Castro, N.</dc:creator>
<dc:creator>Krienen, F. M.</dc:creator>
<dc:creator>Yanny, A. M.</dc:creator>
<dc:creator>Close, J.</dc:creator>
<dc:creator>Gelfand, E.</dc:creator>
<dc:creator>Travaglini, K. J.</dc:creator>
<dc:creator>Basu, S.</dc:creator>
<dc:creator>Beaudin, M.</dc:creator>
<dc:creator>Bertagnolli, D.</dc:creator>
<dc:creator>Crow, M.</dc:creator>
<dc:creator>Ding, S.-L.</dc:creator>
<dc:creator>Eggermont, J.</dc:creator>
<dc:creator>Glandon, A.</dc:creator>
<dc:creator>Goldy, J.</dc:creator>
<dc:creator>Kroes, T.</dc:creator>
<dc:creator>Long, B.</dc:creator>
<dc:creator>McMillen, D.</dc:creator>
<dc:creator>Pham, T.</dc:creator>
<dc:creator>Rimorin, C.</dc:creator>
<dc:creator>Siletti, K.</dc:creator>
<dc:creator>Somasundaram, S.</dc:creator>
<dc:creator>Tieu, M.</dc:creator>
<dc:creator>Torkelson, A.</dc:creator>
<dc:creator>Ward, K.</dc:creator>
<dc:creator>Feng, G.</dc:creator>
<dc:creator>Hopkins, W. D.</dc:creator>
<dc:creator>Hollt, T.</dc:creator>
<dc:creator>Keene, C. D.</dc:creator>
<dc:creator>Linnarsson, S.</dc:creator>
<dc:creator>McCarroll, S. A.</dc:creator>
<dc:creator>Lelieveldt, B. P.</dc:creator>
<dc:creator>Sherwood, C. C.</dc:creator>
<dc:creator>Smith, K.</dc:creator>
<dc:creator>Walsh, C. A.</dc:creator>
<dc:creator>Dobin, A.</dc:creator>
<dc:creator>Gillis, J.</dc:creator>
<dc:creator>Lein, E. S.</dc:creator>
<dc:creator>Hodge, R. D.</dc:creator>
<dc:creator>Bakken, T. E.</dc:creator>
<dc:date>2022-09-19</dc:date>
<dc:identifier>doi:10.1101/2022.09.19.508480</dc:identifier>
<dc:title><![CDATA[Comparative transcriptomics reveals human-specific cortical features]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-09-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.11.09.515833v1?rss=1">
<title>
<![CDATA[
A comparative atlas of single-cell chromatin accessibility in the human brain 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.11.09.515833v1?rss=1"
</link>
<description><![CDATA[
The human brain contains an extraordinarily diverse set of neuronal and glial cell types. Recent advances in single cell transcriptomics have begun to delineate the cellular heterogeneity in different brain regions, but the transcriptional regulatory programs responsible for the identity and function of each brain cell type remain to be defined. Here, we carried out single nucleus ATAC-seq analysis to probe the open chromatin landscape from over 1.1 million cells in 42 brain regions of three neurotypical adult donors. Integrative analysis of the resulting data identified 107 distinct cell types and revealed the cell-type-specific usage of 544,735 candidate cis-regulatory DNA elements (cCREs) in the human genome. Nearly 1/3 of them displayed sequence conservation as well as chromatin accessibility in the mouse brain. On the other hand, nearly 40% cCREs were human specific, with chromatin accessibility associated with species-restricted gene expression. Interestingly, these human specific cCREs were enriched for distinct families of retrotransposable elements, which displayed cell-type-specific chromatin accessibility. We uncovered strong associations between specific brain cell types and neuropsychiatric disorders. We futher developed deep learning models to predict regulatory function of non-coding disease risk variants.
]]></description>
<dc:creator>Li, Y. E.</dc:creator>
<dc:creator>Preissl, S.</dc:creator>
<dc:creator>Miller, M.</dc:creator>
<dc:creator>Johnson, N. D.</dc:creator>
<dc:creator>Wang, Z.</dc:creator>
<dc:creator>Jiao, H.</dc:creator>
<dc:creator>Zhu, C.</dc:creator>
<dc:creator>Wang, Z.</dc:creator>
<dc:creator>Xie, Y.</dc:creator>
<dc:creator>Poirin, O.</dc:creator>
<dc:creator>Kern, C.</dc:creator>
<dc:creator>Pinto-Duarte, A.</dc:creator>
<dc:creator>Tian, W.</dc:creator>
<dc:creator>Siletti, K.</dc:creator>
<dc:creator>Emerson, N.</dc:creator>
<dc:creator>Osteen, J.</dc:creator>
<dc:creator>Lucero, J.</dc:creator>
<dc:creator>Lin, L.</dc:creator>
<dc:creator>Yang, Q.</dc:creator>
<dc:creator>Espinoza, S.</dc:creator>
<dc:creator>Zhu, Q.</dc:creator>
<dc:creator>Zemke, N.</dc:creator>
<dc:creator>Yanny, A. M.</dc:creator>
<dc:creator>Nyhus, J.</dc:creator>
<dc:creator>Dee, N.</dc:creator>
<dc:creator>Casper, T.</dc:creator>
<dc:creator>Shapovalova, N.</dc:creator>
<dc:creator>Hirschstein, D.</dc:creator>
<dc:creator>Hodge, R. D.</dc:creator>
<dc:creator>Linnarsson, S.</dc:creator>
<dc:creator>Bakken, T. E.</dc:creator>
<dc:creator>Levi, B.</dc:creator>
<dc:creator>Keene, C. D.</dc:creator>
<dc:creator>Shang, J.</dc:creator>
<dc:creator>Lein, E. S.</dc:creator>
<dc:creator>Wang, A.</dc:creator>
<dc:creator>Behrens, M. M.</dc:creator>
<dc:creator>Ecker, J. R.</dc:creator>
<dc:creator>Ren, B.</dc:creator>
<dc:date>2022-11-10</dc:date>
<dc:identifier>doi:10.1101/2022.11.09.515833</dc:identifier>
<dc:title><![CDATA[A comparative atlas of single-cell chromatin accessibility in the human brain]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-11-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.10.18.512724v1?rss=1">
<title>
<![CDATA[
Molecular programs of regional specification and neural stem cell fate progression in developing macaque telencephalon 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.10.18.512724v1?rss=1"
</link>
<description><![CDATA[
Early telencephalic development involves patterning of the distinct regions and fate specification of the neural stem cells (NSCs). These processes, mainly characterized in rodents, remain elusive in primates and thus our understanding of conserved and species-specific features. Here, we profiled 761,529 single-cell transcriptomes from multiple regions of the prenatal macaque telencephalon. We defined the molecular programs of the early organizing centers and their cross-talk with NSCs, finding primate-biased signaling active in the antero-ventral telencephalon. Regional transcriptomic divergences were evident at early states of neocortical NSC progression and in differentiated neurons and astrocytes, more than in intermediate transitions. Finally, we show that neuropsychiatric disease- and brain cancer-risk genes have putative early roles in the telencephalic organizers activity and across cortical NSC progression.

One-Sentence SummarySingle-cell transcriptomics reveals molecular logics of arealization and neural stem cell fate specification in developing macaque brain
]]></description>
<dc:creator>Micali, N.</dc:creator>
<dc:creator>Ma, S.</dc:creator>
<dc:creator>Li, M.</dc:creator>
<dc:creator>Kim, S.-K.</dc:creator>
<dc:creator>Mato-Blanco, X.</dc:creator>
<dc:creator>Sindhu, S.</dc:creator>
<dc:creator>Arellano, J. I.</dc:creator>
<dc:creator>Gao, T.</dc:creator>
<dc:creator>Duque, A.</dc:creator>
<dc:creator>Santpere, G.</dc:creator>
<dc:creator>Sestan, N.</dc:creator>
<dc:creator>Rakic, P.</dc:creator>
<dc:date>2022-10-18</dc:date>
<dc:identifier>doi:10.1101/2022.10.18.512724</dc:identifier>
<dc:title><![CDATA[Molecular programs of regional specification and neural stem cell fate progression in developing macaque telencephalon]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-10-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.08.09.503303v1?rss=1">
<title>
<![CDATA[
Temporal disparity of action potentials triggered in axon initial segments and distal axons in the neocortex 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.08.09.503303v1?rss=1"
</link>
<description><![CDATA[
Neural population activity determines the timing of synaptic inputs, which arrive to dendrites, cell bodies and axon initial segments (AISs) of cortical neurons. Action potential initiation in the AIS (AIS-APs) is driven by input integration, and the phase preference of AIS-APs during network oscillations is characteristic to cell classes. Distal regions of cortical axons do not receive synaptic inputs, yet experimental induction protocols can trigger retroaxonal action potentials (RA-APs) in axons distal from the soma. We report spontaneously occurring RAAPs in human and rodent cortical interneurons that appear uncorrelated to inputs and population activity. Network linked triggering of AIS-APs versus input independent timing of RA-APs of the same interneurons result in disparate temporal contribution of a single cell to in vivo network operation through perisomatic and distal axonal firing.

One-Sentence SummaryNetwork linked triggering of AIS-APs versus input independent timing of RA-APs of the same interneurons result in disparate temporal contribution of a single cell to in vivo network operation.
]]></description>
<dc:creator>Rozsa, M.</dc:creator>
<dc:creator>Toth, M.</dc:creator>
<dc:creator>Olah, G.</dc:creator>
<dc:creator>Baka, J.</dc:creator>
<dc:creator>Lakovics, R.</dc:creator>
<dc:creator>Barzo, P.</dc:creator>
<dc:creator>Tamas, G.</dc:creator>
<dc:date>2022-08-10</dc:date>
<dc:identifier>doi:10.1101/2022.08.09.503303</dc:identifier>
<dc:title><![CDATA[Temporal disparity of action potentials triggered in axon initial segments and distal axons in the neocortex]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-08-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.11.29.518317v1?rss=1">
<title>
<![CDATA[
Human voltage-gated Na+ and K+ channel properties underlie sustained fast AP signaling 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.11.29.518317v1?rss=1"
</link>
<description><![CDATA[
Human cortical pyramidal neurons are large, have extensive dendritic trees, and yet have surprisingly fast input-output properties: rapid subthreshold synaptic membrane potential changes are reliably encoded in timing of action potentials (APs). Here, we tested whether biophysical properties of voltage-gated sodium (Na+) and potassium (K+) currents in human pyramidal neurons can explain their fast input-output properties. Human Na+ and K+ currents exhibited more depolarized voltage-dependence, slower inactivation and faster recovery from inactivation compared with their mouse counterparts. Computational modeling showed that despite lower Na+ channel densities in human neurons, the biophysical properties of Na+ channels resulted in higher channel availability and contributed to fast AP kinetics stability. Finally, human Na+ channel properties also resulted in a larger dynamic range for encoding of subthreshold membrane potential changes. Thus, biophysical adaptations of voltage-gated Na+ and K+ channels enable fast input-output properties of large human pyramidal neurons.

One-Sentence SummaryBiophysical properties of Na+ and K+ ion channels enable human neurons to reliably encode fast inputs into output.
]]></description>
<dc:creator>Wilbers, R.</dc:creator>
<dc:creator>Metodieva, V. D.</dc:creator>
<dc:creator>Duverdin, S.</dc:creator>
<dc:creator>Heyer, D. B.</dc:creator>
<dc:creator>Galakhova, A. A.</dc:creator>
<dc:creator>Mertens, E. J.</dc:creator>
<dc:creator>Versluis, T. D.</dc:creator>
<dc:creator>Baayen, J. C.</dc:creator>
<dc:creator>Idema, S.</dc:creator>
<dc:creator>Noske, D. P.</dc:creator>
<dc:creator>Verburg, N.</dc:creator>
<dc:creator>Willemse, R. B.</dc:creator>
<dc:creator>de Witt Hamer, P. C.</dc:creator>
<dc:creator>Kole, M. H. P.</dc:creator>
<dc:creator>de Kock, C. P. J.</dc:creator>
<dc:creator>Mansvelder, H. D.</dc:creator>
<dc:creator>Goriounova, N. A.</dc:creator>
<dc:date>2022-12-02</dc:date>
<dc:identifier>doi:10.1101/2022.11.29.518317</dc:identifier>
<dc:title><![CDATA[Human voltage-gated Na+ and K+ channel properties underlie sustained fast AP signaling]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-12-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.10.29.514375v1?rss=1">
<title>
<![CDATA[
Whole Human-Brain Mapping of Single Cortical Neurons for Profiling Morphological Diversity and Stereotypy 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.10.29.514375v1?rss=1"
</link>
<description><![CDATA[
Quantification of individual cells morphology and their distribution at the whole brain scale is essential to understand the structure and diversity of cell types. Despite recent technological advances, especially single cell labeling and whole brain imaging, for many prevailing animal models, it is exceedingly challenging to reuse similar technologies to study human brains. Here we propose Adaptive Cell Tomography (ACTomography), a low-cost, high-throughput, high-efficacy tomography approach, based on adaptive targeting of individual cells suitable for human-brain scale modeling of single neurons to characterize their 3-D structures, statistical distributions, and extensible for other cellular features. Specifically, we established a platform to inject dyes into cortical neurons in surgical tissues of 18 patients with brain tumors or other conditions and 1 donated fresh postmortem brain. We collected 3-D images of 1746 cortical neurons, of which 852 neurons were subsequentially reconstructed to quantify their local dendritic morphology, and mapped to standard atlases both computationally and semantically. In our data, human neurons are more diverse across brain regions than by subject age or gender. The strong stereotypy within cohorts of brain regions allows generating a statistical tensor-field of neuron morphology to characterize 3-D anatomical modularity of a human brain.
]]></description>
<dc:creator>Han, X.</dc:creator>
<dc:creator>Guo, S.</dc:creator>
<dc:creator>Ji, N.</dc:creator>
<dc:creator>Li, T.</dc:creator>
<dc:creator>Liu, J.</dc:creator>
<dc:creator>Ye, X.</dc:creator>
<dc:creator>Wang, Y.</dc:creator>
<dc:creator>Yun, Z.</dc:creator>
<dc:creator>Xiong, F.</dc:creator>
<dc:creator>Rong, J.</dc:creator>
<dc:creator>Liu, D.</dc:creator>
<dc:creator>Ma, H.</dc:creator>
<dc:creator>Wang, Y.</dc:creator>
<dc:creator>Huang, Y.</dc:creator>
<dc:creator>Zhang, P.</dc:creator>
<dc:creator>Wu, W.</dc:creator>
<dc:creator>Ding, L.</dc:creator>
<dc:creator>Hawrylycz, M.</dc:creator>
<dc:creator>Lein, E.</dc:creator>
<dc:creator>Ascoli, G. A.</dc:creator>
<dc:creator>Xie, W.</dc:creator>
<dc:creator>Liu, L.</dc:creator>
<dc:creator>Zhang, L.</dc:creator>
<dc:creator>Peng, H.</dc:creator>
<dc:date>2022-10-30</dc:date>
<dc:identifier>doi:10.1101/2022.10.29.514375</dc:identifier>
<dc:title><![CDATA[Whole Human-Brain Mapping of Single Cortical Neurons for Profiling Morphological Diversity and Stereotypy]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-10-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.11.29.518193v1?rss=1">
<title>
<![CDATA[
Structural and functional specializations of human fast spiking neurons support fast cortical signaling 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.11.29.518193v1?rss=1"
</link>
<description><![CDATA[
Fast spiking interneurons (FSINs) provide fast inhibition that synchronizes neuronal activity and is critical for cognitive function. Fast synchronization frequencies are evolutionary conserved in the expanded human neocortex, despite larger neuron-to-neuron distances that challenge fast input-output transfer functions of FSINs. Here, we test in human neurons from neurosurgery tissue which mechanistic specializations of human FSINs explain their fast-signaling properties in human cortex. With morphological reconstructions, multi-patch recordings, and biophysical modeling we find that despite three-fold longer dendritic path, human FSINs maintain fast inhibition between connected pyramidal neurons through several mechanisms: stronger synapse strength of excitatory inputs, larger dendrite diameter with reduced complexity, faster AP initiation, and faster and larger inhibitory output, while Na+ current activation/inactivation properties are similar. These adaptations underlie short input-output delays in fast inhibition of human pyramidal neurons through FSINs, explaining how cortical synchronization frequencies are conserved despite expanded and sparse network topology of human cortex.

Teaser/one-sentence summarySpecializations of fast spiking human neurons ensure fast signaling in human cortex.
]]></description>
<dc:creator>Wilbers, R.</dc:creator>
<dc:creator>Galakhova, A. A.</dc:creator>
<dc:creator>Heistek, T. S.</dc:creator>
<dc:creator>Metodieva, V. D.</dc:creator>
<dc:creator>Hagemann, J.</dc:creator>
<dc:creator>Heyer, D. B.</dc:creator>
<dc:creator>Mertens, E. J.</dc:creator>
<dc:creator>Deng, S.</dc:creator>
<dc:creator>Idema, S.</dc:creator>
<dc:creator>de Witt Hamer, P. C.</dc:creator>
<dc:creator>Noske, D. P.</dc:creator>
<dc:creator>van Schie, P.</dc:creator>
<dc:creator>Kommers, I.</dc:creator>
<dc:creator>Luan, G.</dc:creator>
<dc:creator>Li, T.</dc:creator>
<dc:creator>Shu, Y.</dc:creator>
<dc:creator>de Kock, C. P. J.</dc:creator>
<dc:creator>Mansvelder, H. D.</dc:creator>
<dc:creator>Goriounova, N. A.</dc:creator>
<dc:date>2022-11-29</dc:date>
<dc:identifier>doi:10.1101/2022.11.29.518193</dc:identifier>
<dc:title><![CDATA[Structural and functional specializations of human fast spiking neurons support fast cortical signaling]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-11-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.09.30.510346v1?rss=1">
<title>
<![CDATA[
A single-cell multi-omic atlas spanning the adult rhesus macaque brain 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.09.30.510346v1?rss=1"
</link>
<description><![CDATA[
Cataloging the diverse cellular architecture of the primate brain is crucial for understanding cognition, behavior and disease in humans. Here, we generated a brain-wide single-cell multimodal molecular atlas of the rhesus macaque brain. Altogether, we profiled 2.58M transcriptomes and 1.59M epigenomes from single nuclei sampled from 30 regions across the adult brain. Cell composition differed extensively across the brain, revealing cellular signatures of region-specific functions. We also identified 1.19M candidate regulatory elements, many novel, allowing us to explore the landscape of cis-regulatory grammar and neurological disease risk in a cell-type-specific manner. Together, this multi-omic atlas provides an open resource for investigating the evolution of the human brain and identifying novel targets for disease interventions.
]]></description>
<dc:creator>Chiou, K. L.</dc:creator>
<dc:creator>Huang, X.</dc:creator>
<dc:creator>Bohlen, M. O.</dc:creator>
<dc:creator>Tremblay, S.</dc:creator>
<dc:creator>O'Day, D. R.</dc:creator>
<dc:creator>Spurrell, C. H.</dc:creator>
<dc:creator>Gogate, A. A.</dc:creator>
<dc:creator>Zintel, T. M.</dc:creator>
<dc:creator>Cayo Biobank Research Unit,</dc:creator>
<dc:creator>Andrews, M. G.</dc:creator>
<dc:creator>Martinez, M. I.</dc:creator>
<dc:creator>Starita, L. M.</dc:creator>
<dc:creator>Montague, M. J.</dc:creator>
<dc:creator>Platt, M. L.</dc:creator>
<dc:creator>Shendure, J.</dc:creator>
<dc:creator>Snyder-Mackler, N.</dc:creator>
<dc:date>2022-10-03</dc:date>
<dc:identifier>doi:10.1101/2022.09.30.510346</dc:identifier>
<dc:title><![CDATA[A single-cell multi-omic atlas spanning the adult rhesus macaque brain]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-10-03</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.10.14.512250v1?rss=1">
<title>
<![CDATA[
Multi-omic profiling of the developing human cerebral cortex at the single cell level 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.10.14.512250v1?rss=1"
</link>
<description><![CDATA[
The cellular complexity of the human brain is established via dynamic changes in gene expression throughout development that is mediated, in part, by the spatiotemporal activity of cis-regulatory elements. We simultaneously profiled gene expression and chromatin accessibility in 45,549 cortical nuclei across 6 broad developmental time-points from fetus to adult. We identified cell-type specific domains in which chromatin accessibility is highly correlated with gene expression. Differentiation pseudotime trajectory analysis indicates that chromatin accessibility at cis-regulatory elements precedes transcription and that dynamic changes in chromatin structure play a critical role in neuronal lineage commitment. In addition, we mapped cell-type and temporally specific genetic loci implicated in neuropsychiatric traits, including schizophrenia and bipolar disorder. Together, our results describe the complex regulation of cell composition at critical stages in lineage determination, serve as a developmental blueprint of the human brain and shed light on the impact of spatiotemporal alterations in gene expression on neuropsychiatric disease.

One-Sentence SummarySimultaneous profiling of gene expression and chromatin accessibility in single nuclei from 6 developmental time-points sheds light on cell fate determination in the human cerebral cortex and on the molecular basis of neuropsychiatric disease.
]]></description>
<dc:creator>Zhu, K.</dc:creator>
<dc:creator>Bendl, J.</dc:creator>
<dc:creator>Rahman, S.</dc:creator>
<dc:creator>Vicari, J. M.</dc:creator>
<dc:creator>Coleman, C.</dc:creator>
<dc:creator>Clarence, T.</dc:creator>
<dc:creator>Latouche, O.</dc:creator>
<dc:creator>Tsankova, N. M.</dc:creator>
<dc:creator>Li, A.</dc:creator>
<dc:creator>Brennand, K. J.</dc:creator>
<dc:creator>Lee, D.</dc:creator>
<dc:creator>Yuan, G.</dc:creator>
<dc:creator>Fullard, J. F.</dc:creator>
<dc:creator>Roussos, P.</dc:creator>
<dc:date>2022-10-17</dc:date>
<dc:identifier>doi:10.1101/2022.10.14.512250</dc:identifier>
<dc:title><![CDATA[Multi-omic profiling of the developing human cerebral cortex at the single cell level]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-10-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.10.18.512442v1?rss=1">
<title>
<![CDATA[
A marmoset brain cell census reveals persistent influence of developmental origin on neurons 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.10.18.512442v1?rss=1"
</link>
<description><![CDATA[
The mammalian brain is composed of many brain structures, each with its own ontogenetic and developmental history. Transcriptionally-based cell type taxonomies reveal cell type composition and similarity relationships within and across brain structures. We sampled over 2.4 million brain cells across 18 locations in the common marmoset, a New World monkey primed for genetic engineering, and used single-nucleus RNA sequencing to examine global gene expression patterns of cell types within and across brain structures. Our results indicate that there is generally a high degree of transcriptional similarity between GABAergic and glutamatergic neurons found in the same brain structure, and there are generally few shared molecular features between neurons that utilize the same neurotransmitter but reside in different brain structures. We also show that in many cases the transcriptional identities of cells are intrinsically retained from their birthplaces, even when they migrate beyond their cephalic compartments. Thus, the adult transcriptomic identity of most neuronal types appears to be shaped much more by their developmental identity than by their primary neurotransmitter signaling repertoire. Using quantitative mapping of single molecule FISH (smFISH) for markers for GABAergic interneurons, we found that the similar types (e.g. PVALB+ interneurons) have distinct biodistributions in the striatum and neocortex. Interneuron types follow medio-lateral gradients in striatum but form complex distributions across the neocortex that are not described by simple gradients. Lateral prefrontal areas in marmoset are distinguished by high relative proportions of VIP+ neurons. We further used cell-type-specific enhancer driven AAV-GFP to visualize the morphology of molecularly-resolved interneuron classes in neocortex and striatum, including the previously discovered novel primate-specific TAC3+ striatal interneurons. Our comprehensive analyses highlight how lineage and functional class contribute to the transcriptional identity and biodistribution of primate brain cell types.

One-Sentence SummaryAdult primate neurons are imprinted by their region of origin, more so than by their functional identity.
]]></description>
<dc:creator>Krienen, F. M.</dc:creator>
<dc:creator>Levandowski, K. M.</dc:creator>
<dc:creator>Zaniewski, H.</dc:creator>
<dc:creator>del Rosario, R. C.</dc:creator>
<dc:creator>Schroeder, M. E.</dc:creator>
<dc:creator>Goldman, M.</dc:creator>
<dc:creator>Lutservitz, A.</dc:creator>
<dc:creator>Zhang, Q.</dc:creator>
<dc:creator>Li, K. X.</dc:creator>
<dc:creator>Beja-Glasser, V. F.</dc:creator>
<dc:creator>Sharma, J.</dc:creator>
<dc:creator>Shin, T. W.</dc:creator>
<dc:creator>Mauermann, A.</dc:creator>
<dc:creator>Wysoker, A.</dc:creator>
<dc:creator>Nemesh, J.</dc:creator>
<dc:creator>Kashin, S.</dc:creator>
<dc:creator>Vergara, J.</dc:creator>
<dc:creator>Chelini, G.</dc:creator>
<dc:creator>Dimidschstein, J.</dc:creator>
<dc:creator>Berretta, S.</dc:creator>
<dc:creator>Boyden, E.</dc:creator>
<dc:creator>McCarroll, S. A.</dc:creator>
<dc:creator>Feng, G.</dc:creator>
<dc:date>2022-10-19</dc:date>
<dc:identifier>doi:10.1101/2022.10.18.512442</dc:identifier>
<dc:title><![CDATA[A marmoset brain cell census reveals persistent influence of developmental origin on neurons]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-10-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.11.30.518285v1?rss=1">
<title>
<![CDATA[
Epigenomic complexity of the human brain revealed by single-cell DNA methylomes and 3D genome structures 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.11.30.518285v1?rss=1"
</link>
<description><![CDATA[
Delineating the gene regulatory programs underlying complex cell types is fundamental for understanding brain functions in health and disease. Here, we comprehensively examine human brain cell epigenomes by probing DNA methylation and chromatin conformation at single-cell resolution in over 500,000 cells from 46 brain regions. We identified 188 cell types and characterized their molecular signatures. Integrative analyses revealed concordant changes in DNA methylation, chromatin accessibility, chromatin organization, and gene expression across cell types, cortical areas, and basal ganglia structures. With these resources, we developed scMCodes that reliably predict brain cell types using their methylation status at select genomic sites. This multimodal epigenomic brain cell atlas provides new insights into the complexity of cell type-specific gene regulation in the adult human brain.
]]></description>
<dc:creator>Tian, W.</dc:creator>
<dc:creator>Zhou, J.</dc:creator>
<dc:creator>Bartlett, A.</dc:creator>
<dc:creator>Zeng, Q.</dc:creator>
<dc:creator>Liu, H.</dc:creator>
<dc:creator>Castanon, R. G.</dc:creator>
<dc:creator>Kenworthy, M.</dc:creator>
<dc:creator>Altshul, J.</dc:creator>
<dc:creator>Valadon, C.</dc:creator>
<dc:creator>Aldridge, A.</dc:creator>
<dc:creator>Nery, J. R.</dc:creator>
<dc:creator>Chen, H.</dc:creator>
<dc:creator>Xu, J.</dc:creator>
<dc:creator>Johnson, N. D.</dc:creator>
<dc:creator>Lucero, J.</dc:creator>
<dc:creator>Osteen, J. K.</dc:creator>
<dc:creator>Emerson, N.</dc:creator>
<dc:creator>Rink, J.</dc:creator>
<dc:creator>Lee, J.</dc:creator>
<dc:creator>Li, Y.</dc:creator>
<dc:creator>Siletti, K.</dc:creator>
<dc:creator>Liem, M.</dc:creator>
<dc:creator>Claffey, N.</dc:creator>
<dc:creator>OConnor, C.</dc:creator>
<dc:creator>Yanny, A. M.</dc:creator>
<dc:creator>Nyhus, J.</dc:creator>
<dc:creator>Dee, N.</dc:creator>
<dc:creator>Casper, T.</dc:creator>
<dc:creator>Shapovalova, N.</dc:creator>
<dc:creator>Hirschstein, D.</dc:creator>
<dc:creator>Hodge, R.</dc:creator>
<dc:creator>Levi, B. P.</dc:creator>
<dc:creator>Keene, C. D.</dc:creator>
<dc:creator>Linnarsson, S.</dc:creator>
<dc:creator>Lein, E.</dc:creator>
<dc:creator>Ren, B.</dc:creator>
<dc:creator>Behrens, M. M.</dc:creator>
<dc:creator>Ecker, J. R.</dc:creator>
<dc:date>2022-12-01</dc:date>
<dc:identifier>doi:10.1101/2022.11.30.518285</dc:identifier>
<dc:title><![CDATA[Epigenomic complexity of the human brain revealed by single-cell DNA methylomes and 3D genome structures]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-12-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.10.20.464979v1?rss=1">
<title>
<![CDATA[
A multimodal imaging and analysis pipeline for creating a cellular census of the human cerebral cortex 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.10.20.464979v1?rss=1"
</link>
<description><![CDATA[
Cells are not uniformly distributed in the human cerebral cortex. Rather, they are arranged in a regional and laminar fashion that span a range of scales. Here we demonstrate an innovative imaging and analysis pipeline to construct a reliable cell census across the human cerebral cortex. Magnetic resonance imaging (MRI) is used to establish a macroscopic reference coordinate system of laminar and cytoarchitectural boundaries. Cell counting is obtained with both traditional immunohistochemistry, to provide a stereological gold-standard, and with a custom-made inverted confocal light-sheet fluorescence microscope (LSFM) for 3D imaging at cellular resolution. Finally, mesoscale optical coherence tomography (OCT) enables the registration of the distorted histological cell typing obtained with LSFM to the MRI-based atlas coordinate system.
]]></description>
<dc:creator>Costantini, I.</dc:creator>
<dc:creator>Morgan, L.</dc:creator>
<dc:creator>Yang, J.</dc:creator>
<dc:creator>Balbastre, Y.</dc:creator>
<dc:creator>Varadarajan, D.</dc:creator>
<dc:creator>Pesce, L.</dc:creator>
<dc:creator>Scardigli, M.</dc:creator>
<dc:creator>Mazzamuto, G.</dc:creator>
<dc:creator>Gavryusev, V.</dc:creator>
<dc:creator>Castelli, F. M.</dc:creator>
<dc:creator>Roffilli, M.</dc:creator>
<dc:creator>Silvestri, L.</dc:creator>
<dc:creator>Laffey, J.</dc:creator>
<dc:creator>Raia, S.</dc:creator>
<dc:creator>Varghese, M.</dc:creator>
<dc:creator>Wicinski, B.</dc:creator>
<dc:creator>Chang, S.</dc:creator>
<dc:creator>Chen I-Chun, A.</dc:creator>
<dc:creator>Wang, H.</dc:creator>
<dc:creator>Cordero, D.</dc:creator>
<dc:creator>Vera, M.</dc:creator>
<dc:creator>Nolan, J.</dc:creator>
<dc:creator>Nestor, K.</dc:creator>
<dc:creator>Mora, J.</dc:creator>
<dc:creator>Iglesias, J. E.</dc:creator>
<dc:creator>Pallares, E. G.</dc:creator>
<dc:creator>Evancic, K.</dc:creator>
<dc:creator>Augustinack, J.</dc:creator>
<dc:creator>Fogarty, M.</dc:creator>
<dc:creator>Dalca, A. V.</dc:creator>
<dc:creator>Frosch, M.</dc:creator>
<dc:creator>Magnain, C.</dc:creator>
<dc:creator>Frost, R.</dc:creator>
<dc:creator>van der Kouwe, A.</dc:creator>
<dc:creator>Chen, S.-C.</dc:creator>
<dc:creator>Boas, D. A.</dc:creator>
<dc:creator>Pavone, F. S.</dc:creator>
<dc:creator>Fischl, B.</dc:creator>
<dc:creator>Hof, P. R.</dc:creator>
<dc:date>2021-10-21</dc:date>
<dc:identifier>doi:10.1101/2021.10.20.464979</dc:identifier>
<dc:title><![CDATA[A multimodal imaging and analysis pipeline for creating a cellular census of the human cerebral cortex]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-10-21</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.07.26.501598v1?rss=1">
<title>
<![CDATA[
Early-childhood inflammation blunts the transcriptional maturation of cerebellar neurons 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.07.26.501598v1?rss=1"
</link>
<description><![CDATA[
Inflammation early in life is a clinically established risk factor for autism spectrum disorders and schizophrenia, yet the impact of inflammation on human brain development is poorly understood. The cerebellum undergoes protracted postnatal maturation, making it especially susceptible to perturbations contributing to risk of neurodevelopmental disorders. Here, using single-cell genomics, we characterize the postnatal development of cerebellar neurons and glia in 1-5-year-old children, comparing those who died while experiencing inflammation vs. non-inflamed controls. Our analyses reveal that inflammation and postnatal maturation are associated with extensive, overlapping transcriptional changes primarily in two subtypes of inhibitory neurons: Purkinje neurons and Golgi neurons. Immunohistochemical analysis of a subset of these brains revealed no change to Purkinje neuron soma size but evidence for increased activation of microglia in those subjects experiencing inflammation. Maturation- and inflammation-associated genes were strongly enriched for those implicated in neurodevelopmental disorders. A gene regulatory network model integrating cell type-specific gene expression and chromatin accessibility identified seven temporally specific gene networks in Purkinje neurons and suggested that the effects of inflammation correspond to blunted cellular maturation.

One Sentence SummaryPost-mortem cerebelli from children who perished under conditions that included inflammation exhibit transcriptomic changes consistent with blunted maturation of Purkinje neurons compared to those who succumbed to sudden accidental death.
]]></description>
<dc:creator>Ament, S. A.</dc:creator>
<dc:creator>Cortes-Gutierrez, M.</dc:creator>
<dc:creator>Herb, B. R.</dc:creator>
<dc:creator>Mocci, E.</dc:creator>
<dc:creator>Colantuoni, C.</dc:creator>
<dc:creator>McCarthy, M. M.</dc:creator>
<dc:date>2022-07-26</dc:date>
<dc:identifier>doi:10.1101/2022.07.26.501598</dc:identifier>
<dc:title><![CDATA[Early-childhood inflammation blunts the transcriptional maturation of cerebellar neurons]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-07-26</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.07.20.453090v1?rss=1">
<title>
<![CDATA[
Single-cell genomics reveals region-specific developmental trajectories underlying neuronal diversity in the prenatal human hypothalamus 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.07.20.453090v1?rss=1"
</link>
<description><![CDATA[
The development and diversity of neuronal subtypes in the human hypothalamus has been insufficiently characterized. We sequenced the transcriptomes of 40,927 cells from the prenatal human hypothalamus spanning from 6 to 25 gestational weeks and 25,424 mature neurons in regions of the adult human hypothalamus, revealing a temporal trajectory from proliferative stem cell populations to mature neurons and glia. Developing hypothalamic neurons followed branching trajectories leading to 170 transcriptionally distinct neuronal subtypes in ten hypothalamic nuclei in the adult. The uniqueness of hypothalamic neuronal lineages was examined developmentally by comparing excitatory lineages present in cortex and inhibitory lineages in ganglionic eminence from the same individuals, revealing both distinct and shared drivers of neuronal maturation across the human forebrain. Cross-species comparisons to the mouse hypothalamus identified human-specific POMC populations expressing unique combinations of transcription factors and neuropeptides. These results provide the first comprehensive transcriptomic view of human hypothalamus development at cellular resolution.

One-Sentence SummaryUsing single-cell genomics, we reconstructed the developmental lineages by which precursor populations give rise to 170 distinct neuronal subtypes in the human hypothalamus.
]]></description>
<dc:creator>Herb, B.</dc:creator>
<dc:creator>Glover, H. J.</dc:creator>
<dc:creator>Bhaduri, A.</dc:creator>
<dc:creator>Casella, A. M.</dc:creator>
<dc:creator>Bale, T. L.</dc:creator>
<dc:creator>Kriegstein, A.</dc:creator>
<dc:creator>Doege, C.</dc:creator>
<dc:creator>Ament, S. A.</dc:creator>
<dc:date>2021-07-20</dc:date>
<dc:identifier>doi:10.1101/2021.07.20.453090</dc:identifier>
<dc:title><![CDATA[Single-cell genomics reveals region-specific developmental trajectories underlying neuronal diversity in the prenatal human hypothalamus]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-07-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.03.13.484171v1?rss=1">
<title>
<![CDATA[
Integrated platform for multi-scale molecular imaging andphenotyping of the human brain 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.03.13.484171v1?rss=1"
</link>
<description><![CDATA[
Understanding cellular architectures and their connectivity is essential for interrogating system function and dysfunction. However, we lack technologies for mapping the multi-scale details of individual cells in the human organ-scale system. To address this challenge, we developed a platform that simultaneously extracts spatial, molecular, morphological, and connectivity information of individual cells from the same human brain, by integrating novel chemical, mechanical, and computational tools. The platform includes three key tools: (i) a vibrating microtome for ultra-precision slicing of large-scale tissues without losing cellular connectivity (MEGAtome), (ii) a polymer hydrogel-based tissue processing technology for multiplexed multiscale imaging of human organ-scale tissues (mELAST), and (iii) a computational pipeline for reconstructing 3D connectivity across multiple brain slabs (UNSLICE). We demonstrated the transformative potential of our platform by analyzing human Alzheimers disease pathology at multiple scales and demonstrating scalable neural connectivity mapping in the human brain.

One-Sentence SummaryWe developed an integrated, scalable platform for highly multiplexed, multi-scale phenotyping and connectivity mapping in the same human brain tissue, which incorporated novel tissue processing, labeling, imaging, and computational technologies.
]]></description>
<dc:creator>Park, J.</dc:creator>
<dc:creator>Wang, J.</dc:creator>
<dc:creator>Guan, W.</dc:creator>
<dc:creator>Kamentsky, L.</dc:creator>
<dc:creator>Evans, N. B.</dc:creator>
<dc:creator>Gjesteby, L.</dc:creator>
<dc:creator>Pollack, D.</dc:creator>
<dc:creator>Choi, S. W.</dc:creator>
<dc:creator>Snyder, M.</dc:creator>
<dc:creator>Chavez, D.</dc:creator>
<dc:creator>Tian, Y.</dc:creator>
<dc:creator>Su-Arcaro, C.</dc:creator>
<dc:creator>Yun, D. H.</dc:creator>
<dc:creator>Zhao, C.</dc:creator>
<dc:creator>Brattain, L.</dc:creator>
<dc:creator>Frosh, M. P.</dc:creator>
<dc:creator>Chung, K.</dc:creator>
<dc:date>2022-03-15</dc:date>
<dc:identifier>doi:10.1101/2022.03.13.484171</dc:identifier>
<dc:title><![CDATA[Integrated platform for multi-scale molecular imaging andphenotyping of the human brain]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-03-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.06.24.497299v1?rss=1">
<title>
<![CDATA[
An Atlas of Cells in the Human Tonsil 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.06.24.497299v1?rss=1"
</link>
<description><![CDATA[
Palatine tonsils are secondary lymphoid organs representing the first line of immunological defense against inhaled or ingested pathogens. Here, we present a comprehensive census of cell types forming the human tonsil by applying single-cell transcriptome, epigenome, proteome and adaptive immune repertoire sequencing as well as spatial transcriptomics, resulting in an atlas of >357,000 cells. We provide a glossary of 121 annotated cell types and states, and disentangle gene regulatory mechanisms that drive cells through specialized lineage trajectories. Exemplarily, we stratify multiple tonsil-resident myeloid slancyte subtypes, establish a distant BCL6 superenhancer as locally active in both follicle-associated T and B cells, and describe SIX5 as a potentially novel transcriptional regulator of plasma cell maturation. Further, our atlas is a reference map to understand alterations observed in disease. Here, we discover immune-phenotype plasticity in tumoral cells and microenvironment shifts of mantle cell lymphomas (MCL). To facilitate such reference-based analysis, we develop HCATonsilData and SLOcatoR, a computational framework that provides programmatic and modular access to our dataset; and allows the straightforward annotation of future single-cell profiles from secondary lymphoid organs.
]]></description>
<dc:creator>Massoni-Badosa, R.</dc:creator>
<dc:creator>Soler-Vila, P.</dc:creator>
<dc:creator>Aguilar-Fernandez, S.</dc:creator>
<dc:creator>Nieto, J. C.</dc:creator>
<dc:creator>Elosua-Bayes, M.</dc:creator>
<dc:creator>Marchese, D.</dc:creator>
<dc:creator>Kulis, M.</dc:creator>
<dc:creator>Vilas-Zornoza, A.</dc:creator>
<dc:creator>Bühler, M. M.</dc:creator>
<dc:creator>Rashmi, S.</dc:creator>
<dc:creator>Alsinet, C.</dc:creator>
<dc:creator>Caratu, G.</dc:creator>
<dc:creator>Moutinho, C.</dc:creator>
<dc:creator>Ruiz, S.</dc:creator>
<dc:creator>Lorden, P.</dc:creator>
<dc:creator>Lunazzi, G.</dc:creator>
<dc:creator>Colomer, D.</dc:creator>
<dc:creator>Frigola, G.</dc:creator>
<dc:creator>Blevins, W.</dc:creator>
<dc:creator>Palomino, S.</dc:creator>
<dc:creator>Gomez-Cabrero, D.</dc:creator>
<dc:creator>Aguirre, X.</dc:creator>
<dc:creator>Weniger, M. A.</dc:creator>
<dc:creator>Marini, F.</dc:creator>
<dc:creator>Cervera-Paz, F. J.</dc:creator>
<dc:creator>Baptista, P. M.</dc:creator>
<dc:creator>Vilaseca, I.</dc:creator>
<dc:creator>Prosper, F.</dc:creator>
<dc:creator>Küppers, R.</dc:creator>
<dc:creator>Gut, I. G.</dc:creator>
<dc:creator>Campo, E.</dc:creator>
<dc:creator>Martin-Subero, J. I.</dc:creator>
<dc:creator>Heyn, H.</dc:creator>
<dc:date>2022-06-26</dc:date>
<dc:identifier>doi:10.1101/2022.06.24.497299</dc:identifier>
<dc:title><![CDATA[An Atlas of Cells in the Human Tonsil]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-06-26</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.10.14.561800v1?rss=1">
<title>
<![CDATA[
Quantification of the escape from X chromosome inactivation with the million cell-scale human single-cell omics datasets reveals heterogeneity of escape across cell types and tissues 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.10.14.561800v1?rss=1"
</link>
<description><![CDATA[
One of the two X chromosomes of females is silenced through X chromosome inactivation (XCI) to compensate for the difference in the dosage between sexes. Among the X-linked genes, several genes escape from XCI, which could contribute to the differential gene expression between the sexes. However, the differences in the escape across cell types and tissues are still poorly characterized because no methods could directly evaluate the escape under a physiological condition at the cell-cluster resolution with versatile technology. Here, we developed a method, single-cell Level inactivated X chromosome mapping (scLinaX), which directly quantifies relative gene expression from the inactivated X chromosome with droplet-based single-cell RNA-sequencing (scRNA-seq) data. The scLinaX and differentially expressed genes analyses with the scRNA-seq datasets of [~]1,000,000 blood cells consistently identified the relatively strong degree of escape in lymphocytes compared to myeloid cells. An extension of scLinaX for multi-modal datasets, scLinaX-multi, suggested a stronger degree of escape in lymphocytes than myeloid cells at the chromatin-accessibility level with a 10X multiome dataset. The scLinaX analysis with the human multiple-organ scRNA-seq datasets also identified the relatively strong degree of escape from XCI in lymphoid tissues and lymphocytes. Finally, effect size comparisons of genome-wide association studies between sexes identified the larger effect sizes of the PRKX gene locus-lymphocyte counts association in females than males. This could suggest evidence of the underlying impact of escape on the genotype-phenotype association in humans. Overall, scLinaX and the quantified catalog of escape identified the heterogeneity of escape across cell types and tissues and would contribute to expanding the current understanding of the XCI, escape, and sex differences in gene regulation.
]]></description>
<dc:creator>Tomofuji, Y.</dc:creator>
<dc:creator>Edahiro, R.</dc:creator>
<dc:creator>Shirai, Y.</dc:creator>
<dc:creator>Kock, K. H.</dc:creator>
<dc:creator>Sonehara, K.</dc:creator>
<dc:creator>Wang, Q. S.</dc:creator>
<dc:creator>Namba, S.</dc:creator>
<dc:creator>Moody, J.</dc:creator>
<dc:creator>Ando, Y.</dc:creator>
<dc:creator>Suzuki, A.</dc:creator>
<dc:creator>Yata, T.</dc:creator>
<dc:creator>Ogawa, K.</dc:creator>
<dc:creator>Namkoong, H.</dc:creator>
<dc:creator>Lin, Q. X. X.</dc:creator>
<dc:creator>Buyamin, E. V.</dc:creator>
<dc:creator>Tan, L. M.</dc:creator>
<dc:creator>Sonthalia, R.</dc:creator>
<dc:creator>Han, K. Y.</dc:creator>
<dc:creator>Tanaka, H.</dc:creator>
<dc:creator>Lee, H.</dc:creator>
<dc:creator>Asian Immune Diversity Atlas Network,</dc:creator>
<dc:creator>Japan COVID-19 Task Force,</dc:creator>
<dc:creator>The BioBank Japan Project,</dc:creator>
<dc:creator>Okuno, T.</dc:creator>
<dc:creator>Liu, B.</dc:creator>
<dc:creator>Matsuda, K.</dc:creator>
<dc:creator>Fukunaga, K.</dc:creator>
<dc:creator>Mochizuki, H.</dc:creator>
<dc:creator>Park, W.-Y.</dc:creator>
<dc:creator>Yamamoto, K.</dc:creator>
<dc:creator>Hon, C.-C.</dc:creator>
<dc:creator>Shin, J. W.</dc:creator>
<dc:creator>Prabhakar, S.</dc:creator>
<dc:creator>Kumanogoh, A.</dc:creator>
<dc:creator>Okada, Y.</dc:creator>
<dc:date>2023-10-18</dc:date>
<dc:identifier>doi:10.1101/2023.10.14.561800</dc:identifier>
<dc:title><![CDATA[Quantification of the escape from X chromosome inactivation with the million cell-scale human single-cell omics datasets reveals heterogeneity of escape across cell types and tissues]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-10-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.10.27.564191v1?rss=1">
<title>
<![CDATA[
Integrated spatial protein and RNA analysis on the same section using MICS technology 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.10.27.564191v1?rss=1"
</link>
<description><![CDATA[
Spatial Biology has evolved from the molecular characterization of microdissected cells to high throughput spatial RNA and protein expression analysis at scale. The main limitation of spatial technologies so far is the inability to resolve protein and RNA information in the same histological section. Here, we report for the first time the integration of highly multiplexed RNA and protein detection on the same tissue section. We developed a new, automated, spatial RNA detection method (RNAsky), which is based on targeted rolling circle amplification and iterative staining. We combine RNAsky with MACSima Imaging Cyclic Staining (MICS) based protein analysis and show compatibility with subsequent standard hematoxylin and eosin (H&E) staining. Using both, open-source tools and our recently developed software suite MACS(R) iQ View, we demonstrate our multiomics MICS workflow by characterizing key immune-oncology markers at subcellular resolution across normal and diseased tissues.
]]></description>
<dc:creator>Neil, E.</dc:creator>
<dc:creator>Park, D.</dc:creator>
<dc:creator>Hennessey, R. C.</dc:creator>
<dc:creator>DiBiasio, E. C.</dc:creator>
<dc:creator>DiBuono, M.</dc:creator>
<dc:creator>Lafayette, H.</dc:creator>
<dc:creator>Lloyd, E.</dc:creator>
<dc:creator>Lo, H.</dc:creator>
<dc:creator>Femel, J.</dc:creator>
<dc:creator>Makrigiorgos, A.</dc:creator>
<dc:creator>Soliman, S.</dc:creator>
<dc:creator>Mangiardi, D.</dc:creator>
<dc:creator>Praveen, P.</dc:creator>
<dc:creator>Rüberg, S.</dc:creator>
<dc:creator>Staubach, F.</dc:creator>
<dc:creator>Hindman, R.</dc:creator>
<dc:creator>Rothmann, T.</dc:creator>
<dc:creator>Meyer, H.</dc:creator>
<dc:creator>Wantenaar, T.</dc:creator>
<dc:creator>Wang, J.</dc:creator>
<dc:creator>Müller, W.</dc:creator>
<dc:creator>Pinard, R.</dc:creator>
<dc:creator>Bosio, A.</dc:creator>
<dc:date>2023-10-27</dc:date>
<dc:identifier>doi:10.1101/2023.10.27.564191</dc:identifier>
<dc:title><![CDATA[Integrated spatial protein and RNA analysis on the same section using MICS technology]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-10-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.10.07.561331v1?rss=1">
<title>
<![CDATA[
Scaling cross-tissue single-cell annotation models 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.10.07.561331v1?rss=1"
</link>
<description><![CDATA[
Identifying cellular identities (both novel and well-studied) is one of the key use cases in single-cell transcriptomics. While supervised machine learning has been leveraged to automate cell annotation predictions for some time, there has been relatively little progress both in scaling neural networks to large data sets and in constructing models that generalize well across diverse tissues and biological contexts up to whole organisms. Here, we propose scTab, an automated, feature-attention-based cell type prediction model specific to tabular data, and train it using a novel data augmentation scheme across a large corpus of single-cell RNA-seq observations (22.2 million human cells in total). In addition, scTab leverages deep ensembles for uncertainty quantification. Moreover, we account for ontological relationships between labels in the model evaluation to accommodate for differences in annotation granularity across datasets. On this large-scale corpus, we show that cross-tissue annotation requires nonlinear models and that the performance of scTab scales in terms of training dataset size as well as model size - demonstrating the advantage of scTab over current state-of-the-art linear models in this context. Additionally, we show that the proposed data augmentation schema improves model generalization. In summary, we introduce a de novo cell type prediction model for single-cell RNA-seq data that can be trained across a large-scale collection of curated datasets from a diverse selection of human tissues and demonstrate the benefits of using deep learning methods in this paradigm. Our codebase, training data, and model checkpoints are publicly available at https://github.com/theislab/scTab to further enable rigorous benchmarks of foundation models for single-cell RNA-seq data.
]]></description>
<dc:creator>Fischer, F.</dc:creator>
<dc:creator>Fischer, D. S.</dc:creator>
<dc:creator>Biederstedt, E.</dc:creator>
<dc:creator>Villani, A.-C.</dc:creator>
<dc:creator>Theis, F. J.</dc:creator>
<dc:date>2023-10-10</dc:date>
<dc:identifier>doi:10.1101/2023.10.07.561331</dc:identifier>
<dc:title><![CDATA[Scaling cross-tissue single-cell annotation models]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-10-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.10.26.564050v1?rss=1">
<title>
<![CDATA[
Pan-cancer profiling of tumor-infiltrating natural killer cells through transcriptional reference mapping 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.10.26.564050v1?rss=1"
</link>
<description><![CDATA[
The functional diversity of natural killer (NK) cell repertoires stems from differentiation, homeostatic receptor-ligand interactions, and adaptive-like responses to viral infections. Here, we generated a single-cell transcriptional reference map of healthy human blood and tissue-derived NK cells, with temporal resolution and fate-specific expression of gene regulator networks defining NK cell differentiation. Using transfer learning, transcriptomes of tumor-infiltrating NK cells from seven solid tumor types (427 patients), combined from 39 datasets, were incorporated into the reference map and interrogated for tumor microenvironment (TME)-induced perturbations. We identified six functionally distinct NK cellular states in healthy and malignant tissues, two of which were commonly enriched for across tumor types: a dysfunctional  stressed CD56bright state susceptible to TME-induced immunosuppression and a cytotoxic TME-resistant  effector CD56dim state. The ratio of  stressed CD56bright and  effector CD56dim was predictive of patient outcome in malignant melanoma and osteosarcoma. This resource may inform the design of novel NK cell therapies and can be extended endlessly through transfer learning to interrogate new datasets from experimental perturbations or disease conditions.
]]></description>
<dc:creator>Netskar, H. K.</dc:creator>
<dc:creator>Pfefferle, A.</dc:creator>
<dc:creator>Goodridge, J. P.</dc:creator>
<dc:creator>Sohlberg, E.</dc:creator>
<dc:creator>Dufva, O.</dc:creator>
<dc:creator>Teichmann, S.</dc:creator>
<dc:creator>Clancy, T.</dc:creator>
<dc:creator>horowitz, a.</dc:creator>
<dc:creator>Malmberg, K.-J.</dc:creator>
<dc:date>2023-10-30</dc:date>
<dc:identifier>doi:10.1101/2023.10.26.564050</dc:identifier>
<dc:title><![CDATA[Pan-cancer profiling of tumor-infiltrating natural killer cells through transcriptional reference mapping]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-10-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.10.27.563754v1?rss=1">
<title>
<![CDATA[
Oncogenic RAS-Pathway Activation Drives Oncofetal Reprogramming and Creates Therapeutic Vulnerabilities in Juvenile Myelomonocytic Leukemia 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.10.27.563754v1?rss=1"
</link>
<description><![CDATA[
Aberrant fetal gene expression facilitates tumor-specific cellular plasticity by hijacking molecular programs of embryogenesis1. Persistent fetal gene signatures in childhood malignancies are typically explained by their prenatal origins2-6. In contrast, reactivation of fetal gene expression is considered a consequence of oncofetal reprogramming (OFR) in adult malignancies and is associated with aggressive disease7-10. To date, OFR has not been described in the context of childhood malignancies. Here, we performed a comprehensive multi-layered molecular characterization of juvenile myelomonocytic leukemia (JMML) and identified OFR as a hallmark of aggressive JMML. We observed that hematopoietic stem cells (HSCs) aberrantly express mixed developmental programs in JMML. Expression of fetal gene signatures combined with a postnatal epigenetic landscape suggested OFR, which was validated in a JMML mouse model, demonstrating that postnatal activation of RAS signaling is sufficient to induce fetal gene signatures. Integrative analysis identified the fetal HSC maturation marker CD52 as a novel therapeutic target for aggressive JMML. Anti-CD52 treatment depleted human JMML HSCs and disrupted disease propagation in vivo. In summary, this study implicates OFR, defined as postnatal acquisition of fetal transcription signatures, in the pathobiology of a childhood malignancy. We provide evidence for the direct involvement of oncogenic RAS signaling in OFR. Finally, we demonstrate how OFR can be leveraged for the development of novel treatment strategies.

Highlights{blacksquare} Epigenomic and transcriptomic landscape of juvenile myelomonocytic leukemia (JMML) in the context of hematopoietic development.
{blacksquare}The presence of fetal transcription signatures in childhood malignancies is not indicative of a developmental maturation block.
{blacksquare}High-risk JMML is characterized by oncofetal reprogramming of postnatal hematopoietic stem cells (HSCs).
{blacksquare}RAS-pathway mutations induce fetal-like gene expression signatures in murine postnatal HSCs.
{blacksquare}The fetal maturation marker CD52 is a novel therapeutic target in high-risk JMML.
]]></description>
<dc:creator>Hartmann, M.</dc:creator>
<dc:creator>Schoenung, M.</dc:creator>
<dc:creator>Rajak, J.</dc:creator>
<dc:creator>Maurer, V.</dc:creator>
<dc:creator>Hai, L.</dc:creator>
<dc:creator>Bauer, K.</dc:creator>
<dc:creator>Hakobyan, M.</dc:creator>
<dc:creator>Staeble, S.</dc:creator>
<dc:creator>Langstein, J.</dc:creator>
<dc:creator>Jardine, L.</dc:creator>
<dc:creator>Roelz, R.</dc:creator>
<dc:creator>Bohler, S.</dc:creator>
<dc:creator>Khabirova, E.</dc:creator>
<dc:creator>Maag, A.-H.</dc:creator>
<dc:creator>Vonficht, D.</dc:creator>
<dc:creator>Lebrecht, D.</dc:creator>
<dc:creator>Bernt, K. M.</dc:creator>
<dc:creator>Tan, K.</dc:creator>
<dc:creator>Chen, C.</dc:creator>
<dc:creator>Alikarami, F.</dc:creator>
<dc:creator>Boch, T.</dc:creator>
<dc:creator>Flore, V.</dc:creator>
<dc:creator>Lutsik, P.</dc:creator>
<dc:creator>Milsom, M. D.</dc:creator>
<dc:creator>Raffel, S.</dc:creator>
<dc:creator>Buske, C.</dc:creator>
<dc:creator>Haas, S.</dc:creator>
<dc:creator>Haniffa, M.</dc:creator>
<dc:creator>Mallm, J.-P.</dc:creator>
<dc:creator>Behjati, S.</dc:creator>
<dc:creator>Bonder, M.-J.</dc:creator>
<dc:creator>Froehling, S.</dc:creator>
<dc:creator>Niemeyer, C. M.</dc:creator>
<dc:creator>Hey, J.</dc:creator>
<dc:creator>Flotho, C.</dc:creator>
<dc:creator>Plass, C.</dc:creator>
<dc:creator>Erlacher, M.</dc:creator>
<dc:creator>Schlesner, M.</dc:creator>
<dc:creator>Lipka, D. B.</dc:creator>
<dc:date>2023-11-01</dc:date>
<dc:identifier>doi:10.1101/2023.10.27.563754</dc:identifier>
<dc:title><![CDATA[Oncogenic RAS-Pathway Activation Drives Oncofetal Reprogramming and Creates Therapeutic Vulnerabilities in Juvenile Myelomonocytic Leukemia]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-11-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.11.07.566105v1?rss=1">
<title>
<![CDATA[
Integrated multi-omics single cell atlas of the human retina 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.11.07.566105v1?rss=1"
</link>
<description><![CDATA[
Single-cell sequencing has revolutionized the scale and resolution of molecular profiling of tissues and organs. Here, we present an integrated multimodal reference atlas of the most accessible portion of the mammalian central nervous system, the retina. We compiled around 2.4 million cells from 55 donors, including 1.4 million unpublished data points, to create a comprehensive human retina cell atlas (HRCA) of transcriptome and chromatin accessibility, unveiling over 110 types. Engaging the retina community, we annotated each cluster, refined the Cell Ontology for the retina, identified distinct marker genes, and characterized cis-regulatory elements and gene regulatory networks (GRNs) for these cell types. Our analysis uncovered intriguing differences in transcriptome, chromatin, and GRNs across cell types. In addition, we modeled changes in gene expression and chromatin openness across gender and age. This integrated atlas also enabled the fine-mapping of GWAS and eQTL variants. Accessible through interactive browsers, this multimodal cross-donor and cross-lab HRCA, can facilitate a better understanding of retinal function and pathology.
]]></description>
<dc:creator>Li, J.</dc:creator>
<dc:creator>Wang, J.</dc:creator>
<dc:creator>Ibarra, I. L.</dc:creator>
<dc:creator>Cheng, X.</dc:creator>
<dc:creator>Luecken, M. D.</dc:creator>
<dc:creator>Lu, J.</dc:creator>
<dc:creator>Monavarfeshani, A.</dc:creator>
<dc:creator>Yan, W.</dc:creator>
<dc:creator>Zheng, Y.</dc:creator>
<dc:creator>Zuo, Z.</dc:creator>
<dc:creator>Colborn, S. L. Z.</dc:creator>
<dc:creator>Cortez, B. S.</dc:creator>
<dc:creator>Owen, L. A.</dc:creator>
<dc:creator>Tran, N. M.</dc:creator>
<dc:creator>Shekhar, K.</dc:creator>
<dc:creator>Sanes, J. R.</dc:creator>
<dc:creator>Stout, J. T.</dc:creator>
<dc:creator>Chen, S.</dc:creator>
<dc:creator>Li, Y.</dc:creator>
<dc:creator>DeAngelis, M. M.</dc:creator>
<dc:creator>Theis, F. J.</dc:creator>
<dc:creator>Chen, R.</dc:creator>
<dc:date>2023-11-08</dc:date>
<dc:identifier>doi:10.1101/2023.11.07.566105</dc:identifier>
<dc:title><![CDATA[Integrated multi-omics single cell atlas of the human retina]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-11-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.10.27.564403v1?rss=1">
<title>
<![CDATA[
Early human fetal lung atlas reveals the temporal dynamics of epithelial cell plasticity. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.10.27.564403v1?rss=1"
</link>
<description><![CDATA[
While animal models have provided key insights into conserved mechanisms of how the lung forms during development, human-specific developmental mechanisms are not always captured. To fully appreciate how developmental defects and disease states alter the function of the lungs, studies in human lung models are important. Here, we sequenced >150,000 single single-cells from 19 healthy human fetal lung tissues from gestational weeks 10-19 and identified at least 58 unique cell types/states contributing to the developing lung. We captured novel dynamic developmental trajectories from various progenitor cells that give rise to club, ciliated, and pulmonary neuroendocrine cells. We also identified four CFTR-expressing progenitor cell types and pinpointed the temporal emergence of these cell types. These developmental dynamics reveal broader epithelial cell plasticity and novel lineage hierarchies that were not previously reported. Combined with spatial transcriptomics, we identified both cell autonomous and non-cell autonomous signalling pathways that may dictate the temporal and spatial emergence of cell lineages. Finally, we showed that human pluripotent stem cell-derived fetal lung models capture cell lineage trajectories specifically through CFTR-expressing progenitor cells, that were also observed in the native fetal tissue. Overall, this study provides a comprehensive single-cell atlas of the developing human lung, outlining the temporal and spatial complexities of cell lineage development.

HighlightsO_LISingle-cell transcriptomics atlas from 19 human fetal lungs reveals cellular heterogeneity and previously unappreciated cellular plasticity in the epithelial compartment.
C_LIO_LIIdentification of novel CFTR-expressing progenitor cells that gives rise to club, ciliated and PNEC.
C_LIO_LINovel RNA velocity facilitated the identification of dynamic lineage trajectories in the epithelial compartment.
C_LIO_LITemporally regulated cell signaling through promiscuous interactions between sender and receiving cells may dictate cell lineage fates.
C_LIO_LIIntegration of human pluripotent stem cell (hPSC)-derived fetal lung cells and organoids with primary lung dataset show hPSC-differentiations captures key developmental trajectories of fetal epithelial cell states.
C_LI
]]></description>
<dc:creator>Quach, H. T.</dc:creator>
<dc:creator>Farrell, S.</dc:creator>
<dc:creator>Kanagarajah, K.</dc:creator>
<dc:creator>Wu, M.</dc:creator>
<dc:creator>Xu, X.</dc:creator>
<dc:creator>Kallurkar, P.</dc:creator>
<dc:creator>Turinsky, A.</dc:creator>
<dc:creator>Bear, C.</dc:creator>
<dc:creator>Ratjen, F.</dc:creator>
<dc:creator>Goyal, S.</dc:creator>
<dc:creator>Moraes, T. J.</dc:creator>
<dc:creator>Wong, A.</dc:creator>
<dc:date>2023-10-28</dc:date>
<dc:identifier>doi:10.1101/2023.10.27.564403</dc:identifier>
<dc:title><![CDATA[Early human fetal lung atlas reveals the temporal dynamics of epithelial cell plasticity.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-10-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.11.16.566964v1?rss=1">
<title>
<![CDATA[
Spatially resolved single-cell atlas of the lung in fatal Covid19 in an African population reveals a distinct cellular signature and an interferon gamma dominated response 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.11.16.566964v1?rss=1"
</link>
<description><![CDATA[
Postmortem single-cell studies have transformed understanding of lower respiratory tract diseases (LRTD) including Covid19 but there is almost no data from African settings where HIV, malaria and other environmental exposures may affect disease pathobiology and treatment targets. We used histology and high-dimensional imaging to characterise fatal lung disease in Malawian adults with (n=9) and without (n=7) Covid19, and generated single-cell transcriptomics data from lung, blood and nasal cells. Data integration with other cohorts showed a conserved Covid19 histopathological signature, driven by contrasting immune and inflammatory mechanisms: in the Malawi cohort, by response to interferon-gamma (IFN-{gamma}) in lung-resident alveolar macrophages, in USA, European and Asian cohorts by type I/III interferon responses, particularly in blood-derived monocytes. HIV status had minimal impact on histology or immunopathology. Our study provides data resources and highlights the importance of studying the cellular mechanisms of disease in underrepresented populations, indicating shared and distinct targets for treatment.
]]></description>
<dc:creator>Nyirenda, J.</dc:creator>
<dc:creator>Hardy, O.</dc:creator>
<dc:creator>Silva Filho, J. L.</dc:creator>
<dc:creator>Herder, V.</dc:creator>
<dc:creator>Attipa, C.</dc:creator>
<dc:creator>Ndovi, C.</dc:creator>
<dc:creator>Siwombo, M.</dc:creator>
<dc:creator>Namalima, T.</dc:creator>
<dc:creator>Suwedi, L.</dc:creator>
<dc:creator>Nyasulu, W.</dc:creator>
<dc:creator>Ngulube, T.</dc:creator>
<dc:creator>Nyirenda, D.</dc:creator>
<dc:creator>Mvaya, L.</dc:creator>
<dc:creator>Phiri, J.</dc:creator>
<dc:creator>Chasweka, D.</dc:creator>
<dc:creator>Eneya, C.</dc:creator>
<dc:creator>Makwinja, C.</dc:creator>
<dc:creator>Phiri, C.</dc:creator>
<dc:creator>Ziwoya, F.</dc:creator>
<dc:creator>Tembo, A.</dc:creator>
<dc:creator>Makwangwala, K.</dc:creator>
<dc:creator>Khoswe, S.</dc:creator>
<dc:creator>Banda, P.</dc:creator>
<dc:creator>Morton, B.</dc:creator>
<dc:creator>Hilton, O.</dc:creator>
<dc:creator>Lawrence, S.</dc:creator>
<dc:creator>Frere dos Reis, M.</dc:creator>
<dc:creator>Cardoso Melo, G.</dc:creator>
<dc:creator>Vinicious Guimaros de Lecerda, M.</dc:creator>
<dc:creator>Trindande Maranhao Costa, F.</dc:creator>
<dc:creator>Marcelo Monteiro, W.</dc:creator>
<dc:creator>Carlos de Lima Fereirra, L.</dc:creator>
<dc:creator>Johnson, C.</dc:creator>
<dc:creator>Mcguinness, D.</dc:creator>
<dc:creator>Jambo, K.</dc:creator>
<dc:creator>Haley, M. J.</dc:creator>
<dc:creator>Kumwenda, B.</dc:creator>
<dc:creator>Palmarini, M.</dc:creator>
<dc:creator>Barnes, K. G.</dc:creator>
<dc:creator>Denno, D. M.</dc:creator>
<dc:creator>Vo</dc:creator>
<dc:date>2023-11-17</dc:date>
<dc:identifier>doi:10.1101/2023.11.16.566964</dc:identifier>
<dc:title><![CDATA[Spatially resolved single-cell atlas of the lung in fatal Covid19 in an African population reveals a distinct cellular signature and an interferon gamma dominated response]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-11-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.11.20.567929v1?rss=1">
<title>
<![CDATA[
scSemiProfiler: Advancing Large-scale Single-cell Studiesthrough Semi-profiling with Deep Generative Models andActive Learning 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.11.20.567929v1?rss=1"
</link>
<description><![CDATA[
AbstractSingle-cell sequencing is a crucial tool for dissecting the cellular intricacies of complex diseases. Its prohibitive cost, however, hampers its application in expansive biomedical studies. Traditional cellular deconvolution approaches can infer cell type proportions from more affordable bulk sequencing data, yet they fall short in providing the detailed resolution required for single-cell-level analyses. To overcome this challenge, we introduce "scSemiProfiler", an innovative computational framework that marries deep generative models with active learning strategies. This method adeptly infers single-cell profiles across large cohorts by fusing bulk sequencing data with targeted single-cell sequencing from a few rigorously chosen representatives. Extensive validation across heterogeneous datasets verifies the precision of our semi-profiling approach, aligning closely with true single-cell profiling data and empowering refined cellular analyses. Originally developed for extensive disease cohorts, "scSemiProfiler" is adaptable for broad applications. It provides a scalable, cost-effective solution for single-cell profiling, facilitating in-depth cellular investigation in various biological domains.
]]></description>
<dc:creator>Wang, J.</dc:creator>
<dc:creator>Fonseca, G. J.</dc:creator>
<dc:creator>Ding, J.</dc:creator>
<dc:date>2023-11-21</dc:date>
<dc:identifier>doi:10.1101/2023.11.20.567929</dc:identifier>
<dc:title><![CDATA[scSemiProfiler: Advancing Large-scale Single-cell Studiesthrough Semi-profiling with Deep Generative Models andActive Learning]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-11-21</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.11.28.569028v1?rss=1">
<title>
<![CDATA[
A deep lung cell atlas reveals cytokine-mediated lineage switching of a rare cell progenitor of the human airway epithelium 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.11.28.569028v1?rss=1"
</link>
<description><![CDATA[
The human airway contains specialized rare epithelial cells whose roles in respiratory disease are not well understood. Ionocytes express the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR), while chemosensory tuft cells express asthma-associated alarmins. However, surprisingly, exceedingly few mature tuft cells have been identified in human lung cell atlases despite the ready identification of rare ionocytes and neuroendocrine cells. To identify human rare cell progenitors and define their lineage relationship to mature tuft cells, we generated a deep lung cell atlas containing 311,748 single cell RNA-Seq (scRNA-seq) profiles from discrete anatomic sites along the large and small airways and lung lobes of explanted donor lungs that could not be used for organ transplantation. Of 154,222 airway epithelial cells, we identified 687 ionocytes (0.45%) that are present in similar proportions in both large and small airways, suggesting that they may contribute to both large and small airways pathologies in CF. In stark contrast, we recovered only 3 mature tuft cells (0.002%). Instead, we identified rare bipotent progenitor cells that can give rise to both ionocytes and tuft cells, which we termed tuft-ionocyte progenitor cells (TIP cells). Remarkably, the cycling fraction of these TIP cells was comparable to that of basal stem cells. We used scRNA-seq and scATAC-seq to predict transcription factors that mark this novel rare cell progenitor population and define intermediate states during TIP cell lineage transitions en route to the differentiation of mature ionocytes and tuft cells. The default lineage of TIP cell descendants is skewed towards ionocytes, explaining the paucity of mature tuft cells in the human airway. However, Type 2 and Type 17 cytokines, associated with asthma and CF, diverted the lineage of TIP cell descendants in vitro, resulting in the differentiation of mature tuft cells at the expense of ionocytes. Consistent with this model of mature tuft cell differentiation, we identify mature tuft cells in a patient who died from an asthma flare. Overall, our findings suggest that the immune signaling pathways active in asthma and CF may skew the composition of disease-relevant rare cells and illustrate how deep atlases are required for identifying physiologically-relevant scarce cell populations.
]]></description>
<dc:creator>Waghray, A.</dc:creator>
<dc:creator>Monga, I.</dc:creator>
<dc:creator>Lin, B.</dc:creator>
<dc:creator>Shah, V.</dc:creator>
<dc:creator>Slyper, M.</dc:creator>
<dc:creator>Giotti, B.</dc:creator>
<dc:creator>Xu, J.</dc:creator>
<dc:creator>Waldman, J.</dc:creator>
<dc:creator>Dionne, D.</dc:creator>
<dc:creator>Nguyen, L. T.</dc:creator>
<dc:creator>Lou, W.</dc:creator>
<dc:creator>Cai, P.</dc:creator>
<dc:creator>Park, E.</dc:creator>
<dc:creator>Muus, C.</dc:creator>
<dc:creator>Sun, J.</dc:creator>
<dc:creator>Surve, M. V.</dc:creator>
<dc:creator>Yang, L. C. C.</dc:creator>
<dc:creator>Rozenblatt-Rosen, O.</dc:creator>
<dc:creator>Dolerey, T. M.</dc:creator>
<dc:creator>Saladi, S. V.</dc:creator>
<dc:creator>Tsankov, A. M.</dc:creator>
<dc:creator>Regev, A.</dc:creator>
<dc:creator>Rajagopal, J.</dc:creator>
<dc:date>2023-11-29</dc:date>
<dc:identifier>doi:10.1101/2023.11.28.569028</dc:identifier>
<dc:title><![CDATA[A deep lung cell atlas reveals cytokine-mediated lineage switching of a rare cell progenitor of the human airway epithelium]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-11-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.12.13.519713v1?rss=1">
<title>
<![CDATA[
Early human lung immune cell development and its role in epithelial cell fate 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.12.13.519713v1?rss=1"
</link>
<description><![CDATA[
During human development, lungs develop their roles of gas exchange and barrier function. Recent single cell studies have focused on epithelial and mesenchymal cell types, but much less is known about the developing lung immune cells, although the airways are a major site of mucosal immunity after birth. An open question is whether tissue-resident immune cells play a role in shaping the tissue as it develops in utero. In order to address this, we profiled lung immune cells using scRNAseq, smFISH and immunohistochemistry. At the embryonic stage, we observed an early wave of innate immune cells, including ILCs, NK, myeloid cells and lineage progenitors. By the canalicular stage, we detected naive T lymphocytes high in cytotoxicity genes, and mature B lymphocytes, including B1 cells. Our analysis suggests that fetal lungs provide a niche for full B cell maturation. Given the abundance of immune cells, we investigated their possible effect on epithelial maturation and found that IL-1{beta} drives epithelial progenitor exit from self-renewal and differentiation to basal cells in vitro. In vivo, IL-1{beta}-producing myeloid cells were found adjacent to epithelial tips, suggesting that immune cells may direct the developing lung epithelium.
]]></description>
<dc:creator>Barnes, J. L.</dc:creator>
<dc:creator>He, P.</dc:creator>
<dc:creator>Yoshida, M.</dc:creator>
<dc:creator>Worlock, K. B.</dc:creator>
<dc:creator>Lindeboom, R. G. H.</dc:creator>
<dc:creator>Suo, C.</dc:creator>
<dc:creator>Pett, J. P.</dc:creator>
<dc:creator>Wilbrey-Clark, A.</dc:creator>
<dc:creator>Dann, E.</dc:creator>
<dc:creator>Mamanova, L.</dc:creator>
<dc:creator>Richardson, L.</dc:creator>
<dc:creator>Oliver, A. J.</dc:creator>
<dc:creator>Pennycuick, A. L.</dc:creator>
<dc:creator>Allen-Hyttinen, J.</dc:creator>
<dc:creator>Herczeg, I. T.</dc:creator>
<dc:creator>Hynds, R. E.</dc:creator>
<dc:creator>Teixeira, V. H.</dc:creator>
<dc:creator>Haniffa, M.</dc:creator>
<dc:creator>Lim, K.</dc:creator>
<dc:creator>Sun, D.</dc:creator>
<dc:creator>Rawlins, E. L.</dc:creator>
<dc:creator>Polanski, K.</dc:creator>
<dc:creator>Lyons, P. A.</dc:creator>
<dc:creator>Marioni, J. C.</dc:creator>
<dc:creator>Tuong, Z. K.</dc:creator>
<dc:creator>Clatworthy, M. R.</dc:creator>
<dc:creator>Reading, J. L.</dc:creator>
<dc:creator>Janes, S. M.</dc:creator>
<dc:creator>Teichmann, S.</dc:creator>
<dc:creator>Meyer, K. B.</dc:creator>
<dc:creator>Nikolic, M. Z.</dc:creator>
<dc:date>2022-12-15</dc:date>
<dc:identifier>doi:10.1101/2022.12.13.519713</dc:identifier>
<dc:title><![CDATA[Early human lung immune cell development and its role in epithelial cell fate]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-12-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.05.01.538994v1?rss=1">
<title>
<![CDATA[
Automatic cell type harmonization and integration across Human Cell Atlas datasets 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.05.01.538994v1?rss=1"
</link>
<description><![CDATA[
Harmonizing cell types across the single-cell community and assembling them into a common framework is central to building a standardized Human Cell Atlas. Here we present CellHint, a predictive clustering tree-based tool to resolve cell type differences in annotation resolution and technical biases across datasets. CellHint accurately quantifies cell-cell transcriptomic similarities and places cell types into a relationship graph that hierarchically defines shared and unique cell subtypes. Application to multiple immune datasets recapitulates expert-curated annotations. CellHint also reveals underexplored relationships between healthy and diseased lung cell states in eight diseases. Furthermore, we present a workflow for fast cross-dataset integration guided by harmonized cell types and cell hierarchy, which uncovers underappreciated cell types in adult human hippocampus. Finally, we apply CellHint to 12 tissues from 38 datasets, providing a deeply curated cross-tissue database with [~]3.7 million cells and various machine learning models for automatic cell annotation across human tissues.
]]></description>
<dc:creator>Xu, C.</dc:creator>
<dc:creator>Prete, M.</dc:creator>
<dc:creator>Webb, S.</dc:creator>
<dc:creator>Jardine, L.</dc:creator>
<dc:creator>Stewart, B.</dc:creator>
<dc:creator>Hoo, R.</dc:creator>
<dc:creator>He, P.</dc:creator>
<dc:creator>Teichmann, S.</dc:creator>
<dc:date>2023-05-01</dc:date>
<dc:identifier>doi:10.1101/2023.05.01.538994</dc:identifier>
<dc:title><![CDATA[Automatic cell type harmonization and integration across Human Cell Atlas datasets]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-05-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.05.24.493094v1?rss=1">
<title>
<![CDATA[
Human skeletal muscle ageing atlas 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.05.24.493094v1?rss=1"
</link>
<description><![CDATA[
Skeletal muscle ageing increases the incidence of age-associated frailty and sarcopenia in the elderly worldwide, leading to increased morbidity and mortality. However, our understanding of the cellular and molecular mechanisms of muscle ageing is still far from complete. Here, we generate a single-cell and single-nucleus transcriptomic atlas of skeletal muscle ageing from 15 donors across the adult human lifespan, accompanied by myofiber typing using imaging. Our atlas reveals ageing mechanisms acting across different compartments of the muscle, including muscle stem cells (MuSCs), myofibers and the muscle microenvironment. Firstly, we uncover two mechanisms driving MuSC ageing, namely a decrease in ribosome biogenesis and an increase in inflammation. Secondly, we identify a set of nuclei populations explaining the preferential degeneration of the fast-twitch myofibers and suggest two mechanisms acting to compensate for their loss. Importantly, we identify a neuromuscular junction accessory population, which helps myofiber to compensate for aged-related denervation. Thirdly, we reveal multiple microenvironment cell types contributing to the inflammatory milieu of ageing muscle by producing cytokines and chemokines to attract immune cells. Finally, we provide a comparable mouse muscle ageing atlas and further investigate conserved and specific ageing hallmarks across species. In summary, we present a comprehensive human skeletal muscle ageing resource by combining different data modalities, which significantly expands our understanding of muscle biology and ageing.
]]></description>
<dc:creator>Kedlian, V. R.</dc:creator>
<dc:creator>Wang, Y.</dc:creator>
<dc:creator>Liu, T.</dc:creator>
<dc:creator>Chen, X.</dc:creator>
<dc:creator>Bolt, L.</dc:creator>
<dc:creator>Shen, Z.</dc:creator>
<dc:creator>Fasouli, E. S.</dc:creator>
<dc:creator>Prigmore, E.</dc:creator>
<dc:creator>Kleshchevnikov, V.</dc:creator>
<dc:creator>Li, T.</dc:creator>
<dc:creator>Lawrence, J. E.</dc:creator>
<dc:creator>Huang, N.</dc:creator>
<dc:creator>Guo, Q.</dc:creator>
<dc:creator>Yang, L.</dc:creator>
<dc:creator>Polanski, K.</dc:creator>
<dc:creator>Dabrowska, M.</dc:creator>
<dc:creator>Tudor, C.</dc:creator>
<dc:creator>Li, X.</dc:creator>
<dc:creator>Bayraktar, O. A.</dc:creator>
<dc:creator>Patel, M.</dc:creator>
<dc:creator>Meyer, K. B.</dc:creator>
<dc:creator>Kumasaka, N.</dc:creator>
<dc:creator>Mahbubani, K. T.</dc:creator>
<dc:creator>Xiang, A. P.</dc:creator>
<dc:creator>Saeb-Parsy, K.</dc:creator>
<dc:creator>Teichmann, S. A.</dc:creator>
<dc:creator>Zhang, H.</dc:creator>
<dc:date>2022-05-25</dc:date>
<dc:identifier>doi:10.1101/2022.05.24.493094</dc:identifier>
<dc:title><![CDATA[Human skeletal muscle ageing atlas]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-05-25</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.11.13.566791v1?rss=1">
<title>
<![CDATA[
A single-cell atlas of transcribed cis-regulatory elements in the human genome 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.11.13.566791v1?rss=1"
</link>
<description><![CDATA[
Transcribed cis-regulatory elements (tCREs), such as promoters and enhancers, are fundamental to modulate gene expression and define cell identity. The detailed mapping of tCREs at single-cell resolution is essential for understanding the regulatory mechanisms that govern cellular functions. Prior tCRE catalogs, limited by bulk analysis, have often overlooked cellular heterogeneity. We have constructed a tCRE atlas using single-cell 5-RNA-seq, capturing over 340,000 single-cells from 23 human tissues and annotating more than 175,000 tCREs, substantially enhancing the scope and granularity of existing cis-regulatory element annotations in the human genome. This atlas unveils patterns of gene regulation, revealing connections between broadly expressed promoters and cell type-specific distal tCREs. Assessing trait heritability at single-cell resolution with a novel tCRE module-based approach, we uncovered the nuanced trait-gene regulatory relationships across a continuum of cell populations, offering insights beyond traditional gene-level and bulk-sample analyses. Our study bridges the gap between gene regulation and trait heritability, underscoring the potential of single-cell analysis to elucidate the genetic foundations of complex traits. These insights set the stage for future research to investigate the impact of genetic variations on diseases at the individual level, advancing the understanding of cellular and molecular basis of trait heritability.
]]></description>
<dc:creator>Moody, J.</dc:creator>
<dc:creator>Kouno, T.</dc:creator>
<dc:creator>Kojima, M.</dc:creator>
<dc:creator>Koya, I.</dc:creator>
<dc:creator>Leon, J.</dc:creator>
<dc:creator>Suzuki, A.</dc:creator>
<dc:creator>Hasegawa, A.</dc:creator>
<dc:creator>Akiyama, T.</dc:creator>
<dc:creator>Akiyama, N.</dc:creator>
<dc:creator>Amagai, M.</dc:creator>
<dc:creator>Chang, J.-C.</dc:creator>
<dc:creator>Fukushima-Nomura, A.</dc:creator>
<dc:creator>Handa, M.</dc:creator>
<dc:creator>Hino, K.</dc:creator>
<dc:creator>Hino, M.</dc:creator>
<dc:creator>Hirata, T.</dc:creator>
<dc:creator>Imai, Y.</dc:creator>
<dc:creator>Inoue, K.</dc:creator>
<dc:creator>Kawasaki, H.</dc:creator>
<dc:creator>Kimura, T.</dc:creator>
<dc:creator>Kinoshita, T.</dc:creator>
<dc:creator>Kubo, K.-i.</dc:creator>
<dc:creator>Kunii, Y.</dc:creator>
<dc:creator>Lopez-Redondo, F.</dc:creator>
<dc:creator>Manabe, R.-i.</dc:creator>
<dc:creator>Miyai, T.</dc:creator>
<dc:creator>Morimoto, S.</dc:creator>
<dc:creator>Nagaoka, A.</dc:creator>
<dc:creator>Nakajima, J.</dc:creator>
<dc:creator>Noma, S.</dc:creator>
<dc:creator>Okazaki, Y.</dc:creator>
<dc:creator>Ozaki, K.</dc:creator>
<dc:creator>Saeki, N.</dc:creator>
<dc:creator>Sakai, H.</dc:creator>
<dc:creator>Seyama, K.</dc:creator>
<dc:creator>Shibayama, Y.</dc:creator>
<dc:creator>Sujino, T.</dc:creator>
<dc:creator>Tagami, M.</dc:creator>
<dc:creator>Takahashi, H.</dc:creator>
<dc:creator>Takao, M.</dc:creator>
<dc:creator>Takeshita, M.</dc:creator>
<dc:creator>Takiuchi, T.</dc:creator>
<dc:creator>Terao, C.</dc:creator>
<dc:creator>Yip, C. W.</dc:creator>
<dc:creator>Yoshinaga, S.</dc:creator>
<dc:creator>Okano, H.</dc:creator>
<dc:creator>Yamamoto, K.</dc:creator>
<dc:creator>Kasuka</dc:creator>
<dc:date>2023-11-14</dc:date>
<dc:identifier>doi:10.1101/2023.11.13.566791</dc:identifier>
<dc:title><![CDATA[A single-cell atlas of transcribed cis-regulatory elements in the human genome]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-11-14</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.05.19.541329v1?rss=1">
<title>
<![CDATA[
WebAtlas pipeline for integrated single cell and spatial transcriptomic data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.05.19.541329v1?rss=1"
</link>
<description><![CDATA[
Single cell and spatial transcriptomics illuminate complementary features of tissues. However, online dissemination and exploration of integrated datasets is challenging due to the heterogeneity and scale of data. We introduce the WebAtlas pipeline for user-friendly sharing and interactive navigation of integrated datasets. WebAtlas unifies commonly used atlassing technologies into the cloud-optimised Zarr format and builds on Vitessce to enable remote data navigation. We showcase WebAtlas on the developing human lower limb to cross-query cell types and genes across single cell, sequencing- and imaging-based spatial transcriptomic data.
]]></description>
<dc:creator>Li, T.</dc:creator>
<dc:creator>Horsfall, D.</dc:creator>
<dc:creator>Basurto-Lozada, D.</dc:creator>
<dc:creator>Roberts, K.</dc:creator>
<dc:creator>Prete, M.</dc:creator>
<dc:creator>Lawrence, J. E. G.</dc:creator>
<dc:creator>He, P.</dc:creator>
<dc:creator>Tuck, E.</dc:creator>
<dc:creator>Moore, J.</dc:creator>
<dc:creator>Ghazanfar, S.</dc:creator>
<dc:creator>Teichmann, S.</dc:creator>
<dc:creator>Haniffa, M.</dc:creator>
<dc:creator>Bayraktar, O. A.</dc:creator>
<dc:date>2023-05-22</dc:date>
<dc:identifier>doi:10.1101/2023.05.19.541329</dc:identifier>
<dc:title><![CDATA[WebAtlas pipeline for integrated single cell and spatial transcriptomic data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-05-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.05.31.539801v1?rss=1">
<title>
<![CDATA[
Single cell-guided prenatal derivation of primary epithelial organoids from the human amniotic and tracheal fluids. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.05.31.539801v1?rss=1"
</link>
<description><![CDATA[
Despite advances in prenatal diagnosis, it is still difficult to predict severity and outcomes of many congenital malformations. New patient-specific prenatal disease modelling may optimise personalised prediction. We and others have described the presence of mesenchymal stem cells in amniotic fluid (AFSC) that can generate induced pluripotent stem cells (iPSCs). The lengthy reprogramming processes, however, limits the ability to define individual phenotypes or plan prenatal treatment. Therefore, it would be advantageous if fetal stem cells could be obtained during pregnancy and expanded without reprogramming. Using single cell analysis, we characterised the cellular identities in amniotic fluid (AF) and identified viable epithelial stem/progenitor cells of fetal intestinal, renal and pulmonary origin. With relevance for prenatal disease modelling, these cells could be cultured to form clonal epithelial organoids manifesting small intestine, kidney and lung identity. To confirm this, we derived lung organoids from AF and tracheal fluid (TF) cells of Congenital Diaphragmatic Hernia (CDH) fetuses and found that they show differences to non-CDH controls and can recapitulate some pathological features of the disease. Amniotic Fluid Organoids (AFO) allow investigation of fetal epithelial tissues at clinically relevant developmental stages and may enable the development of therapeutic tools tailored to the fetus, as well as to predicting the effects of such therapies.
]]></description>
<dc:creator>Gerli, M. F. M.</dc:creator>
<dc:creator>Cala, G.</dc:creator>
<dc:creator>Beesley, M. A.</dc:creator>
<dc:creator>Sina, B.</dc:creator>
<dc:creator>Tullie, L.</dc:creator>
<dc:creator>Panariello, F.</dc:creator>
<dc:creator>Michielin, F.</dc:creator>
<dc:creator>Sun, K. Y.</dc:creator>
<dc:creator>Davidson, J. R.</dc:creator>
<dc:creator>Russo, F. M.</dc:creator>
<dc:creator>Jones, B. C.</dc:creator>
<dc:creator>Lee, D.</dc:creator>
<dc:creator>Savvidis, S.</dc:creator>
<dc:creator>Xenakis, T.</dc:creator>
<dc:creator>Simcock, I. C.</dc:creator>
<dc:creator>Straatman-Iwanowska, A. A.</dc:creator>
<dc:creator>Hirst, R. A.</dc:creator>
<dc:creator>David, A. L.</dc:creator>
<dc:creator>O'Callaghan, C.</dc:creator>
<dc:creator>Olivo, A.</dc:creator>
<dc:creator>Eaton, S.</dc:creator>
<dc:creator>Loukogeorgakis, S. P.</dc:creator>
<dc:creator>Cacchiarelli, D.</dc:creator>
<dc:creator>Deprest, J.</dc:creator>
<dc:creator>Li, V. S.</dc:creator>
<dc:creator>Giobbe, G. G.</dc:creator>
<dc:creator>De Coppi, P.</dc:creator>
<dc:date>2023-06-01</dc:date>
<dc:identifier>doi:10.1101/2023.05.31.539801</dc:identifier>
<dc:title><![CDATA[Single cell-guided prenatal derivation of primary epithelial organoids from the human amniotic and tracheal fluids.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-06-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.02.02.578633v1?rss=1">
<title>
<![CDATA[
Spatial transcriptomics of healthy and fibrotic human liver at single-cell resolution 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.02.02.578633v1?rss=1"
</link>
<description><![CDATA[
Single-cell RNA sequencing (scRNA-seq) has advanced our understanding of cell types and their heterogeneity within the human liver, but the spatial organization at single-cell resolution has not yet been described. Here we apply multiplexed error robust fluorescent in situ hybridization (MERFISH) to map the zonal distribution of hepatocytes, resolve subsets of macrophage and mesenchymal populations, and investigate the relationship between hepatocyte ploidy and gene expression within the healthy human liver. We next integrated spatial information from MERFISH with the more complete transcriptome produced by single-nucleus RNA sequencing (snRNA-seq), revealing zonally enriched receptor-ligand interactions. Finally, analysis of fibrotic liver samples identified two hepatocyte populations that are not restricted to zonal distribution and expand with injury. Together these spatial maps of the healthy and fibrotic liver provide a deeper understanding of the cellular and spatial remodeling that drives disease which, in turn, could provide new avenues for intervention and further study.
]]></description>
<dc:creator>Watson, B.</dc:creator>
<dc:creator>Paul, B.</dc:creator>
<dc:creator>Amir-Zilberstein, L.</dc:creator>
<dc:creator>Segerstolpe, A.</dc:creator>
<dc:creator>Rahman, R.</dc:creator>
<dc:creator>Shih, A.</dc:creator>
<dc:creator>Deguine, J.</dc:creator>
<dc:creator>Xavier, R.</dc:creator>
<dc:creator>Moffitt, J. R.</dc:creator>
<dc:creator>Mullen, A. C.</dc:creator>
<dc:date>2024-02-07</dc:date>
<dc:identifier>doi:10.1101/2024.02.02.578633</dc:identifier>
<dc:title><![CDATA[Spatial transcriptomics of healthy and fibrotic human liver at single-cell resolution]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-02-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.12.19.521110v1?rss=1">
<title>
<![CDATA[
Single nucleus and spatial transcriptomic profiling of human healthy hamstring tendon 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.12.19.521110v1?rss=1"
</link>
<description><![CDATA[
The molecular and cellular basis of health in human tendons remains poorly understood. Amongst human tendons, the hamstrings are the least likely to be injured or degenerate, providing a prototypic healthy tendon reference. The aim of this study was to define the transcriptome and location of all cell types in healthy hamstring tendon. We profiled the transcriptomes of 10,533 nuclei from 4 healthy donors using single-nucleus RNA sequencing (snRNA-seq) and identified 12 distinct cell types. We confirmed the presence of two fibroblast cell types, endothelial cells, mural cells, and immune cells, and revealed the presence of cell types previously unreported for tendon sites, including different skeletal muscle cell types, satellite cells, adipocytes, and nerve cells, which are undefined nervous system cells. Location of these cell types within tendon was defined using spatial transcriptomics and imaging, and transcriptional networks and cell-cell interactions were identified. We demonstrate that fibroblasts have a high number of potential cell-cell interactions, are present throughout the whole tendon tissue, and play an important role in the production and organisation of extracellular matrix, thus confirming their role as key regulators of hamstring tendon tissue homeostasis. Overall, our findings highlight the highly complex cellular networks underpinning tendon function and underpins the importance of fibroblasts as key regulators of hamstring tendon tissue homeostasis.
]]></description>
<dc:creator>Mimpen, J. Y.</dc:creator>
<dc:creator>Ramos-Mucci, L. D.</dc:creator>
<dc:creator>Paul, C.</dc:creator>
<dc:creator>Kurjan, A.</dc:creator>
<dc:creator>Hulley, P.</dc:creator>
<dc:creator>Ikwuanusi, C.</dc:creator>
<dc:creator>Gwilym, S.</dc:creator>
<dc:creator>Baldwin, M. J.</dc:creator>
<dc:creator>Cribbs, A. P.</dc:creator>
<dc:creator>Snelling, S. J. B.</dc:creator>
<dc:date>2022-12-20</dc:date>
<dc:identifier>doi:10.1101/2022.12.19.521110</dc:identifier>
<dc:title><![CDATA[Single nucleus and spatial transcriptomic profiling of human healthy hamstring tendon]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-12-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.03.07.583985v1?rss=1">
<title>
<![CDATA[
Cellular heterogeneity and dynamics of the human uterus in healthy premenopausal women 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.03.07.583985v1?rss=1"
</link>
<description><![CDATA[
The human uterus is a complex and dynamic organ whose lining grows, remodels, and regenerates in every menstrual cycle or upon tissue damage. Here we applied single-cell RNA sequencing to profile more the 50,000 uterine cells from both the endometrium and myometrium of 5 healthy premenopausal individuals, and jointly analyzed the data with a previously published dataset from 15 subjects. The resulting normal uterus cell atlas contains more than 167K cells representing the lymphatic endothelium, blood endothelium, stromal, ciliated epithelium, unciliated epithelium, and immune cell populations. Focused analyses within each major cell type and comparisons with subtype labels from prior studies allowed us to document supporting evidence, resolve naming conflicts, and to propose a consensus annotation system of 39 subtypes. We release their gene expression centroids, differentially expressed genes, and mRNA patterns of literature-based markers as a shared community resource. We find many subtypes show dynamic changes over different phases of the cycle and identify multiple potential progenitor cells: compartment-wide progenitors for each major cell type, transitional cells that are upstream of other subtypes, and potential cross-lineage multipotent stromal progenitors that may be capable of replenishing the epithelial, stromal, and endothelial compartments. When compared to the healthy premenopausal samples, a postpartum and a postmenopausal uterus sample revealed substantially altered tissue composition, involving the rise or fall of stromal, endothelial, and immune cells. The cell taxonomy and molecular markers we report here are expected to inform studies of both basic biology of uterine function and its disorders.

SIGNIFICANCEWe present single-cell RNA sequencing data from seven individuals (five healthy pre-menopausal women, one post-menopausal woman, and one postpartum) and perform an integrated analysis of this data alongside 15 previously published scRNA-seq datasets. We identified 39 distinct cell subtypes across four major cell types in the uterus. By using RNA velocity analysis and centroid-centroid comparisons we identify multiple computationally predicted progenitor populations for each of the major cell compartments, as well as potential cross-compartment, multi-potent progenitors. While the function and interactions of these cell populations remain to be validated through future experiments, the markers and their "dual characteristics" that we describe will serve as a rich resource to the scientific community. Importantly, we address a significant challenge in the field: reconciling multiple uterine cell taxonomies being proposed. To achieve this, we focused on integrating historical and contemporary knowledge across multiple studies. By providing detailed evidence used for cell classification we lay the groundwork for establishing a stable, consensus cell atlas of the human uterus.
]]></description>
<dc:creator>Ulrich, N. D.</dc:creator>
<dc:creator>Vargo, A. H.</dc:creator>
<dc:creator>Ma, Q.</dc:creator>
<dc:creator>Shen, Y.-C.</dc:creator>
<dc:creator>Hannum, D. F.</dc:creator>
<dc:creator>Gurczynski, S. J.</dc:creator>
<dc:creator>Moore, B. B.</dc:creator>
<dc:creator>Schon, S.</dc:creator>
<dc:creator>Lieberman, R.</dc:creator>
<dc:creator>Shikanov, A.</dc:creator>
<dc:creator>Marsh, E. E.</dc:creator>
<dc:creator>Fazleabas, A. T.</dc:creator>
<dc:creator>Li, J. Z.</dc:creator>
<dc:creator>Hammoud, S. S.</dc:creator>
<dc:date>2024-03-12</dc:date>
<dc:identifier>doi:10.1101/2024.03.07.583985</dc:identifier>
<dc:title><![CDATA[Cellular heterogeneity and dynamics of the human uterus in healthy premenopausal women]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-03-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.08.23.554489v1?rss=1">
<title>
<![CDATA[
Harnessing Agent-Based Modeling in CellAgentChat to Unravel Cell-Cell Interactions from Single-Cell Data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.08.23.554489v1?rss=1"
</link>
<description><![CDATA[
Understanding cell-cell interactions (CCIs) is essential yet challenging due to the inherent intricacy and diversity of cellular dynamics. Existing approaches often analyze global patterns of CCIs using statistical frameworks, missing the nuances of individual cell behavior due to their focus on aggregate data. This makes them insensitive in complex environments where the detailed dynamics of cell interactions matter. We introduce CellAgentChat, an agent-based model (ABM) designed to decipher CCIs from single-cell RNA sequencing and spatial transcriptomics data. This approach models biological systems as collections of autonomous agents governed by biologically inspired principles and rules. Validated against seven diverse single-cell datasets, CellAgentChat demonstrates its effectiveness in detecting intricate signaling events across different cell populations. Moreover, CellAgentChat offers the ability to generate animated visualizations of single-cell interactions and provides flexibility in modifying agent behavior rules, facilitating thorough exploration of both close and distant cellular communications. Furthermore, CellAgentChat leverages ABM features to enable intuitive in silico perturbations via agent rule modifications, pioneering new avenues for innovative intervention strategies. This ABM method empowers an in-depth understanding of cellular signaling interactions across various biological contexts, thereby enhancing in-silico studies for cellular communication-based therapies.
]]></description>
<dc:creator>Raghavan, V.</dc:creator>
<dc:creator>Ding, J.</dc:creator>
<dc:date>2023-08-24</dc:date>
<dc:identifier>doi:10.1101/2023.08.23.554489</dc:identifier>
<dc:title><![CDATA[Harnessing Agent-Based Modeling in CellAgentChat to Unravel Cell-Cell Interactions from Single-Cell Data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-08-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.08.24.554614v1?rss=1">
<title>
<![CDATA[
Spatial landmark detection and tissue registration with deep learning 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.08.24.554614v1?rss=1"
</link>
<description><![CDATA[
Spatial landmarks are crucial in describing histological features between samples or sites, tracking regions of interest in microscopy, and registering tissue samples within a common coordinate framework. Although other studies have explored unsupervised landmark detection, existing methods are not well-suited for histological image data as they often require a large number of images to converge, are unable to handle non-linear deformations between tissue sections, and are ineffective for z-stack alignment, other modalities beyond image data, or multimodal data. We address these challenges by introducing a new landmark detection and registration method, utilizing neural-network-guided thin-plate splines. Our proposed method is evaluated on a diverse range of datasets, demonstrating superior performance in both accuracy and stability compared to existing approaches.
]]></description>
<dc:creator>Ekvall, M. N.</dc:creator>
<dc:creator>Bergenstrahle, L.</dc:creator>
<dc:creator>Andersson, A.</dc:creator>
<dc:creator>Olegard, J.</dc:creator>
<dc:creator>Czarnewski, P.</dc:creator>
<dc:creator>Kall, L.</dc:creator>
<dc:creator>Lundeberg, J.</dc:creator>
<dc:date>2023-08-26</dc:date>
<dc:identifier>doi:10.1101/2023.08.24.554614</dc:identifier>
<dc:title><![CDATA[Spatial landmark detection and tissue registration with deep learning]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-08-26</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.09.09.459281v1?rss=1">
<title>
<![CDATA[
A General Framework for Representing and Annotating Multifaceted Cell Heterogeneity in Human Cell Atlas 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.09.09.459281v1?rss=1"
</link>
<description><![CDATA[
A universal coordinate system that can ensemble the huge number of cells and capture their heterogeneities is of vital importance for constructing large-scale cell atlases as references for future molecular and cellular studies. Studies have shown that cells in complex organs exhibit multifaceted heterogeneities in their transcriptomic features at multiple resolutions. This nature of complexity makes it hard to design a fixed coordinate system through a combination of known features. It is desirable to build a learnable universal coordinate model that can capture major heterogeneities and serve as a controlled generative model for data argumentation. We developed UniCoord, a specially tuned joint-VAE model to represent single-cell transcriptomic data in a lower-dimensional latent space with high interpretability. Each latent dimension can represent either discrete or continuous feature, and either supervised by prior knowledge or unsupervised. The original transcriptomic profiles can be regenerated from the latent dimensions. The latent dimensions can be easily reconfigured to generate transcriptomic profiles of pseudo cells with desired properties. UniCoord can also be used as a pre-trained model to analyze new data with unseen cell types and thus can serve as a feasible framework for cell annotation and comparison. UniCoord provides a prototype for a learnable universal coordinate framework to enable better analysis and generation of cells with highly orchestrated functions and heterogeneities.
]]></description>
<dc:creator>Gao, H.</dc:creator>
<dc:creator>Hua, K.</dc:creator>
<dc:creator>Chen, S.</dc:creator>
<dc:creator>Yin, Q.</dc:creator>
<dc:creator>Jiang, R.</dc:creator>
<dc:creator>Zhang, X.</dc:creator>
<dc:date>2021-09-10</dc:date>
<dc:identifier>doi:10.1101/2021.09.09.459281</dc:identifier>
<dc:title><![CDATA[A General Framework for Representing and Annotating Multifaceted Cell Heterogeneity in Human Cell Atlas]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-09-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.09.23.509186v1?rss=1">
<title>
<![CDATA[
Learning Spatially-Aware Representations of Transcriptomic Data via Transfer Learning 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.09.23.509186v1?rss=1"
</link>
<description><![CDATA[
Profiling spatial variations of cellular composition and transcriptomic characteristics is important for understanding the physiology and pathology of tissues in health or diseases. Spatial transcriptomics (ST) data are powerful for depicting spatial gene expression but the currently dominating high-throughput technology is yet not at single-cell resolution. On the other hand, single-cell RNA-sequencing (SC) data provide high-throughput transcriptomic information at the single-cell level but lack spatial information. Integrating these two types of data would be ideal for revealing transcriptomic landscapes at single-cell resolution. We developed the method STEM (SpaTially aware EMbedding) for this purpose. It uses deep transfer learning to encode both ST and SC data into a unified spatially aware embedding space, and then uses the embeddings to infer the SC-ST mapping and predict pseudo-spatial adjacency between cells in the SC data. Semi-simulation and real data experiments verified that the embeddings preserved the spatial information and eliminated technical biases between SC and ST data. Besides, we can use attribution analysis in STEM to reveal genes whose expressions dominate spatial information. We applied STEM to data of human squamous cell carcinoma and of hepatic lobule to uncover the spatial localization of rare cell types data and reveal cell-type-specific gene expression variation along a spatial axis. STEM is a powerful tool for mapping SC and ST data to build single-cell level spatial transcriptomic landscapes, and can provide mechanistic insights into the spatial heterogeneity and microenvironments of tissues.
]]></description>
<dc:creator>Hao, M.</dc:creator>
<dc:creator>Wei, L.</dc:creator>
<dc:creator>Zhang, X.</dc:creator>
<dc:date>2022-09-26</dc:date>
<dc:identifier>doi:10.1101/2022.09.23.509186</dc:identifier>
<dc:title><![CDATA[Learning Spatially-Aware Representations of Transcriptomic Data via Transfer Learning]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-09-26</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.01.25.577163v1?rss=1">
<title>
<![CDATA[
High-parametric protein maps reveal the spatial organization in early-developing human lung 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.01.25.577163v1?rss=1"
</link>
<description><![CDATA[
The respiratory system, including the lungs, is essential for terrestrial life. While recent research has advanced our understanding of lung development, much still relies on animal models and transcriptome analyses. In this study conducted within the Human Developmental Cell Atlas (HDCA) initiative, we describe the protein-level spatiotemporal organization of the lung during the first trimester of human gestation. Using high-parametric tissue imaging with a 30-plex antibody panel, we analyzed human lung samples from 6 to 13 post-conception weeks, generating data from over 2 million cells across five developmental timepoints. We present a resource detailing spatially resolved cell type composition of the developing human lung, including proliferative states, immune cell patterns, spatial arrangement traits, and their temporal evolution. This represents an extensive single-cell resolved protein-level examination of the developing human lungs and provides a valuable resource for further research into the developmental roots of human respiratory health and disease.
]]></description>
<dc:creator>Sariyar, S.</dc:creator>
<dc:creator>Sountoulidis, A.</dc:creator>
<dc:creator>Hansen, J. N.</dc:creator>
<dc:creator>Salas, S. M.</dc:creator>
<dc:creator>Mardamshina, M.</dc:creator>
<dc:creator>Martinez Sacals, A.</dc:creator>
<dc:creator>Ballllosera Navarro, F.</dc:creator>
<dc:creator>Andrusivova, Z.</dc:creator>
<dc:creator>Li, X.</dc:creator>
<dc:creator>Czarnewski, P.</dc:creator>
<dc:creator>Lundeberg, J.</dc:creator>
<dc:creator>Linnarsson, S.</dc:creator>
<dc:creator>Nilsson, M.</dc:creator>
<dc:creator>Sundstrom, E.</dc:creator>
<dc:creator>Samakovlis, C.</dc:creator>
<dc:creator>Lundberg, E.</dc:creator>
<dc:creator>Ayoglu, B.</dc:creator>
<dc:date>2024-01-25</dc:date>
<dc:identifier>doi:10.1101/2024.01.25.577163</dc:identifier>
<dc:title><![CDATA[High-parametric protein maps reveal the spatial organization in early-developing human lung]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-01-25</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.10.05.561097v1?rss=1">
<title>
<![CDATA[
An integrated transcriptomic cell atlas of human neural organoids 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.10.05.561097v1?rss=1"
</link>
<description><![CDATA[
Neural tissues generated from human pluripotent stem cells in vitro (known as neural organoids) are becoming useful tools to study human brain development, evolution and disease. The characterization of neural organoids using single-cell genomic methods has revealed a large diversity of neural cell types with molecular signatures similar to those observed in primary human brain tissue. However, it is unclear which domains of the human nervous system are covered by existing protocols. It is also difficult to quantitatively assess variation between protocols and the specific cell states in organoids as compared to primary counterparts. Single-cell transcriptome data from primary tissue and neural organoids derived with guided or un-guided approaches and under diverse conditions combined with large-scale integrative analyses make it now possible to address these challenges. Recent advances in computational methodology enable the generation of integrated atlases across many data sets. Here, we integrated 36 single-cell transcriptomics data sets spanning 26 protocols into one integrated human neural organoid cell atlas (HNOCA) totaling over 1.7 million cells. We harmonize cell type annotations by incorporating reference data sets from the developing human brain. By mapping to the developing human brain reference, we reveal which primary cell states have been generated in vitro, and which are under-represented. We further compare transcriptomic profiles of neuronal populations in organoids to their counterparts in the developing human brain. To support rapid organoid phenotyping and quantitative assessment of new protocols, we provide a programmatic interface to browse the atlas and query new data sets, and showcase the power of the atlas to annotate new query data sets and evaluate new organoid protocols. Taken together, the HNOCA will be useful to assess the fidelity of organoids, characterize perturbed and diseased states and facilitate protocol development in the future.
]]></description>
<dc:creator>He, Z.</dc:creator>
<dc:creator>Dony, L.</dc:creator>
<dc:creator>Fleck, J. S.</dc:creator>
<dc:creator>Szalata, A.</dc:creator>
<dc:creator>Li, K. X.</dc:creator>
<dc:creator>Sliskovic, I.</dc:creator>
<dc:creator>Lin, H.-C.</dc:creator>
<dc:creator>Santel, M.</dc:creator>
<dc:creator>Atamian, A.</dc:creator>
<dc:creator>Quadrato, G.</dc:creator>
<dc:creator>Sun, J.</dc:creator>
<dc:creator>Pasca, S. P.</dc:creator>
<dc:creator>Camp, G.</dc:creator>
<dc:creator>Theis, F.</dc:creator>
<dc:creator>Treutlein, B.</dc:creator>
<dc:date>2023-10-06</dc:date>
<dc:identifier>doi:10.1101/2023.10.05.561097</dc:identifier>
<dc:title><![CDATA[An integrated transcriptomic cell atlas of human neural organoids]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-10-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.10.04.560911v1?rss=1">
<title>
<![CDATA[
Single nuclei chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.10.04.560911v1?rss=1"
</link>
<description><![CDATA[
Single nuclei analysis is allowing robust classification of cell types in an organ that helps to establish relationships between cell-type specific gene expression and chromatin accessibility status of gene regulatory regions. Using breast tissues of 92 healthy donors of various genetic ancestry, we have developed a comprehensive chromatin accessibility and gene expression atlas of human breast tissues. Integrated analysis revealed 10 distinct cell types in the healthy breast, which included three major epithelial cell subtypes (luminal hormone sensing, luminal adaptive secretory precursor, and basal-myoepithelial cells), two endothelial subtypes, two adipocyte subtypes, fibroblasts, T-cells, and macrophages. By integrating gene expression signatures derived from epithelial cell subtypes with spatial transcriptomics, we identify specific gene expression differences between lobular and ductal epithelial cells and age-associated changes in epithelial cell gene expression patterns and signaling networks. Among various cell types, luminal adaptive secretory cells and fibroblasts showed genetic ancestry dependent variability. A subpopulation of luminal adaptive secretory cells with alveolar progenitor (AP) cell state were enriched in Indigenous American (IA) ancestry and fibroblast populations were distinct in African ancestry. ESR1 expression pattern was distinctly different in cells from IA compared to the rest, with a high level of ESR1 expression extending to AP cells and crosstalk between growth factors and Estrogen Receptor signaling being evident in these AP cells. In general, cell subtype-specific gene expression did not uniformly correlate with cell-specific chromatin accessibility, suggesting that transcriptional regulation independent of chromatin accessibility governs cell type-specific gene expression in the breast.
]]></description>
<dc:creator>Bhat-Nakshatri, P.</dc:creator>
<dc:creator>Gao, H.</dc:creator>
<dc:creator>Khatpe, A. S.</dc:creator>
<dc:creator>McGuire, P. C.</dc:creator>
<dc:creator>Erdogan, C.</dc:creator>
<dc:creator>Chen, D.</dc:creator>
<dc:creator>Jiang, G.</dc:creator>
<dc:creator>New, F.</dc:creator>
<dc:creator>German, R.</dc:creator>
<dc:creator>Storniolo, A. M.</dc:creator>
<dc:creator>Liu, Y.</dc:creator>
<dc:creator>Nakshatri, H.</dc:creator>
<dc:date>2023-10-06</dc:date>
<dc:identifier>doi:10.1101/2023.10.04.560911</dc:identifier>
<dc:title><![CDATA[Single nuclei chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-10-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.10.06.559946v1?rss=1">
<title>
<![CDATA[
Quantifying Adaptive Evolution of the Human Immune Cell Landscape 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.10.06.559946v1?rss=1"
</link>
<description><![CDATA[
The human immune system is under constant evolutionary pressure, primarily through its role as first line of defence against pathogens. Accordingly, population genomics studies have shown that immune-related genes have a high rate of adaptive evolution. These studies, however, are mainly based on protein-coding genes without cellular context, leaving the adaptive role of cell types and states uncharted. Inferring the rate of protein-coding genes adaptation in developing and adult immune cells at cellular resolution, we found cell types from both the lymphoid and myeloid compartments to harbour significantly increased adaptation rates. Specific cell states, such as foetal Pre-Pro B cells and adult T resident memory CD8+ cells show highly elevated rates of adaptation. We further analysed activated cell states, specifically, iPSC-derived macrophages responding to various challenges, including pro- and anti-inflammatory cytokines or bacterial and viral infections, the latter simulating the evolutionary arms race between humans and pathogens. Here, we found positive selection to be concentrated in early immune responses, suggesting benefits for the host to adapt to early stages of infection to control pathogen numbers and spread. Together, our study reveals spatio-temporal and functional biases in human immune populations with evidence of rapid adaptive evolution and provides a retrospect of forces that shaped the complexity, architecture, and function of the human body.
]]></description>
<dc:creator>Salvador-Martinez, I.</dc:creator>
<dc:creator>Murga-Moreno, J.</dc:creator>
<dc:creator>Nieto, J. C.</dc:creator>
<dc:creator>Alsinet, C.</dc:creator>
<dc:creator>Enard, D.</dc:creator>
<dc:creator>Heyn, H.</dc:creator>
<dc:date>2023-10-06</dc:date>
<dc:identifier>doi:10.1101/2023.10.06.559946</dc:identifier>
<dc:title><![CDATA[Quantifying Adaptive Evolution of the Human Immune Cell Landscape]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-10-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.11.20.567825v1?rss=1">
<title>
<![CDATA[
An integrated transcriptomic cell atlas of human endoderm-derived organoids 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.11.20.567825v1?rss=1"
</link>
<description><![CDATA[
Human stem cells can generate complex, multicellular epithelial tissues of endodermal origin in vitro that recapitulate aspects of developing and adult human physiology. These tissues, also called organoids, can be derived from pluripotent stem cells or tissue-resident fetal and adult stem cells. However, it has remained difficult to understand the precision and accuracy of organoid cell states through comparison with primary counterparts, and to comprehensively assess the similarity and differences between organoid protocols. Advances in computational single-cell biology now allow the integration of datasets with high technical variability. Here, we integrate single-cell transcriptomes from 218 samples covering organoids of diverse endoderm-derived tissues including lung, pancreas, intestine, liver, biliary system, stomach, and prostate to establish an initial version of a human endoderm organoid cell atlas (HEOCA). The integration includes nearly one million cells across diverse conditions, data sources and protocols. We align and compare cell types and states between organoid models, and harmonize cell type annotations by mapping the atlas to primary tissue counterparts. To demonstrate utility of the atlas, we focus on intestine and lung, and clarify ontogenic cell states that can be modeled in vitro. We further provide examples of mapping novel data from new organoid protocols to expand the atlas, and showcase how integrating organoid models of disease into the HEOCA identifies altered cell proportions and states between healthy and disease conditions. The atlas makes diverse datasets centrally available, and will be valuable to assess organoid fidelity, characterize perturbed and diseased states, and streamline protocol development.
]]></description>
<dc:creator>Xu, Q.</dc:creator>
<dc:creator>Halle, L.</dc:creator>
<dc:creator>Hediyeh-zadeh, S.</dc:creator>
<dc:creator>Kuijs, M.</dc:creator>
<dc:creator>Kilik, U.</dc:creator>
<dc:creator>Yu, Q.</dc:creator>
<dc:creator>Frum, T.</dc:creator>
<dc:creator>Adam, L.</dc:creator>
<dc:creator>Parikh, S.</dc:creator>
<dc:creator>Gander, M.</dc:creator>
<dc:creator>Kfuri-Rubens, R.</dc:creator>
<dc:creator>Klein, D.</dc:creator>
<dc:creator>He, Z.</dc:creator>
<dc:creator>Fleck, J. S.</dc:creator>
<dc:creator>Oost, K.</dc:creator>
<dc:creator>Kahnwald, M.</dc:creator>
<dc:creator>Barbiero, S.</dc:creator>
<dc:creator>Mitrofanova, O.</dc:creator>
<dc:creator>Maciag, G.</dc:creator>
<dc:creator>Jensen, K. B.</dc:creator>
<dc:creator>Lutolf, M.</dc:creator>
<dc:creator>Liberali, P.</dc:creator>
<dc:creator>Beumer, J.</dc:creator>
<dc:creator>Spence, J. R.</dc:creator>
<dc:creator>Treutlein, B.</dc:creator>
<dc:creator>Theis, F. J.</dc:creator>
<dc:creator>Camp, J. G.</dc:creator>
<dc:date>2023-11-20</dc:date>
<dc:identifier>doi:10.1101/2023.11.20.567825</dc:identifier>
<dc:title><![CDATA[An integrated transcriptomic cell atlas of human endoderm-derived organoids]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-11-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.09.19.558548v1?rss=1">
<title>
<![CDATA[
Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.09.19.558548v1?rss=1"
</link>
<description><![CDATA[
Single-cell spatial transcriptomics such as in-situ hybridization or sequencing technologies can provide subcellular resolution that enables the identification of individual cell identities, locations, and a deep understanding of subcellular mechanisms. However, accurate segmentation and annotation that allows individual cell boundaries to be determined remains a major challenge that limits all the above and downstream insights. Current machine learning methods heavily rely on nuclei or cell body staining, resulting in the significant loss of both transcriptome depth and the limited ability to learn latent representations of spatial colocalization relationships. Here, we propose Bering, a graph deep learning model that leverages transcript colocalization relationships for joint noise-aware cell segmentation and molecular annotation in 2D and 3D spatial transcriptomics data. Graph embeddings for the cell annotation are transferred as a component of multi-modal input for cell segmentation, which is employed to enrich gene relationships throughout the process. To evaluate performance, we benchmarked Bering with state-of-the-art methods and observed significant improvement in cell segmentation accuracies and numbers of detected transcripts across various spatial technologies and tissues. To streamline segmentation processes, we constructed expansive pre-trained models, which yield high segmentation accuracy in new data through transfer learning and self-distillation, demonstrating the generalizability of Bering.
]]></description>
<dc:creator>Jin, K.</dc:creator>
<dc:creator>Zhang, Z.</dc:creator>
<dc:creator>Zhang, K.</dc:creator>
<dc:creator>Viggiani, F.</dc:creator>
<dc:creator>Callahan, C.</dc:creator>
<dc:creator>Tang, J.</dc:creator>
<dc:creator>Aronow, B. J.</dc:creator>
<dc:creator>Shu, J.</dc:creator>
<dc:date>2023-09-22</dc:date>
<dc:identifier>doi:10.1101/2023.09.19.558548</dc:identifier>
<dc:title><![CDATA[Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-09-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.10.25.562925v1?rss=1">
<title>
<![CDATA[
A spatial human thymus cell atlas mapped to a continuous tissue axis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.10.25.562925v1?rss=1"
</link>
<description><![CDATA[
T cells develop from circulating precursors, which enter the thymus and migrate throughout specialised sub-compartments to support maturation and selection. This process starts already in early fetal development and is highly active until the involution of the thymus in adolescence. To map the micro-anatomical underpinnings of this process in pre- vs. post-natal states, we undertook a spatially resolved analysis and established a new quantitative morphological framework for the thymus, the Cortico-Medullary Axis. Using this axis in conjunction with the curation of a multimodal single-cell, spatial transcriptomics and high-resolution multiplex imaging atlas, we show that canonical thymocyte trajectories and thymic epithelial cells are highly organised and fully established by post-conception week 12, pinpoint TEC progenitor states, find that TEC subsets and peripheral tissue genes are associated with Hassalls Corpuscles and uncover divergence in the pace and drivers of medullary entry between CD4 vs. CD8 T cell lineages. These findings are complemented with a holistic toolkit for spatial analysis and annotation, providing a basis for a detailed understanding of T lymphocyte development.
]]></description>
<dc:creator>Yayon, N.</dc:creator>
<dc:creator>Kedlian, V. R.</dc:creator>
<dc:creator>Boehme, L.</dc:creator>
<dc:creator>Suo, C.</dc:creator>
<dc:creator>Wachter, B.</dc:creator>
<dc:creator>Beuschel, R. T.</dc:creator>
<dc:creator>Amsalem, O.</dc:creator>
<dc:creator>Polanski, K.</dc:creator>
<dc:creator>Koplev, S.</dc:creator>
<dc:creator>Tuck, E.</dc:creator>
<dc:creator>Dann, E.</dc:creator>
<dc:creator>Van Hulle, J.</dc:creator>
<dc:creator>Perera, S.</dc:creator>
<dc:creator>Putteman, T.</dc:creator>
<dc:creator>Predeus, A. V.</dc:creator>
<dc:creator>Dabrowska, M.</dc:creator>
<dc:creator>Richardson, L.</dc:creator>
<dc:creator>Tudor, C.</dc:creator>
<dc:creator>Kreins, A. Y.</dc:creator>
<dc:creator>Engelbert, J.</dc:creator>
<dc:creator>Stephenson, E.</dc:creator>
<dc:creator>Kleshchevnikov, V.</dc:creator>
<dc:creator>De Rita, F.</dc:creator>
<dc:creator>Crossland, D.</dc:creator>
<dc:creator>Bosticardo, M.</dc:creator>
<dc:creator>Pala, F.</dc:creator>
<dc:creator>Prigmore, E.</dc:creator>
<dc:creator>Chipampe, N.-J.</dc:creator>
<dc:creator>Prete, M.</dc:creator>
<dc:creator>Fei, L.</dc:creator>
<dc:creator>To, K.</dc:creator>
<dc:creator>Barker, R. A.</dc:creator>
<dc:creator>He, X.</dc:creator>
<dc:creator>Van Nieuwerburgh, F.</dc:creator>
<dc:creator>Bayraktar, O.</dc:creator>
<dc:creator>Patel, M.</dc:creator>
<dc:creator>Davies, G. E.</dc:creator>
<dc:creator>Haniffa, M. A.</dc:creator>
<dc:creator>Uhlmann, V.</dc:creator>
<dc:creator>Notarangelo, L. D.</dc:creator>
<dc:creator>Germain, R. N.</dc:creator>
<dc:creator>Radtke, A. J.</dc:creator>
<dc:creator>Marioni, J. C.</dc:creator>
<dc:creator>Tag</dc:creator>
<dc:date>2023-10-28</dc:date>
<dc:identifier>doi:10.1101/2023.10.25.562925</dc:identifier>
<dc:title><![CDATA[A spatial human thymus cell atlas mapped to a continuous tissue axis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-10-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.01.02.522155v1?rss=1">
<title>
<![CDATA[
Early infection response of the first trimester human placenta at single-cell scale 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.01.02.522155v1?rss=1"
</link>
<description><![CDATA[
Placental infections are a major worldwide burden, particularly in developing countries. The placenta is a transient tissue located at the interface between the mother and the fetus. Some pathogens can access the placental barrier resulting in pathological transmission from mother to fetus, which may have a profound impact on the health of the developing fetus. Limited tissue accessibility, critical differences between humans and mice, and, until recently, lack of proper in vitro models, have hampered our understanding of the early placental response to pathogens. Here we use single-cell transcriptomics to describe the placental primary defence mechanisms against three pathogens that are known to cause fetal and maternal complications during pregnancy - Plasmodium falciparum, Listeria monocytogenes and Toxoplasma gondii. We optimise ex vivo placental explants of the first-trimester human placenta and show that trophoblasts (the epithelial-like cells of the placenta), and Hofbauer cells (placental macrophages) orchestrate a coordinated inflammatory response after 24 hours of infection. We show that hormone biosynthesis and transport are downregulated in the trophoblasts, suggesting that protective responses are promoted at the expense of decreasing other critical functions of the placenta, such as the endocrine production and the nourishment of the fetus. In addition, we pinpoint pathogen-specific effects in some placental lineages, including a strong mitochondrial alteration in the Hofbauer cells in response to T. gondii. Finally, we identify adaptive strategies and validate nutrient acquisition employed by the P. falciparum during placental malaria infection. This study provides the first detailed cellular map of the first-trimester placenta upon infection and describes the early events that may lead to fetal and placental disorders if left unchecked.
]]></description>
<dc:creator>Hoo, R.</dc:creator>
<dc:creator>Ruiz-Morales, E. R.</dc:creator>
<dc:creator>Kelava, I.</dc:creator>
<dc:creator>Sancho-Serra, C.</dc:creator>
<dc:creator>Icoresi Mazzeo, C.</dc:creator>
<dc:creator>Chelaghma, S.</dc:creator>
<dc:creator>Tuck, E.</dc:creator>
<dc:creator>Predeus, A. V.</dc:creator>
<dc:creator>Fernandez-Antoran, D.</dc:creator>
<dc:creator>Waller, R. F.</dc:creator>
<dc:creator>Lee, M.</dc:creator>
<dc:creator>Vento-Tormo, R.</dc:creator>
<dc:date>2023-01-02</dc:date>
<dc:identifier>doi:10.1101/2023.01.02.522155</dc:identifier>
<dc:title><![CDATA[Early infection response of the first trimester human placenta at single-cell scale]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-01-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.10.12.556307v1?rss=1">
<title>
<![CDATA[
A human prenatal skin cell atlas reveals immune cell regulation of skin morphogenesis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.10.12.556307v1?rss=1"
</link>
<description><![CDATA[
Human prenatal skin is populated by innate immune cells including macrophages, and whether they act solely in immunity or have additional functions in morphogenesis is unclear. We assembled the first comprehensive multi-omic reference atlas of prenatal human skin (7-16 post-conception weeks), combining single cell and spatial transcriptomic data, to characterise the skins microenvironmental cellular organisation. This revealed that crosstalk between non-immune and immune cells underpins formation of hair follicles, has implications for scarless wound healing, and is critical for skin angiogenesis. We benchmarked a skin organoid model, derived from human embryonic stem (ES) and induced pluripotent stem (iPS) cells, against prenatal and adult skin, demonstrating close recapitulation of the epidermal and dermal skin components during hair follicle development. Notably, the skin organoid lacked immune cells and had markedly diminished endothelial cell heterogeneity and quantity. From our in vivo skin cell atlas data, we found that macrophages and macrophage-derived growth factors play a key role in driving endothelial development prenatally. Indeed, vascular network formation was enhanced following transfer of autologous iPS-derived macrophages into both endothelial cell angiogenesis assays and skin organoid cultures. In summary, innate immune cells moonlight as key players in skin morphogenesis beyond their conventional immune roles, a function they achieve via extensive crosstalk with non-immune cells. Finally, we leveraged our human prenatal skin cell atlas to further our understanding of the pathogenesis of genetic hair and skin disorders.
]]></description>
<dc:creator>Gopee, N. H.</dc:creator>
<dc:creator>Huang, N.</dc:creator>
<dc:creator>Olabi, B.</dc:creator>
<dc:creator>Admane, C.</dc:creator>
<dc:creator>Botting, R. A.</dc:creator>
<dc:creator>Foster, A. R.</dc:creator>
<dc:creator>Torabi, F.</dc:creator>
<dc:creator>Winheim, E.</dc:creator>
<dc:creator>Sumanaweera, D. N.</dc:creator>
<dc:creator>Goh, I.</dc:creator>
<dc:creator>Miah, M.</dc:creator>
<dc:creator>Stephenson, E.</dc:creator>
<dc:creator>Tun, W. M.</dc:creator>
<dc:creator>Moghimi, P.</dc:creator>
<dc:creator>Rumney, B.</dc:creator>
<dc:creator>He, P.</dc:creator>
<dc:creator>Lawrence, S.</dc:creator>
<dc:creator>Roberts, K.</dc:creator>
<dc:creator>Sidhpura, K.</dc:creator>
<dc:creator>Englebert, J.</dc:creator>
<dc:creator>Jardine, L.</dc:creator>
<dc:creator>Reynolds, G.</dc:creator>
<dc:creator>Rose, A.</dc:creator>
<dc:creator>Ganier, C.</dc:creator>
<dc:creator>Rowe, V.</dc:creator>
<dc:creator>Pritchard, S.</dc:creator>
<dc:creator>Mulas, I.</dc:creator>
<dc:creator>Fletcher, J.</dc:creator>
<dc:creator>Popescu, D.-M.</dc:creator>
<dc:creator>Poyner, E. F.</dc:creator>
<dc:creator>Dubois, A.</dc:creator>
<dc:creator>Filby, A.</dc:creator>
<dc:creator>Lisgo, S.</dc:creator>
<dc:creator>Barker, R. A.</dc:creator>
<dc:creator>PARK, J.-E.</dc:creator>
<dc:creator>Vento-Tormo, R.</dc:creator>
<dc:creator>Le, P. A.</dc:creator>
<dc:creator>Serdy, S.</dc:creator>
<dc:creator>Kim, J.</dc:creator>
<dc:creator>Deakin, C.</dc:creator>
<dc:creator>Lee, J.</dc:creator>
<dc:creator>Nikolova, M. T.</dc:creator>
<dc:creator>Rajan, N.</dc:creator>
<dc:creator>Ballereau, S.</dc:creator>
<dc:creator>Li, T.</dc:creator>
<dc:creator>Moore, J.</dc:creator>
<dc:creator>Horsfall, D.</dc:creator>
<dc:creator>Bas</dc:creator>
<dc:date>2023-10-12</dc:date>
<dc:identifier>doi:10.1101/2023.10.12.556307</dc:identifier>
<dc:title><![CDATA[A human prenatal skin cell atlas reveals immune cell regulation of skin morphogenesis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-10-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.03.14.484345v1?rss=1">
<title>
<![CDATA[
An integrated single-cell atlas of the skeleton from development through adulthood 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.03.14.484345v1?rss=1"
</link>
<description><![CDATA[
The recent growth of single-cell transcriptomics has turned single-cell RNA sequencing (scRNA-seq) into a near-routine experiment. Breakthroughs in improving scalability have led to the creation of organism-wide transcriptomic datasets, aiming to comprehensively profile the cell types and states within an organism throughout its lifecycle. To date, however, the skeleton remains a majorly underrepresented organ system in organism-wide atlases. Considering how the skeleton not only serves as the central framework of the vertebrate body but is also the home of the hematopoietic niche and a central player in major metabolic and homeostatic processes, this presents a major deficit in current reference atlas projects. To address this issue, we integrated ten separate scRNA-seq datasets containing limb skeletal cells and their developmental precursors, generating an atlas of 133 332 cells. This limb skeletal cell atlas describes cells across the mesenchymal lineage from the induction of the limb to the adult bone and encompasses 39 different cell states. Furthermore, expanding the repertoire of available time points and cell types within a single dataset allowed for more complete analyses of cell-cell communication or in silico perturbation studies. Taken together, we present a missing piece in the current atlas mapping efforts, which will be of value to researchers in the fields of skeletal biology, hematopoiesis, metabolism and regenerative medicine.
]]></description>
<dc:creator>Herpelinck, T.</dc:creator>
<dc:creator>Ory, L.</dc:creator>
<dc:creator>Nasello, G.</dc:creator>
<dc:creator>Barzegari, M.</dc:creator>
<dc:creator>Bolander, J.</dc:creator>
<dc:creator>Luyten, F. P.</dc:creator>
<dc:creator>Tylzanowski, P.</dc:creator>
<dc:creator>Geris, L.</dc:creator>
<dc:date>2022-03-15</dc:date>
<dc:identifier>doi:10.1101/2022.03.14.484345</dc:identifier>
<dc:title><![CDATA[An integrated single-cell atlas of the skeleton from development through adulthood]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-03-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.12.09.570903v1?rss=1">
<title>
<![CDATA[
uniLIVER: a Human Liver Cell Atlas for Data-Driven Cellular State Mapping 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.12.09.570903v1?rss=1"
</link>
<description><![CDATA[
The liver performs several vital functions such as metabolism, toxin removal and glucose storage through the coordination of various cell types. The cell type compositions and cellular states undergo significant changes in abnormal conditions such as fatty liver, cirrhosis and liver cancer. As the recent breakthrough of the single-cell/single-nucleus RNA-seq (sc/snRNA-seq) techniques, there is a great opportunity to establish a reference cell map of liver at single cell resolution with transcriptome-wise features. In this study, we build a unified liver cell atlas uniLIVER by integrative analyzing a large-scale sc/snRNA-seq data collection of normal human liver with 331,125 cells and 79 samples from 6 datasets. Besides the hierarchical cell type annotations, uniLIVER also proposed a novel data-driven strategy to map any query dataset to the normal reference map by developing a machine learning based framework named LiverCT. Applying LiverCT on the datasets from multiple abnormal conditions (1,867,641 cells and 439 samples from 12 datasets), the alterations of cell type compositions and cellular states were systematically investigated in liver cancer.
]]></description>
<dc:creator>Wu, Y.</dc:creator>
<dc:creator>Fan, Y.</dc:creator>
<dc:creator>Miao, Y.</dc:creator>
<dc:creator>Li, Y.</dc:creator>
<dc:creator>Chen, Z.</dc:creator>
<dc:creator>Du, G.</dc:creator>
<dc:creator>Diao, J.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Ye, M.</dc:creator>
<dc:creator>You, R.</dc:creator>
<dc:creator>Chen, A.</dc:creator>
<dc:creator>Chen, Y.</dc:creator>
<dc:creator>Li, W.</dc:creator>
<dc:creator>Guo, W.</dc:creator>
<dc:creator>Dong, J.</dc:creator>
<dc:creator>Zhang, X.</dc:creator>
<dc:creator>Wang, Y.</dc:creator>
<dc:creator>Gu, J.</dc:creator>
<dc:date>2023-12-09</dc:date>
<dc:identifier>doi:10.1101/2023.12.09.570903</dc:identifier>
<dc:title><![CDATA[uniLIVER: a Human Liver Cell Atlas for Data-Driven Cellular State Mapping]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-12-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.11.06.565474v1?rss=1">
<title>
<![CDATA[
Cutaneous T cell lymphoma atlas reveals malignant Th2 cells supported by a B cell-rich microenvironment 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.11.06.565474v1?rss=1"
</link>
<description><![CDATA[
Cutaneous T-cell lymphoma (CTCL) is a potentially fatal clonal malignancy of T cells primarily affecting the skin. The most common form of CTCL, mycosis fungoides (MF), can be difficult to diagnose resulting in treatment delay. The pathogenesis of CTCL is not fully understood due to limited data from patient studies. We performed single-cell RNA sequencing and spatial transcriptomics profiling of skin from patients with MF-type CTCL, and an integrated comparative analysis with human skin cell atlas datasets from healthy skin, atopic dermatitis and psoriasis. We reveal the co-optation of Th2-immune gene programmes by malignant CTCL cells and modelling of the tumour microenvironment to support their survival. We identify MHC-II+ fibroblast subsets reminiscent of lymph node T-zone reticular cells and monocyte-derived dendritic cells that can maintain Th2-like tumour cells. CTCL Th2-like tumour cells are spatially associated with B cells, forming aggregates reminiscent of tertiary lymphoid structures which are more prominent with progressive disease. Finally, we validated the enrichment of B cells in CTCL skin infiltrates and its association with disease progression across three independent patient cohorts. Our findings provide diagnostic aids, potential biomarkers for disease staging and therapeutic strategies for CTCL.
]]></description>
<dc:creator>Li, R.</dc:creator>
<dc:creator>Strobl, J.</dc:creator>
<dc:creator>Poyner, E. F. M.</dc:creator>
<dc:creator>Torabi, F.</dc:creator>
<dc:creator>Mazin, P.</dc:creator>
<dc:creator>Chipampe, N.-J.</dc:creator>
<dc:creator>Stephenson, E.</dc:creator>
<dc:creator>Gardner, L.</dc:creator>
<dc:creator>Olabi, B.</dc:creator>
<dc:creator>Coulthard, R.</dc:creator>
<dc:creator>Botting, R. A.</dc:creator>
<dc:creator>Zila, N.</dc:creator>
<dc:creator>Prigmore, E.</dc:creator>
<dc:creator>Gopee, N.</dc:creator>
<dc:creator>Engelbert, J.</dc:creator>
<dc:creator>Goh, I.</dc:creator>
<dc:creator>Chan, H. M.</dc:creator>
<dc:creator>Johnson, H.</dc:creator>
<dc:creator>Ellis, J.</dc:creator>
<dc:creator>Rowe, V.</dc:creator>
<dc:creator>Tun, W.</dc:creator>
<dc:creator>Reynolds, G.</dc:creator>
<dc:creator>Foster, A. R.</dc:creator>
<dc:creator>Gambardella, L.</dc:creator>
<dc:creator>Winheim, E.</dc:creator>
<dc:creator>Admane, C.</dc:creator>
<dc:creator>Rumney, B.</dc:creator>
<dc:creator>Steele, L.</dc:creator>
<dc:creator>Jardine, L.</dc:creator>
<dc:creator>Nenonen, J.</dc:creator>
<dc:creator>Pickard, K.</dc:creator>
<dc:creator>Lumley, J.</dc:creator>
<dc:creator>Hampton, P.</dc:creator>
<dc:creator>Hu, S.</dc:creator>
<dc:creator>Liu, F.</dc:creator>
<dc:creator>Liu, X.</dc:creator>
<dc:creator>Horsfall, D.</dc:creator>
<dc:creator>Basurto-Lozada, D.</dc:creator>
<dc:creator>Grimble, L.</dc:creator>
<dc:creator>Bacon, C. M.</dc:creator>
<dc:creator>Weatherhead, S.</dc:creator>
<dc:creator>Brauner, H.</dc:creator>
<dc:creator>Wang, Y.</dc:creator>
<dc:creator>Bai, F.</dc:creator>
<dc:creator>Reynolds, N. J.</dc:creator>
<dc:creator>Allen, J. E.</dc:creator>
<dc:creator>Jonak, C.</dc:creator>
<dc:creator></dc:creator>
<dc:date>2023-11-07</dc:date>
<dc:identifier>doi:10.1101/2023.11.06.565474</dc:identifier>
<dc:title><![CDATA[Cutaneous T cell lymphoma atlas reveals malignant Th2 cells supported by a B cell-rich microenvironment]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-11-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.11.03.564728v1?rss=1">
<title>
<![CDATA[
An integrated single-cell reference atlas of the human endometrium 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.11.03.564728v1?rss=1"
</link>
<description><![CDATA[
The human endometrium, the inner lining of the uterus, exhibits complex, dynamic changes throughout the menstrual cycle in response to ovarian hormones. Aberrant response of endometrial cells to hormones is associated with multiple disorders, including endometriosis. Previous single-cell studies of the endometrium profiled a limited number of donors and lacked consensus in defining cell types and states. Here, we introduce the Human Endometrial Cell Atlas (HECA), a high-resolution single-cell reference atlas, combining published and newly generated single-cell transcriptomics datasets of endometrial biopsies of women with and without endometriosis. The HECA assigned consensus cell types and states, and uncovered novel ones, which we mapped in situ using spatial transcriptomics. We quantified how coordinated interactions between cell states in space and time contribute to endometrial regeneration and differentiation. In the continuously changing functionalis layer, we identified an intricate coordination of TGF{beta} signalling between stromal and epithelial cells, likely crucial for cell differentiation. In the basalis layer, we defined signalling between fibroblasts and a new epithelial cell population expressing epithelial stem/progenitor markers, suggesting their role in endometrial regeneration. Additionally, integrating the HECA single-cell data with genome-wide association study data and comparing endometrial samples from women with and without endometriosis, we pinpointed subsets of decidualised stromal cells and macrophages as the most dysregulated cell states in endometriosis. Overall, the HECA is an invaluable resource for studying endometrial physiology, investigating endometrial disorders, and guiding the creation of endometrial microphysiological in vitro systems.
]]></description>
<dc:creator>Mareckova, M.</dc:creator>
<dc:creator>Garcia-Alonso, L.</dc:creator>
<dc:creator>Moullet, M.</dc:creator>
<dc:creator>Lorenzi, V.</dc:creator>
<dc:creator>Petryszak, R.</dc:creator>
<dc:creator>Sancho-Serra, C.</dc:creator>
<dc:creator>Oszlanczi, A.</dc:creator>
<dc:creator>Icoresi Mazzeo, C.</dc:creator>
<dc:creator>Hoffman, S.</dc:creator>
<dc:creator>Krassowski, M.</dc:creator>
<dc:creator>Garbutt, K.</dc:creator>
<dc:creator>Kelava, I.</dc:creator>
<dc:creator>Gaitskell, K.</dc:creator>
<dc:creator>Yancheva, S.</dc:creator>
<dc:creator>Von Woon, E.</dc:creator>
<dc:creator>Male, V.</dc:creator>
<dc:creator>Granne, I.</dc:creator>
<dc:creator>Hellner, K.</dc:creator>
<dc:creator>Mahbubani, K. T.</dc:creator>
<dc:creator>Saeb-Parsy, K.</dc:creator>
<dc:creator>Lotfollahi, M.</dc:creator>
<dc:creator>Prigmore, E.</dc:creator>
<dc:creator>Southcombe, J.</dc:creator>
<dc:creator>Dragovic, R. A.</dc:creator>
<dc:creator>Becker, C. M.</dc:creator>
<dc:creator>Zondervan, K. T.</dc:creator>
<dc:creator>Vento-Tormo, R.</dc:creator>
<dc:date>2023-11-05</dc:date>
<dc:identifier>doi:10.1101/2023.11.03.564728</dc:identifier>
<dc:title><![CDATA[An integrated single-cell reference atlas of the human endometrium]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-11-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.10.18.464715v1?rss=1">
<title>
<![CDATA[
Molecular atlas of the human brain vasculature at the single-cell level 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.10.18.464715v1?rss=1"
</link>
<description><![CDATA[
A broad range of brain pathologies critically relies on the vasculature, and cerebrovascular disease is a leading cause of death worldwide. However, the cellular and molecular architecture of the human brain vasculature remains poorly understood. Here, we performed single-cell RNA sequencing of 599,215 freshly isolated endothelial, perivascular and other tissue-derived cells from 47 fetuses and adult patients to construct a molecular atlas of the developing fetal, adult control and diseased human brain vasculature. We uncover extensive molecular heterogeneity of healthy fetal and adult human brains and across eight vascular-dependent CNS pathologies including brain tumors and brain vascular malformations. We identify alteration of arteriovenous differentiation and reactivated fetal as well as conserved dysregulated pathways in the diseased vasculature. Pathological endothelial cells display a loss of CNS-specific properties and reveal an upregulation of MHC class II molecules, indicating atypical features of CNS endothelial cells. Cell-cell interaction analyses predict numerous endothelial-to-perivascular cell ligand-receptor crosstalk including immune-related and angiogenic pathways, thereby unraveling a central role for the endothelium within brain neurovascular unit signaling networks. Our single-cell brain atlas provides insight into the molecular architecture and heterogeneity of the developing, adult/control and diseased human brain vasculature and serves as a powerful reference for future studies.
]]></description>
<dc:creator>Wälchli, T.</dc:creator>
<dc:creator>Ghobrial, M.</dc:creator>
<dc:creator>Schwab, M.</dc:creator>
<dc:creator>Takada, S.</dc:creator>
<dc:creator>Zhong, H.</dc:creator>
<dc:creator>Suntharalingham, S.</dc:creator>
<dc:creator>Vetiska, S.</dc:creator>
<dc:creator>Rodrigues Rodrigues, D.</dc:creator>
<dc:creator>Rehrauer, H.</dc:creator>
<dc:creator>Wu, R.</dc:creator>
<dc:creator>Yu, K.</dc:creator>
<dc:creator>Bisschop, J.</dc:creator>
<dc:creator>Farnhammer, F.</dc:creator>
<dc:creator>Regli, L.</dc:creator>
<dc:creator>Schaller, K.</dc:creator>
<dc:creator>Frei, K.</dc:creator>
<dc:creator>Ketela, T.</dc:creator>
<dc:creator>Bernstein, M.</dc:creator>
<dc:creator>Kongkham, P.</dc:creator>
<dc:creator>Carmeliet, P.</dc:creator>
<dc:creator>Valiante, T.</dc:creator>
<dc:creator>Dirks, P. B.</dc:creator>
<dc:creator>Suva, M. L.</dc:creator>
<dc:creator>Zadeh, G.</dc:creator>
<dc:creator>Tabar, V.</dc:creator>
<dc:creator>Schlapbach, R.</dc:creator>
<dc:creator>De Bock, K.</dc:creator>
<dc:creator>Fish, J.</dc:creator>
<dc:creator>Monnier, P.</dc:creator>
<dc:creator>Bader, G.</dc:creator>
<dc:creator>Radovanovic, I.</dc:creator>
<dc:date>2021-10-19</dc:date>
<dc:identifier>doi:10.1101/2021.10.18.464715</dc:identifier>
<dc:title><![CDATA[Molecular atlas of the human brain vasculature at the single-cell level]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-10-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.11.11.566717v1?rss=1">
<title>
<![CDATA[
Cell type-specific effects of age and sex on human cortical neurons 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.11.11.566717v1?rss=1"
</link>
<description><![CDATA[
Excitatory and inhibitory neurons establish specialized identities early in life through cell type-specific patterns of epigenetic regulation and gene expression. Although cell types are largely stable throughout the lifespan, altered transcriptional and epigenetic regulation may contribute to cognitive changes with advanced age. Using single-nucleus multiomic DNA methylation and transcriptome sequencing (snmCT-seq) in frontal cortex samples from young adult and aged donors, we found widespread age- and sex-related variability in specific neuronal cell types. The proportion of GABAergic inhibitory cells, including SST and VIP expressing cells, was reduced in aged donors. On the other hand, excitatory neurons had more profound age-related changes in their gene expression and DNA methylation compared with inhibitory cells. Hundreds of genes involved in synaptic activity were downregulated, while genes located in subtelomeric regions were upregulated with age and anti-correlated with telomere length. We further mapped sex differences in autosomal gene expression and escape from X-inactivation in specific neuron types. Multiomic single-nucleus epigenomes and transcriptomes provide new insight into the effects of age and sex on human neurons.
]]></description>
<dc:creator>Chien, J.-F.</dc:creator>
<dc:creator>Liu, H.</dc:creator>
<dc:creator>Wang, B.-A.</dc:creator>
<dc:creator>Luo, C.</dc:creator>
<dc:creator>Bartlett, A.</dc:creator>
<dc:creator>Castanon, R.</dc:creator>
<dc:creator>Johnson, N. D.</dc:creator>
<dc:creator>Nery, J. R.</dc:creator>
<dc:creator>Osteen, J.</dc:creator>
<dc:creator>Li, J.</dc:creator>
<dc:creator>Altshul, J.</dc:creator>
<dc:creator>Kenworthy, M.</dc:creator>
<dc:creator>Valadon, C.</dc:creator>
<dc:creator>Liem, M.</dc:creator>
<dc:creator>Claffey, N.</dc:creator>
<dc:creator>O'Connor, C.</dc:creator>
<dc:creator>Seeker, L. A.</dc:creator>
<dc:creator>Ecker, J. R.</dc:creator>
<dc:creator>Behrens, M. M.</dc:creator>
<dc:creator>Mukamel, E. A.</dc:creator>
<dc:date>2023-11-15</dc:date>
<dc:identifier>doi:10.1101/2023.11.11.566717</dc:identifier>
<dc:title><![CDATA[Cell type-specific effects of age and sex on human cortical neurons]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-11-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.03.11.434413v1?rss=1">
<title>
<![CDATA[
Single Cell Transcriptomic Landscape of Diabetic Foot Ulcers 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.03.11.434413v1?rss=1"
</link>
<description><![CDATA[
To understand the diabetic wound healing microenvironment, we profiled 174,962 single cells from foot, forearm, and PBMCs using single-cell RNA sequencing (scRNASeq) approach. Our analysis shows enrichment of a unique population of fibroblasts overexpressing MMP1, MMP3, MMP11, HIF1A, CHI3L1, and TNFAIP6 genes and M1 macrophage polarization in the DFU patients with healing wounds. Further, scRNASeq of spatially separated samples from same patient and spatial transcriptomics (ST) revealed preferential localization of these healing associated fibroblasts toward deep wound/ulcer bed as compared to wound edge or non-wounded skin. ST also validated our findings of higher enrichment of M1 macrophages in healers and M2 macrophages in non-healers. Our analysis provides deep insights into the wound healing microenvironment, identifying cell types that could be critical in promoting DFU healing, and may inform novel therapeutic approaches for DFU treatment.
]]></description>
<dc:creator>Theocharidis, G.</dc:creator>
<dc:creator>Thomas, B. E.</dc:creator>
<dc:creator>Sarkar, D.</dc:creator>
<dc:creator>Pilcher, W. J.</dc:creator>
<dc:creator>Dwivedi, B.</dc:creator>
<dc:creator>Sandoval-Schaefer, T.</dc:creator>
<dc:creator>Sirbulescu, R. F.</dc:creator>
<dc:creator>Kafanas, A.</dc:creator>
<dc:creator>Mezghani, I.</dc:creator>
<dc:creator>Wang, P.</dc:creator>
<dc:creator>Lobao, A.</dc:creator>
<dc:creator>Vlachos, I.</dc:creator>
<dc:creator>Dash, B.</dc:creator>
<dc:creator>Hsia, H. C.</dc:creator>
<dc:creator>Horsley, V.</dc:creator>
<dc:creator>Bhasin, S. S.</dc:creator>
<dc:creator>Veves, A.</dc:creator>
<dc:creator>Bhasin, M.</dc:creator>
<dc:date>2021-03-12</dc:date>
<dc:identifier>doi:10.1101/2021.03.11.434413</dc:identifier>
<dc:title><![CDATA[Single Cell Transcriptomic Landscape of Diabetic Foot Ulcers]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-03-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.10.19.464575v1?rss=1">
<title>
<![CDATA[
Single cell transcriptomics reveals distinct effector profiles of infiltrating T cells in lupus skin and kidney 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.10.19.464575v1?rss=1"
</link>
<description><![CDATA[
Cutaneous lupus is commonly present in patients with systemic lupus erythematosus (SLE) but can also exist as an isolated manifestation without further systemic involvement. T cells have been strongly suspected to contribute to the pathology of cutaneous lupus, yet our understanding of the T cell phenotypes and functions in the skin in lupus remains incomplete, and the extent to which lupus T cell infiltrates in skin resemble those in other tissue beds is unknown. Here, we present a detailed single-cell RNA sequencing profile of T and NK cell populations present within lesional and non-lesional skin biopsies of patients with cutaneous lupus. We identified multiple lymphocyte clusters, including both CD4 and CD8 T cells, NK cells, regulatory T cells, and a population of strongly interferon-responding cells that was present in patients with cutaneous lupus but absent in healthy donors. T cells across clusters from both lesional and non-lesional skin biopsies expressed elevated levels of interferon simulated genes (ISGs); however, compared to T cells from control skin, T cells from cutaneous lupus lesions did not show elevated expression profiles of activation, cytotoxicity, or exhaustion. Integrated analyses comparing skin T/NK cells to lupus nephritis kidney T/NK cells indicated that skin lymphocytes appeared less activated and lacked the expanded cytotoxic populations prominent in lupus nephritis. An integrated comparison of skin T cells from lupus and systemic sclerosis revealed similar activation profiles but an elevated ISG signature specific to cells from lupus skin biopsies. Overall, these data represent the first detailed transcriptomic analysis of the of T and NK cells in cutaneous lupus at the single cell level and have enabled a cross-tissue comparison that highlighted the stark differences in composition and activation of T/NK cells in distinct tissues in lupus.
]]></description>
<dc:creator>Dunlap, G. S.</dc:creator>
<dc:creator>Billi, A. C.</dc:creator>
<dc:creator>Ma, F.</dc:creator>
<dc:creator>Maz, M.</dc:creator>
<dc:creator>Tsoi, L. C.</dc:creator>
<dc:creator>Wasikowski, R.</dc:creator>
<dc:creator>Gudjonsson, J. E.</dc:creator>
<dc:creator>Kahlenberg, J. M.</dc:creator>
<dc:creator>Rao, D. A.</dc:creator>
<dc:date>2021-10-19</dc:date>
<dc:identifier>doi:10.1101/2021.10.19.464575</dc:identifier>
<dc:title><![CDATA[Single cell transcriptomics reveals distinct effector profiles of infiltrating T cells in lupus skin and kidney]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-10-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/784579v1?rss=1">
<title>
<![CDATA[
Single cell transcriptomics of human epidermis reveals basal stem cell transition states 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/784579v1?rss=1"
</link>
<description><![CDATA[
How stem cells give rise to human interfollicular epidermis is unclear despite the crucial role the epidermis plays in barrier and appendage formation. Here we use single cell-RNA sequencing to interrogate basal stem cell heterogeneity of human interfollicular epidermis and find at least four spatially distinct stem cell populations that decorate the top and bottom of rete ridge architecture and hold transitional positions between the basal and suprabasal epidermal layers. Cell-cell communication modeling through co-variance of cognate ligand-receptor pairs indicate that the basal cell populations distinctly serve as critical signaling hubs that maintain epidermal communication. Combining pseudotime, RNA velocity, and cellular entropy analyses point to a hierarchical differentiation lineage supporting multi-stem cell interfollicular epidermal homeostasis models and suggest the "transitional" basal stem cells are stable states essential for proper stratification. Finally, alterations in differentially expressed "transitional" basal stem cell genes result in severe thinning of human skin equivalents, validating their essential role in epidermal homeostasis and reinforcing the critical nature of basal stem cell heterogeneity.
]]></description>
<dc:creator>Wang, S.</dc:creator>
<dc:creator>Drummond, M. L.</dc:creator>
<dc:creator>Guerrero-Juarez, C. F.</dc:creator>
<dc:creator>Tarapore, E.</dc:creator>
<dc:creator>MacLean, A. L.</dc:creator>
<dc:creator>Stabell, A. R.</dc:creator>
<dc:creator>Wu, S. C.</dc:creator>
<dc:creator>Gutierrez, G.</dc:creator>
<dc:creator>That, B. T.</dc:creator>
<dc:creator>Benavente, C. A.</dc:creator>
<dc:creator>Nie, Q.</dc:creator>
<dc:creator>Atwood, S. X.</dc:creator>
<dc:date>2019-09-30</dc:date>
<dc:identifier>doi:10.1101/784579</dc:identifier>
<dc:title><![CDATA[Single cell transcriptomics of human epidermis reveals basal stem cell transition states]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-09-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.04.30.442148v1?rss=1">
<title>
<![CDATA[
Myofibroblast transcriptome indicates SFRP2+ fibroblast progenitors in systemic sclerosis skin 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.04.30.442148v1?rss=1"
</link>
<description><![CDATA[
Skin and lung fibrosis in systemic sclerosis (SSc) is driven by myofibroblasts, alpha-smooth muscle actin expressing cells that arise from a variety of cell types in murine fibrosis models. Utilizing single cell RNA-sequencing to examine the transcriptome changes, we show that SSc dermal myofibroblasts arise from an SFRP2/DPP4-expressing progenitor fibroblast population that globally upregulates expression of transcriptome markers, such as PRSS23 and THBS1. Only a fraction of SSc fibroblasts differentiate into myofibroblasts, as shown by expression of additional markers, SFRP4 and FNDC1. The myofibroblast transcriptome implicates upstream transcription factors that drive myofibroblast differentiation.
]]></description>
<dc:creator>Tabib, T.</dc:creator>
<dc:creator>Huang, M. A.</dc:creator>
<dc:creator>Morse, C.</dc:creator>
<dc:creator>Papazoglou, A.</dc:creator>
<dc:creator>Behera, R.</dc:creator>
<dc:creator>Jia, M.</dc:creator>
<dc:creator>Bulik, M.</dc:creator>
<dc:creator>Monier, D. E.</dc:creator>
<dc:creator>Benos, P. V.</dc:creator>
<dc:creator>Chen, W.</dc:creator>
<dc:creator>Domsic, R.</dc:creator>
<dc:creator>Lafyatis, R.</dc:creator>
<dc:date>2021-04-30</dc:date>
<dc:identifier>doi:10.1101/2021.04.30.442148</dc:identifier>
<dc:title><![CDATA[Myofibroblast transcriptome indicates SFRP2+ fibroblast progenitors in systemic sclerosis skin]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-04-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.10.21.465323v1?rss=1">
<title>
<![CDATA[
Defining cellular complexity in human autosomal dominant polycystic kidney disease by multimodal single cell analysis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.10.21.465323v1?rss=1"
</link>
<description><![CDATA[
Autosomal dominant polycystic kidney disease (ADPKD) is the leading genetic cause of end stage renal disease and is characterized by the formation and progressive expansion of kidney cysts. Most ADPKD cases arise from mutations in either the PKD1 or PKD2 gene but the precise downstream signaling pathways driving cyst growth are not well understood, and relatively few studies investigate human cystic kidney due to sample scarcity. In order to better understand the cell types and states driving human ADPKD progression, we analyzed eight ADPKD and five healthy human kidney samples, generating a single cell multiomic atlas consisting of ~100,000 single nucleus transcriptomes and ~50,000 single nucleus epigenomes. The integrated datasets identified 11 primary cell clusters including most epithelial cell types as well as large endothelial and fibroblast cell clusters. Proximal tubular cells from ADPKD kidneys expressed a failed repair transcriptomic signature characterized by profibrotic and proinflammatory transcripts. We identified the G protein-coupled receptor GPRC5A as specifically upregulated in cyst lining cells derived from collecting duct. The principal cell subpopulation enriched for GPRC5A expression also exhibited increased transcription factor binding motif availability for NF-{kappa}B, TEAD, CREB and retinoic acid receptor families and we identified and validated a distal enhancer regulating GPRC5A expression containing these transcription factor binding motifs. This study establishes the single cell transcriptomic and epigenomic landscape of ADPKD, revealing previously unrecognized cellular heterogeneity.
]]></description>
<dc:creator>Muto, Y.</dc:creator>
<dc:creator>Dixon, E. E.</dc:creator>
<dc:creator>Yoshimura, Y.</dc:creator>
<dc:creator>Wu, H.</dc:creator>
<dc:creator>Omachi, K.</dc:creator>
<dc:creator>King, A. J.</dc:creator>
<dc:creator>Olson, E. N.</dc:creator>
<dc:creator>Gunawan, M. G.</dc:creator>
<dc:creator>Kuo, J. J.</dc:creator>
<dc:creator>Cox, J.</dc:creator>
<dc:creator>Miner, J. H.</dc:creator>
<dc:creator>Seliger, S. L.</dc:creator>
<dc:creator>Woodward, O. M.</dc:creator>
<dc:creator>Welling, P. A.</dc:creator>
<dc:creator>Watnick, T. J.</dc:creator>
<dc:creator>Humphreys, B. D.</dc:creator>
<dc:date>2021-10-22</dc:date>
<dc:identifier>doi:10.1101/2021.10.21.465323</dc:identifier>
<dc:title><![CDATA[Defining cellular complexity in human autosomal dominant polycystic kidney disease by multimodal single cell analysis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-10-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/645424v1?rss=1">
<title>
<![CDATA[
The Single Cell Transcriptomic Landscape of Early Human Diabetic Nephropathy 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/645424v1?rss=1"
</link>
<description><![CDATA[
Diabetic nephropathy is characterized by damage to both the glomerulus and tubulointerstitium, but relatively little is known about accompanying cell-specific changes in gene expression. We performed unbiased single nucleus RNA sequencing (snRNAseq) on cryopreserved human diabetic kidney samples to generate 23,980 single nucleus transcriptomes from three control and three early diabetic nephropathy samples. All major cell types of the kidney were represented in the final dataset. Side by side comparison demonstrated cell-type-specific changes in gene expression that are important for ion transport, angiogenesis, and immune cell activation. In particular, we show that the diabetic loop of Henle, late distal convoluted tubule, and principal cells all adopt a gene expression signature consistent with increased potassium secretion, including alterations in Na-K+-ATPase, WNK1, mineralocorticoid receptor and NEDD4L expression, as well as decreased paracellular calcium and magnesium reabsorption. We also identify strong angiogenic signatures in glomerular cell types, proximal convoluted tubule, distal convoluted tubule and principal cells. Taken together, these results suggest that increased potassium secretion and angiogenic signaling represent early kidney responses in human diabetic nephropathy.nnSignificance StatementSingle nucleus RNA sequencing revealed gene expression changes in early diabetic nephropathy that promote urinary potassium secretion and decreased calcium and magnesium reabsorption. Multiple cell types exhibited angiogenic signatures, which may represent early signs of aberrant angiogenesis. These alterations may help to identify biomarkers for disease progression or signaling pathways amenable to early intervention.nnO_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY
]]></description>
<dc:creator>Wilson, P. C.</dc:creator>
<dc:creator>Wu, H.</dc:creator>
<dc:creator>Kirita, Y.</dc:creator>
<dc:creator>Uchimura, K.</dc:creator>
<dc:creator>Rennke, H. G.</dc:creator>
<dc:creator>Welling, P. A.</dc:creator>
<dc:creator>Waikar, S. S.</dc:creator>
<dc:creator>Humphreys, B. D.</dc:creator>
<dc:date>2019-05-24</dc:date>
<dc:identifier>doi:10.1101/645424</dc:identifier>
<dc:title><![CDATA[The Single Cell Transcriptomic Landscape of Early Human Diabetic Nephropathy]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-05-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.03.10.986075v1?rss=1">
<title>
<![CDATA[
Harnessing Expressed Single Nucleotide Variation and Single Cell RNA Sequencing to Define Immune Cell Chimerism in the Rejecting Kidney Transplant 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.03.10.986075v1?rss=1"
</link>
<description><![CDATA[
In solid organ transplantation, donor derived immune cells are assumed to decline with time after surgery. Whether donor leukocytes persist within kidney transplants or play any role in rejection is unknown, however, in part because of limited techniques for distinguishing recipient and donor cells. To address this question, we performed paired whole exome sequencing of donor and recipient DNA and single cell RNA sequencing (scRNA-seq) of 5 human kidney transplant biopsy cores. Exome sequences were used to define single nucleotide variations (SNV) across all samples. By analyzing expressed SNVs in the scRNA-seq dataset we could define recipient vs. donor cell origin for all 81,139 cells. The leukocyte donor to recipient ratio varied with rejection status for macrophages and with time post-transplant for lymphocytes. Recipient macrophages were characterized by inflammatory activation and donor macrophages by antigen presentation and complement signaling. Recipient origin T cells expressed cytotoxic and pro-inflammatory genes consistent with an effector cell phenotype whereas donor origin T cells are likely quiescent expressing oxidative phosphorylation genes relative to recipient T cells. Finally, both donor and recipient T cell clones were present within the rejecting kidney, suggesting lymphoid aggregation. Our results indicate that donor origin macrophages and T cells have distinct transcriptional profiles compared to their recipient counterparts and donor macrophages can persist for years post transplantation. This study demonstrates the power of this approach to accurately define leukocyte chimerism in a complex tissue such as the kidney transplant coupled with the ability to examine transcriptional profiles at single cell resolution.
]]></description>
<dc:creator>Malone, A. F.</dc:creator>
<dc:creator>Wu, H.</dc:creator>
<dc:creator>Fronick, C.</dc:creator>
<dc:creator>Fulton, R.</dc:creator>
<dc:creator>Gaut, J. P.</dc:creator>
<dc:creator>Humphreys, B. D.</dc:creator>
<dc:date>2020-03-11</dc:date>
<dc:identifier>doi:10.1101/2020.03.10.986075</dc:identifier>
<dc:title><![CDATA[Harnessing Expressed Single Nucleotide Variation and Single Cell RNA Sequencing to Define Immune Cell Chimerism in the Rejecting Kidney Transplant]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-03-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.09.11.507463v1?rss=1">
<title>
<![CDATA[
Defining Cardiac Recovery at Single Cell Resolution 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.09.11.507463v1?rss=1"
</link>
<description><![CDATA[
Recovery of cardiac function is the ultimate goal of heart failure therapy. Unfortunately, cardiac recovery remains a rare and poorly understood phemomenon. Herein, we performed single nucleus RNA-sequencing (snRNA-seq) from non-diseased donors and heart failure patients. By comparing patients who recovered LV systolic function following LV assist device implantation to those who did not recover and donors, we defined the cellular and transcriptional landscape and predictors of cardiac recovery. We sequenced 40 hearts and recovered 185,881 nuclei with 13 distinct cell types. Using pseudobulk differential expression analysis to explicate cell specific signatures of cardiac recovery, we observed that recovered cardiomyocytes do not revert to a normal state, and instead, retain transcriptional signatures observed in heart failure. Macrophages and fibroblasts displayed the strongest signatures of recovery. While some evidence of reversion to a normal state was observed, many heart failure associated genes remained elevated and recovery signatures were predominately indicative of a biological state that was unique from donor and heart failure conditions. Acquisition of recovery states was associated with improved LV systolic function. Pro-inflammatory macrophages and inflammatory signaling in fibroblasts were identified as negative predictors of recovery. We identified downregulation of RUNX1 transcriptional activity in macrophages and fibroblasts as a central event associated with and predictive of cardiac recovery. In silico perturbation of RUNX1 in macrophages and fibroblasts recapitulated the transcriptional state of cardiac recovery. This prediction was corroborated in a mouse model of cardiac recovery mediated by BRD4 inhibition where we observed a decrease in macrophage and fibroblast Runx1 expression, diminished chromatin accessibility within peaks linked to the Runx1 locus, and acquisition of recovery signatures. These findings suggest that cardiac recovery is a unique biological state and identify RUNX1 as a possible therapeutic target to facilitate cardiac recovery.
]]></description>
<dc:creator>Amrute, J. M.</dc:creator>
<dc:creator>Lai, L.</dc:creator>
<dc:creator>Ma, P.</dc:creator>
<dc:creator>Koenig, A. L.</dc:creator>
<dc:creator>Kamimoto, K.</dc:creator>
<dc:creator>Bredemeyer, A.</dc:creator>
<dc:creator>Shankar, T. S.</dc:creator>
<dc:creator>Kuppe, C.</dc:creator>
<dc:creator>Kadyrov, F. F.</dc:creator>
<dc:creator>Schulte, L. J.</dc:creator>
<dc:creator>Stoutenburg, D.</dc:creator>
<dc:creator>Kopecky, B.</dc:creator>
<dc:creator>Navankasattusas, S.</dc:creator>
<dc:creator>Visker, J.</dc:creator>
<dc:creator>Morris, S. A.</dc:creator>
<dc:creator>Kramann, R.</dc:creator>
<dc:creator>Leuschner, F.</dc:creator>
<dc:creator>Mann, D. L.</dc:creator>
<dc:creator>Drakos, S. G.</dc:creator>
<dc:creator>Lavine, K. J.</dc:creator>
<dc:date>2022-09-13</dc:date>
<dc:identifier>doi:10.1101/2022.09.11.507463</dc:identifier>
<dc:title><![CDATA[Defining Cardiac Recovery at Single Cell Resolution]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-09-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.10.17.512579v1?rss=1">
<title>
<![CDATA[
Targeting the Immune-Fibrosis Axis in Myocardial Infarction and Heart Failure 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.10.17.512579v1?rss=1"
</link>
<description><![CDATA[
Cardiac fibrosis is causally linked to heart failure pathogenesis and adverse clinical outcomes. However, the precise fibroblast populations that drive fibrosis in the human heart and the mechanisms that govern their emergence remain incompletely defined. Here, we performed Cellular Indexing of Transcriptomes and Epitomes by sequencing (CITE-seq) in 22 explanted human hearts from healthy donors, acute myocardial infarction (MI), and chronic ischemic and non-ischemic cardiomyopathy patients. We identified a fibroblast trajectory marked by fibroblast activator protein (FAP) and periostin (POSTN) expression that was independent of myofibroblasts, peaked early after MI, remained elevated in chronic heart failure, and displayed a transcriptional signature consistent with fibrotic activity. We assessed the applicability of cardiac fibrosis models and demonstrated that mouse MI, angiotensin II/phenylephrine infusion, and pressure overload models were superior compared to cultured human heart and dermal fibroblasts in recapitulating cardiac fibroblast diversity including pathogenic cell states. Ligand-receptor analysis and spatial transcriptomics predicted interactions between macrophages, T cells, and fibroblasts within spatially defined niches. CCR2+ monocyte and macrophage states were the dominant source of ligands targeting fibroblasts. Inhibition of IL-1{beta} signaling to cardiac fibroblasts was sufficient to suppress fibrosis, emergence, and maturation of FAP+POSTN+ fibroblasts. Herein, we identify a human fibroblast trajectory marked by FAP and POSTN expression that is associated with cardiac fibrosis and identify macrophage-fibroblast crosstalk mediated by IL-1{beta} signaling as a key regulator of pathologic fibroblast differentiation and fibrosis.
]]></description>
<dc:creator>Amrute, J. M.</dc:creator>
<dc:creator>Luo, X.</dc:creator>
<dc:creator>Penna, V.</dc:creator>
<dc:creator>Bredemeyer, A.</dc:creator>
<dc:creator>Yamawaki, T.</dc:creator>
<dc:creator>Heo, G. S.</dc:creator>
<dc:creator>Shi, S.</dc:creator>
<dc:creator>Koenig, A. L.</dc:creator>
<dc:creator>Yang, S.</dc:creator>
<dc:creator>Kadyrov, F. F.</dc:creator>
<dc:creator>Jones, C.</dc:creator>
<dc:creator>Kuppe, C.</dc:creator>
<dc:creator>Kopecky, B.</dc:creator>
<dc:creator>Hayat, S.</dc:creator>
<dc:creator>Ma, P.</dc:creator>
<dc:creator>Feng, G.</dc:creator>
<dc:creator>Terada, Y.</dc:creator>
<dc:creator>Fu, A.</dc:creator>
<dc:creator>Furtado, M.</dc:creator>
<dc:creator>Kreisel, D.</dc:creator>
<dc:creator>Stitziel, N. O.</dc:creator>
<dc:creator>Li, C.-M. K.</dc:creator>
<dc:creator>Kramann, R.</dc:creator>
<dc:creator>Liu, Y.</dc:creator>
<dc:creator>Ason, B.</dc:creator>
<dc:creator>Lavine, K.</dc:creator>
<dc:date>2022-10-21</dc:date>
<dc:identifier>doi:10.1101/2022.10.17.512579</dc:identifier>
<dc:title><![CDATA[Targeting the Immune-Fibrosis Axis in Myocardial Infarction and Heart Failure]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-10-21</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.12.08.411686v1?rss=1">
<title>
<![CDATA[
Spatial multi-omic map of human myocardial infarction 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.12.08.411686v1?rss=1"
</link>
<description><![CDATA[
Myocardial infarction is a leading cause of mortality. While advances in the acute treatment have been made, the late-stage mortality is still high, driven by an incomplete understanding of cardiac remodeling processes1,2. Here we used single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of different physiological zones and timepoints of human myocardial infarction and human control myocardium to generate an integrative high-resolution map of cardiac remodeling. This approach allowed us to increase spatial resolution of cell-type composition and provide spatially resolved insights into the cardiac transcriptome and epigenome with identification of distinct cellular zones of injury, repair and remodeling. We here identified and validated mechanisms of fibroblast to myofibroblast differentiation that drive cardiac fibrosis. Our study provides an integrative molecular map of human myocardial infarction and represents a reference to advance mechanistic and therapeutic studies of cardiac disease.
]]></description>
<dc:creator>Kuppe, C.</dc:creator>
<dc:creator>Ramirez Flores, R. O.</dc:creator>
<dc:creator>Li, Z.</dc:creator>
<dc:creator>Hannani, M. T.</dc:creator>
<dc:creator>Tanevski, J.</dc:creator>
<dc:creator>Halder, M.</dc:creator>
<dc:creator>Cheng, M.</dc:creator>
<dc:creator>Ziegler, S.</dc:creator>
<dc:creator>Zhang, X.</dc:creator>
<dc:creator>Priester, F.</dc:creator>
<dc:creator>Kaesler, N.</dc:creator>
<dc:creator>Xu, Y.</dc:creator>
<dc:creator>Hoogenboezem, R.</dc:creator>
<dc:creator>Bindels, E.</dc:creator>
<dc:creator>Schneider-Kramann, R.</dc:creator>
<dc:creator>Milting, H.</dc:creator>
<dc:creator>Gesteira Costa Filho, I.</dc:creator>
<dc:creator>Saez Rodriguez, J.</dc:creator>
<dc:creator>Kramann, R.</dc:creator>
<dc:date>2020-12-10</dc:date>
<dc:identifier>doi:10.1101/2020.12.08.411686</dc:identifier>
<dc:title><![CDATA[Spatial multi-omic map of human myocardial infarction]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-12-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.07.09.196519v1?rss=1">
<title>
<![CDATA[
Discriminating Mild from Critical COVID-19 by Innate and Adaptive Immune Single-cell Profiling of Bronchoalveolar Lavages 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.07.09.196519v1?rss=1"
</link>
<description><![CDATA[
How innate and adaptive lung immune responses to SARS-CoV-2 synchronize during COVID-19 pneumonitis and regulate disease severity is poorly established. To address this, we applied single-cell profiling to bronchoalveolar lavages from 44 patients with mild or critical COVID-19 versus non-COVID-19 pneumonia as control. Viral RNA-tracking delineated the infection phenotype to epithelial cells, but positioned mainly neutrophils at the forefront of viral clearance activity during COVID-19. In mild disease, neutrophils could execute their antiviral function in an immunologically  controlled fashion, regulated by fully-differentiated T-helper-17 (TH17)-cells, as well as T-helper-1 (TH1)-cells, CD8+ resident-memory (TRM) and partially-exhausted (TEX) T-cells with good effector functions. This was paralleled by  orderly phagocytic disposal of dead/stressed cells by fully-differentiated macrophages, otherwise characterized by anti-inflammatory and antigen-presenting characteristics, hence facilitating lung tissue repair. In critical disease, CD4+ TH1- and CD8+ TEX-cells were characterized by inflammation-associated stress and metabolic exhaustion, while CD4+ TH17- and CD8+ TRM-cells failed to differentiate. Consequently, T-cell effector function was largely impaired thereby possibly facilitating excessive neutrophil-based inflammation. This was accompanied by impaired monocyte-to-macrophage differentiation, with monocytes exhibiting an ATP-purinergic signalling-inflammasome footprint, thereby enabling COVID-19 associated fibrosis and worsening disease severity. Our work represents a major resource for understanding the lung-localised immunity and inflammation landscape during COVID-19.
]]></description>
<dc:creator>Els Wauters</dc:creator>
<dc:creator>Pierre Van Mol</dc:creator>
<dc:creator>Abhishek D. Garg</dc:creator>
<dc:creator>Sander Jansen</dc:creator>
<dc:creator>Yannick Van Herck</dc:creator>
<dc:creator>Lore Vanderbeke</dc:creator>
<dc:creator>Ayse Bassez</dc:creator>
<dc:creator>Bram Boeckx</dc:creator>
<dc:creator>Bert Malengier-Devlies</dc:creator>
<dc:creator>Anna Timmerman</dc:creator>
<dc:creator>Thomas Van Brussel</dc:creator>
<dc:creator>Tina Van Buyten</dc:creator>
<dc:creator>Rogier Schepers</dc:creator>
<dc:creator>Elisabeth Heylen</dc:creator>
<dc:creator>Dieter Dauwe</dc:creator>
<dc:creator>Christophe Dooms</dc:creator>
<dc:creator>Jan Gunst</dc:creator>
<dc:creator>Greet Hermans</dc:creator>
<dc:creator>Philippe Meersseman</dc:creator>
<dc:creator>Dries Testelmans</dc:creator>
<dc:creator>Jonas Yserbyt</dc:creator>
<dc:creator>Patrick Matthys</dc:creator>
<dc:creator>Sabine Tejpar</dc:creator>
<dc:creator>CONTAGIOUS collaborators</dc:creator>
<dc:creator>Johan Neyts</dc:creator>
<dc:creator>Joost Wauters</dc:creator>
<dc:creator>Junbin Qian</dc:creator>
<dc:creator>Diether Lambrechts</dc:creator>
<dc:date>2020-07-10</dc:date>
<dc:identifier>doi:10.1101/2020.07.09.196519</dc:identifier>
<dc:title><![CDATA[Discriminating Mild from Critical COVID-19 by Innate and Adaptive Immune Single-cell Profiling of Bronchoalveolar Lavages]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-07-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.07.30.551145v1?rss=1">
<title>
<![CDATA[
Expansion of profibrotic monocyte-derived alveolar macrophages in patients with persistent respiratory symptoms and radiographic abnormalities after COVID-19 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.07.30.551145v1?rss=1"
</link>
<description><![CDATA[
As many as 10-30% of the over 760 million survivors of COVID-19 develop persistent symptoms, of which respiratory symptoms are among the most common. To understand the cellular and molecular basis for respiratory PASC, we combined a machine learning based analysis of lung computed tomography (CT) with flow cytometry, single-cell RNA-sequencing analysis of bronchoalveolar lavage fluid and nasal curettage samples, and alveolar cytokine profiling in a cohort of thirty-five patients with respiratory symptoms and radiographic abnormalities more than 90 days after infection with COVID-19. CT images from patients with PASC revealed abnormalities involving 73% of the lung, which improved on subsequent imaging. Interstitial abnormalities suggestive of fibrosis on CT were associated with the increased numbers of neutrophils and presence of profibrotic monocyte-derived alveolar macrophages in BAL fluid, reflecting unresolved epithelial injury. Persistent infection with SARS-CoV-2 was identified in six patients and secondary bacterial or viral infections in two others. These findings suggest that despite its heterogenous clinical presentations, respiratory PASC with radiographic abnormalities results from a common pathobiology characterized by the ongoing recruitment of neutrophils and profibrotic monocyte-derived alveolar macrophages driving lung fibrosis with implications for diagnosis and therapy.
]]></description>
<dc:creator>Bailey, J. I.</dc:creator>
<dc:creator>Puritz, C. H.</dc:creator>
<dc:creator>Senkow, K. J.</dc:creator>
<dc:creator>Markov, N. S.</dc:creator>
<dc:creator>Diaz, E.</dc:creator>
<dc:creator>Jonasson, E.</dc:creator>
<dc:creator>Yu, Z.</dc:creator>
<dc:creator>Swaminathan, S.</dc:creator>
<dc:creator>Lu, Z.</dc:creator>
<dc:creator>Fenske, S.</dc:creator>
<dc:creator>Grant, R. A.</dc:creator>
<dc:creator>Abdala-Valencia, H.</dc:creator>
<dc:creator>Mylvaganam, R. J.</dc:creator>
<dc:creator>Miller, J.</dc:creator>
<dc:creator>Cumming, R. I.</dc:creator>
<dc:creator>Tighe, R. M.</dc:creator>
<dc:creator>Gowdy, K. M.</dc:creator>
<dc:creator>Kalhan, R.</dc:creator>
<dc:creator>Jain, M.</dc:creator>
<dc:creator>Bharat, A.</dc:creator>
<dc:creator>Kurihara, C.</dc:creator>
<dc:creator>San Jose Estepar, R.</dc:creator>
<dc:creator>San Jose Estepar, R.</dc:creator>
<dc:creator>Washko, G. R.</dc:creator>
<dc:creator>Shilatifard, A.</dc:creator>
<dc:creator>Sznajder, J. I.</dc:creator>
<dc:creator>Ridge, K. M.</dc:creator>
<dc:creator>Budinger, G. S.</dc:creator>
<dc:creator>Braun, R.</dc:creator>
<dc:creator>Misharin, A. V.</dc:creator>
<dc:creator>Sala, M. A.</dc:creator>
<dc:date>2023-07-31</dc:date>
<dc:identifier>doi:10.1101/2023.07.30.551145</dc:identifier>
<dc:title><![CDATA[Expansion of profibrotic monocyte-derived alveolar macrophages in patients with persistent respiratory symptoms and radiographic abnormalities after COVID-19]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-07-31</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/759902v1?rss=1">
<title>
<![CDATA[
Single Cell RNA-seq reveals ectopic and aberrant lung resident cell populations in Idiopathic Pulmonary Fibrosis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/759902v1?rss=1"
</link>
<description><![CDATA[
We provide a single cell atlas of Idiopathic Pulmonary Fibrosis (IPF), a fatal interstitial lung disease, focusing on resident lung cell populations. By profiling 312,928 cells from 32 IPF, 29 healthy control and 18 chronic obstructive pulmonary disease (COPD) lungs, we demonstrate that IPF is characterized by changes in discrete subpopulations of cells in the three major parenchymal compartments: the epithelium, endothelium and stroma. Among epithelial cells, we identify a novel population of IPF enriched aberrant basaloid cells that co-express basal epithelial markers, mesenchymal markers, senescence markers, developmental transcription factors and are located at the edge of myofibroblast foci in the IPF lung. Among vascular endothelial cells in the in IPF lung parenchyma we identify an expanded cell population transcriptomically identical to vascular endothelial cells normally restricted to the bronchial circulation. We confirm the presence of both populations by immunohistochemistry and independent datasets. Among stromal cells we identify fibroblasts and myofibroblasts in both control and IPF lungs and leverage manifold-based algorithms diffusion maps and diffusion pseudotime to infer the origins of the activated IPF myofibroblast. Our work provides a comprehensive catalogue of the aberrant cellular transcriptional programs in IPF, demonstrates a new framework for analyzing complex disease with scRNAseq, and provides the largest lung disease single-cell atlas to date.
]]></description>
<dc:creator>Adams, T. S.</dc:creator>
<dc:creator>Schupp, J. C.</dc:creator>
<dc:creator>Poli, S.</dc:creator>
<dc:creator>Ayaub, E. A.</dc:creator>
<dc:creator>Neumark, N.</dc:creator>
<dc:creator>Ahangari, F.</dc:creator>
<dc:creator>Chu, S. G.</dc:creator>
<dc:creator>Raby, B.</dc:creator>
<dc:creator>DeIuliis, G.</dc:creator>
<dc:creator>Januszyk, M.</dc:creator>
<dc:creator>Duan, Q.</dc:creator>
<dc:creator>Arnett, H. A.</dc:creator>
<dc:creator>Siddiqui, A.</dc:creator>
<dc:creator>Washko, G. R.</dc:creator>
<dc:creator>Homer, R.</dc:creator>
<dc:creator>Yan, X.</dc:creator>
<dc:creator>Rosas, I. O.</dc:creator>
<dc:creator>Kaminski, N.</dc:creator>
<dc:date>2019-09-06</dc:date>
<dc:identifier>doi:10.1101/759902</dc:identifier>
<dc:title><![CDATA[Single Cell RNA-seq reveals ectopic and aberrant lung resident cell populations in Idiopathic Pulmonary Fibrosis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-09-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.03.16.484543v1?rss=1">
<title>
<![CDATA[
A Unique Cellular Organization of Human Distal Airways and Its Disarray in Chronic Obstructive Pulmonary Disease 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.03.16.484543v1?rss=1"
</link>
<description><![CDATA[
In the human lung, terminal bronchioles (TBs), the most distal conducting airways, open to respiratory bronchioles (RBs) that lead to the alveolar region where gas exchange takes place. This transition occurs in pulmonary lobules, lung tissue units supplied by pre-TBs, which give rise to TBs. Accumulating evidence suggests that remodeling and loss of pre-TBs and TBs underlies progressive airflow limitation in chronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide. Understanding the nature of these changes at the single-cell level has so far been limited by poor accessibility of pre-TBs and TBs. Here, we introduce a novel method of region-precise airway dissection, which enables capture of the entire anatomical continuum of peripheral airways, from pre-TBs to RBs, and the associated alveolar region within the lobule. This approach allowed us to identify terminal airway-enriched secretory cells (TASCs), a unique epithelial cell population of distal airways expressing secretoglobin 3A2 (SCGB3A2) and/or surfactant protein B (SFTPB). TASCs were enriched in TBs, particularly, in areas of TB-RB transition and exhibited an intermediate, broncho-alveolar molecular pattern. TASC frequency was markedly decreased in pre-TBs and TBs of COPD patients compared to those in non-diseased lungs, accompanied by changes in cellular composition of vascular and immune microenvironments. In vitro regeneration assays identified basal cells (BCs) of pre-TBs and TBs as a cellular origin of TASCs in the human lung. Generation of TASCs by these region-specific progenitors was suppressed by IFN-{gamma} signaling that was augmented in distal airways of COPD patients. Thus, altered maintenance of region-specific cellular organization of pre-TBs and TBs represents a key component of distal airway pathology in COPD.
]]></description>
<dc:creator>Rustam, S.</dc:creator>
<dc:creator>Hu, Y.</dc:creator>
<dc:creator>Mahjour, S. B.</dc:creator>
<dc:creator>Rendeiro, A. F.</dc:creator>
<dc:creator>Ravichandran, H.</dc:creator>
<dc:creator>Randell, S. H.</dc:creator>
<dc:creator>Richmond, B.</dc:creator>
<dc:creator>Polosukhin, V.</dc:creator>
<dc:creator>Kropski, J. A.</dc:creator>
<dc:creator>Blackwell, T. S.</dc:creator>
<dc:creator>d'Ovidio, F.</dc:creator>
<dc:creator>Martinez, F. J.</dc:creator>
<dc:creator>Elemento, O.</dc:creator>
<dc:creator>Shaykhiev, R.</dc:creator>
<dc:date>2022-03-16</dc:date>
<dc:identifier>doi:10.1101/2022.03.16.484543</dc:identifier>
<dc:title><![CDATA[A Unique Cellular Organization of Human Distal Airways and Its Disarray in Chronic Obstructive Pulmonary Disease]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-03-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.05.18.491687v1?rss=1">
<title>
<![CDATA[
Guided construction of single cell reference for human and mouse lung 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.05.18.491687v1?rss=1"
</link>
<description><![CDATA[
Accurate cell type identification is a key and rate-limiting step in single cell data analysis. Single cell references with comprehensive cell types, reproducible and functional validated cell identities, and common nomenclatures are much needed by the research community to optimize automated cell type annotation and facilitate data integration, sharing, and collaboration. In the present study, we developed a novel computational pipeline to utilize the LungMAP CellCards as a dictionary to consolidate single-cell transcriptomic datasets of 104 human lungs and 17 mouse lung samples and constructed "LungMAP CellRef" and "LungMAP CellRef Seed" for both normal human and mouse lungs. "CellRef Seed" has an equivalent prediction power and produces consistent cell annotation as does "CellRef" but improves computational efficiency and simplifies its utilization for fast automated cell type annotation and online visualization. This atlas set incorporates 48 human and 40 mouse well-defined lung cell types catalogued from diverse anatomic locations and developmental time points. Using independent datasets, we demonstrated the utility of our CellRefs for automated cell type annotation analysis of both normal and disease lungs. User-friendly web interfaces were developed to support easy access and maximal utilization of the LungMAP CellRefs. LungMAP CellRefs are freely available to the pulmonary research community through fast interactive web interfaces to facilitate hypothesis generation, research discovery, and identification of cell type alterations in disease conditions.
]]></description>
<dc:creator>Guo, M.</dc:creator>
<dc:creator>Morley, M. P.</dc:creator>
<dc:creator>Wu, Y.</dc:creator>
<dc:creator>Du, Y.</dc:creator>
<dc:creator>Zhao, S.</dc:creator>
<dc:creator>Wagner, A.</dc:creator>
<dc:creator>Kouril, M.</dc:creator>
<dc:creator>Jin, K.</dc:creator>
<dc:creator>Gaddis, N.</dc:creator>
<dc:creator>Kitzmiller, J. A.</dc:creator>
<dc:creator>Stewart, K.</dc:creator>
<dc:creator>Basil, M. C.</dc:creator>
<dc:creator>Lin, S. M.</dc:creator>
<dc:creator>Ying, Y.</dc:creator>
<dc:creator>Babu, A.</dc:creator>
<dc:creator>Wikenheiser-Brokamp, K. A.</dc:creator>
<dc:creator>Mun, K. S.</dc:creator>
<dc:creator>Naren, A. P.</dc:creator>
<dc:creator>Lin, S.</dc:creator>
<dc:creator>Clair, G.</dc:creator>
<dc:creator>Adkins, J. N.</dc:creator>
<dc:creator>Pryhuber, G. S.</dc:creator>
<dc:creator>Misra, R. S.</dc:creator>
<dc:creator>Aronow, B. J.</dc:creator>
<dc:creator>Tickle, T. L.</dc:creator>
<dc:creator>Salomonis, N.</dc:creator>
<dc:creator>Sun, X.</dc:creator>
<dc:creator>Morrisey, E. E.</dc:creator>
<dc:creator>Whitsett, J. A.</dc:creator>
<dc:creator>Xu, Y.</dc:creator>
<dc:date>2022-05-20</dc:date>
<dc:identifier>doi:10.1101/2022.05.18.491687</dc:identifier>
<dc:title><![CDATA[Guided construction of single cell reference for human and mouse lung]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-05-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.07.31.454200v1?rss=1">
<title>
<![CDATA[
Gene regulatory networks controlling temporal patterning, neurogenesis and cell fate specification in the mammalian retina. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.07.31.454200v1?rss=1"
</link>
<description><![CDATA[
Gene regulatory networks (GRNs), consisting of transcription factors and their target cis- regulatory sequences, control neurogenesis and cell fate specification in the developing central nervous system, but their organization is poorly characterized. In this study, we performed integrated single-cell RNA- and scATAC-seq analysis in both mouse and human retina to profile dynamic changes in gene expression, chromatin accessibility and transcription factor footprinting during retinal neurogenesis. We identified multiple interconnected, evolutionarily-conserved GRNs consisting of cell type-specific transcription factors that both activate expression of genes within their own network and often inhibit expression of genes in other networks. These GRNs control state transitions within primary retinal progenitors that underlie temporal patterning, regulate the transition from primary to neurogenic progenitors, and drive specification of each major retinal cell type. We confirmed the prediction of this analysis that the NFI transcription factors Nfia, Nfib, and Nfix selectively activate expression of genes that promote late-stage temporal identity in primary retinal progenitors. We also used GRNs to identify additional transcription factors that promote (Insm1/2) and inhibit (Tbx3, Tcf7l1/2) rod photoreceptor specification in postnatal retina. This study provides an inventory of cis- and trans-acting factors that control retinal development, identifies transcription factors that control the temporal identity of retinal progenitors and cell fate specification, and will potentially guide cell-based therapies aimed at replacing retinal neurons lost due to disease.
]]></description>
<dc:creator>Lyu, P.</dc:creator>
<dc:creator>Hoang, T.</dc:creator>
<dc:creator>Santiago, C. P.</dc:creator>
<dc:creator>Thomas, E.</dc:creator>
<dc:creator>Timms, A.</dc:creator>
<dc:creator>Appel, H.</dc:creator>
<dc:creator>Gimmen, M.</dc:creator>
<dc:creator>Le, N.</dc:creator>
<dc:creator>Jiang, L.</dc:creator>
<dc:creator>Kim, D. W.</dc:creator>
<dc:creator>Chen, S.</dc:creator>
<dc:creator>Espinoza, D.</dc:creator>
<dc:creator>Tegler, A.</dc:creator>
<dc:creator>Weir, K.</dc:creator>
<dc:creator>Clark, B.</dc:creator>
<dc:creator>Cherry, T. M.</dc:creator>
<dc:creator>Qian, J.</dc:creator>
<dc:creator>Blackshaw, S.</dc:creator>
<dc:date>2021-08-02</dc:date>
<dc:identifier>doi:10.1101/2021.07.31.454200</dc:identifier>
<dc:title><![CDATA[Gene regulatory networks controlling temporal patterning, neurogenesis and cell fate specification in the mammalian retina.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-08-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.02.11.943779v1?rss=1">
<title>
<![CDATA[
CELL ATLAS OF THE HUMAN FOVEA AND PERIPHERAL RETINA 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.02.11.943779v1?rss=1"
</link>
<description><![CDATA[
Most irreversible blindness results from retinal disease. To advance our understanding of the etiology of blinding diseases, we used single-cell RNA-sequencing (scRNA-seq) to analyze the transcriptomes of [~]85,000 cells from the fovea and peripheral retina of seven adult human donors. Utilizing computational methods, we identified 58 cell types within 6 classes: photoreceptor, horizontal, bipolar, amacrine, retinal ganglion and non-neuronal cells. Nearly all types are shared between the two retinal regions, but there are notable differences in gene expression and proportions between foveal and peripheral cohorts of shared types. We then used the human retinal atlas to map expression of 636 genes implicated as causes of or risk factors for blinding diseases. Many are expressed in striking cell class-, type-, or region-specific patterns. Finally, we compared gene expression signatures of cell types between human and the cynomolgus macaque monkey, Macaca fascicularis. We show that over 90% of human types correspond transcriptomically to those previously identified in macaque, and that expression of disease-related genes is largely conserved between the two species. These results validate the use of the macaque for modeling blinding disease, and provide a foundation for investigating molecular mechanisms underlying visual processing.
]]></description>
<dc:creator>Yan, W.</dc:creator>
<dc:creator>Peng, Y.-R.</dc:creator>
<dc:creator>van Zyl, T.</dc:creator>
<dc:creator>Regev, A.</dc:creator>
<dc:creator>Shekhar, K.</dc:creator>
<dc:creator>Juric, D.</dc:creator>
<dc:creator>Sanes, J.</dc:creator>
<dc:date>2020-02-12</dc:date>
<dc:identifier>doi:10.1101/2020.02.11.943779</dc:identifier>
<dc:title><![CDATA[CELL ATLAS OF THE HUMAN FOVEA AND PERIPHERAL RETINA]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-02-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.09.24.460132v1?rss=1">
<title>
<![CDATA[
Maturation of human intestinal epithelium from pluripotency in vitro 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.09.24.460132v1?rss=1"
</link>
<description><![CDATA[
Methods to generate human intestinal tissue from pluripotent stem cells (PSCs) open new inroads into modeling intestine development and disease. However, current protocols require organoid transplantation into an immunocompromised mouse to achieve matured and differentiated epithelial cell states. Inspired by developmental reconstructions from primary tissues, we establish a regimen of inductive cues that enable stem cell maturation and epithelial differentiation entirely in vitro. We show that the niche factor Neuregulin1 (NRG1) promotes morphological change from proliferative epithelial cysts to matured epithelial tissue in three-dimensional cultures. Single-cell transcriptome analyses reveal differentiated epithelial cell populations, including diverse secretory and absorptive lineages. Comparison to multi-organ developmental and adult intestinal cell atlases confirm the specificity and maturation state of cell populations. Altogether, this work opens a new direction to use in vitro matured epithelium from human PSCs to study human intestinal epithelium development, disease, and evolution in controlled culture environments.
]]></description>
<dc:creator>Kilik, U.</dc:creator>
<dc:creator>Yu, Q.</dc:creator>
<dc:creator>Holtackers, R.</dc:creator>
<dc:creator>Seimiya, M.</dc:creator>
<dc:creator>Xavier da Silveira dos Santos, A.</dc:creator>
<dc:creator>Treutlein, B.</dc:creator>
<dc:creator>Spence, J. R.</dc:creator>
<dc:creator>Camp, G.</dc:creator>
<dc:date>2021-09-24</dc:date>
<dc:identifier>doi:10.1101/2021.09.24.460132</dc:identifier>
<dc:title><![CDATA[Maturation of human intestinal epithelium from pluripotency in vitro]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-09-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.06.27.601103v1?rss=1">
<title>
<![CDATA[
Integrative Transcriptomics Reveals Layer 1 Astrocytes Altered in Schizophrenia 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.06.27.601103v1?rss=1"
</link>
<description><![CDATA[
Schizophrenia is one of the most prevalent psychiatric disorders with unclear pathophysiology despite a century-long history of intense research. Schizophrenia affects multiple networks across different brain regions. The anterior cingulate cortex (ACC) is the region that connects the limbic system to cognitive areas such as the prefrontal cortex and represents a pivotal region for the etiology of schizophrenia; however, the molecular pathology, considering its cellular and anatomical complexity, is not well understood. Here, we performed an integrative analysis of spatial and single-nucleus transcriptomics of the postmortem ACC of people with schizophrenia, together with a thorough histological analysis. The data revealed major transcriptomics signatures altered in schizophrenia, pointing at the dysregulation of glial cells, primarily in astrocytes. We further discovered a decrease in the cellular density and abundance of processes of interlaminar astrocytes, a subpopulation of astrocytes specific to primates that localize in the layer 1 and influence the superficial cortical microenvironment across layer 1 and layers 2/3 of the cortex. Our study suggests that aberrant changes in interlaminar astrocytes could explain the cell-to-cell circuit alterations found in schizophrenia and represent novel therapeutic targets to ameliorate schizophrenia-associated dysfunction.
]]></description>
<dc:creator>Leon, J.</dc:creator>
<dc:creator>Yoshinaga, S.</dc:creator>
<dc:creator>Hino, M.</dc:creator>
<dc:creator>Nagaoka, A.</dc:creator>
<dc:creator>Ando, Y.</dc:creator>
<dc:creator>Moody, J.</dc:creator>
<dc:creator>Kojima, M.</dc:creator>
<dc:creator>Kitazawa, A.</dc:creator>
<dc:creator>Hayashi, K.</dc:creator>
<dc:creator>Nakajima, K.</dc:creator>
<dc:creator>Condello, C.</dc:creator>
<dc:creator>Carninci, P.</dc:creator>
<dc:creator>Kunii, Y.</dc:creator>
<dc:creator>Hon, C.-C.</dc:creator>
<dc:creator>Shin, J. W.</dc:creator>
<dc:creator>Kubo, K.-i.</dc:creator>
<dc:date>2024-06-28</dc:date>
<dc:identifier>doi:10.1101/2024.06.27.601103</dc:identifier>
<dc:title><![CDATA[Integrative Transcriptomics Reveals Layer 1 Astrocytes Altered in Schizophrenia]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-06-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.07.24.219147v1?rss=1">
<title>
<![CDATA[
An organoid and multi-organ developmental cell atlas reveals multilineage fate specification in the human intestine 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.07.24.219147v1?rss=1"
</link>
<description><![CDATA[
Human intestinal organoids (HIOs) generated from pluripotent stem cells provide extraordinary opportunities to explore development and disease. Here, we generate a single-cell transcriptome reference atlas from HIOs and from multiple developing human organs to quantify the specificity of HIO cell fate acquisition, and to explore alternative fates. We identify epithelium-mesenchyme interactions, transcriptional regulators involved in cell fate specification, and stem cell maturation features in the primary tissue that are recapitulated in HIOs. We use an HIO time course to reconstruct the molecular dynamics of intestinal stem cell emergence, as well as the specification of multiple mesenchyme subtypes. We find that the intestinal master regulator CDX2 correlates with distinct phases of epithelial and mesenchymal development, and CDX2 deletion perturbs the differentiation of both intestinal epithelium and mesenchyme. Collectively our data provides a comprehensive and quantitative assessment of HIO development, and illuminates the molecular machinery underlying endodermal and mesodermal cell fate specification.
]]></description>
<dc:creator>Yu, Q.</dc:creator>
<dc:creator>Kilik, U.</dc:creator>
<dc:creator>Holloway, E. M.</dc:creator>
<dc:creator>Tsai, Y.-H.</dc:creator>
<dc:creator>Wu, A.</dc:creator>
<dc:creator>Wu, J. H.</dc:creator>
<dc:creator>Czerwinski, M.</dc:creator>
<dc:creator>Childs, C.</dc:creator>
<dc:creator>He, Z.</dc:creator>
<dc:creator>Glass, I. A.</dc:creator>
<dc:creator>Higgins, P. D. R.</dc:creator>
<dc:creator>Treutlein, B.</dc:creator>
<dc:creator>Spence, J. R.</dc:creator>
<dc:creator>Camp, J. G.</dc:creator>
<dc:date>2020-07-25</dc:date>
<dc:identifier>doi:10.1101/2020.07.24.219147</dc:identifier>
<dc:title><![CDATA[An organoid and multi-organ developmental cell atlas reveals multilineage fate specification in the human intestine]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-07-25</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.11.21.624698v1?rss=1">
<title>
<![CDATA[
Multi-modal refinement of the human heart atlas during the first gestational trimester 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.11.21.624698v1?rss=1"
</link>
<description><![CDATA[
1.Forty first-trimester human hearts were studied to lay groundwork for further studies of principles underlying congenital heart defects. We first sampled 49,227 cardiac nuclei from three fetuses at 8.6, 9.0, and 10.7 post-conceptional weeks (pcw) for single-nucleus RNA sequencing, enabling distinction of six classes comprising 21 cell types. Improved resolution led to identification of novel cardiomyocytes and minority autonomic and lymphatic endothelial transcriptomes, among others. After integration with 5-7 pcw heart single-cell RNAseq, we identified a human cardiomyofibroblast progenitor preceding diversification of cardiomyocyte and stromal lineages. Analysis of six Visium sections from two additional hearts was aided by deconvolution, and key spatial markers validated on sectioned and whole hearts in two- and three-dimensional space and over time. Altogether, anatomical-positional features including innervation, conduction and subdomains of the atrioventricular septum translate latent molecular identity into specialized cardiac functions. This atlas adds unprecedented spatial and temporal resolution to the characterization of human-specific aspects of early heart formation.
]]></description>
<dc:creator>De Bono, C.</dc:creator>
<dc:creator>Xu, Y.</dc:creator>
<dc:creator>Kausar, S.</dc:creator>
<dc:creator>Herbane, M.</dc:creator>
<dc:creator>Humbert, C.</dc:creator>
<dc:creator>Rafatov, S.</dc:creator>
<dc:creator>Missirian, C.</dc:creator>
<dc:creator>Moreno, M.</dc:creator>
<dc:creator>Shi, W.</dc:creator>
<dc:creator>Gitton, Y. I.</dc:creator>
<dc:creator>Lombardini, A.</dc:creator>
<dc:creator>Vanzetta, I.</dc:creator>
<dc:creator>Mazaud-Guittot, S.</dc:creator>
<dc:creator>Chedotal, A.</dc:creator>
<dc:creator>Baudot, A.</dc:creator>
<dc:creator>Zaffran, S.</dc:creator>
<dc:creator>Etchevers, H. C.</dc:creator>
<dc:date>2024-11-21</dc:date>
<dc:identifier>doi:10.1101/2024.11.21.624698</dc:identifier>
<dc:title><![CDATA[Multi-modal refinement of the human heart atlas during the first gestational trimester]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-11-21</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.01.27.635138v1?rss=1">
<title>
<![CDATA[
Single-cell transcriptomics reveals the impact of sex and age in the healthy human liver 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.01.27.635138v1?rss=1"
</link>
<description><![CDATA[
Background & AimsThe liver is a vital organ composed of parenchymal, nonparenchymal, and immune cell populations. Single-cell sequencing approaches now provide the opportunity to understand how sex and age influence gene expression and cellular function across cell types within the liver.

MethodsWe analyzed the cellular composition and interactions for the human liver through single-nucleus RNA sequencing (snRNA-seq), incorporating insights from 37 healthy liver samples. The dataset contains cells from female and male donors spanning more than seven decades of life, and analysis was performed to evaluate the impact of sex and age on differential gene expression, pathway enrichment, and predicted ligand-receptor and protein-protein interactions.

ResultsExcluding the X and Y chromosomes, we identified 374 genes uniquely enriched in cells of the female liver and 520 genes enriched in cells of the male liver. Differential expression analysis defined unique circuitries enriched within each cell type between females and males and their impact on cell-cell communication and response to external signals, including enrichment of cholesterol/lipid metabolism, transforming growth factor beta (TGF-beta) signaling, and fibronectin (FN1) production in female cells and bone morphogenic protein (BMP) signaling in male cells. With increased age, we observe a greater diversity in gene expression, including enrichment of genes that regulate neuregulin (NGR) signaling at older ages, while genes regulating insulin growth factor (IGF) signaling are enriched at younger ages. Senescence signatures were also identified for each cell type within the liver.

ConclusionsThese results define the activities of healthy cell types within the liver across sex and age and provide a foundation for studies to examine how ancestry, geography, and disease states influence liver function within these contexts.

Impact and ImplicationsOur study analyzes 37 human liver samples at the single-cell level to understand how sex and age influence gene expression, cell interactions, and response to signals across liver cell types and sub-types. These findings are of particular significance for researchers who need to understand how sex and age may influence the response of individual cell types to injury or treatment of injury. This dataset will also provide a healthy reference for future studies to understand how ancestry, geography, and disease states shape liver biology across age and sex.
]]></description>
<dc:creator>Rahman, R.</dc:creator>
<dc:creator>Epstein, E. T.</dc:creator>
<dc:creator>Murphy, S.</dc:creator>
<dc:creator>Amir-Zilberstein, L.</dc:creator>
<dc:creator>McCabe, C.</dc:creator>
<dc:creator>Delorey, T.</dc:creator>
<dc:creator>Koene, H.</dc:creator>
<dc:creator>Fernandes, L.</dc:creator>
<dc:creator>Tanabe, K. K.</dc:creator>
<dc:creator>Qadan, M.</dc:creator>
<dc:creator>Ferrone, C.</dc:creator>
<dc:creator>Berger, D. L.</dc:creator>
<dc:creator>Shih, A.</dc:creator>
<dc:creator>Deguine, J.</dc:creator>
<dc:creator>Mullen, A.</dc:creator>
<dc:date>2025-01-30</dc:date>
<dc:identifier>doi:10.1101/2025.01.27.635138</dc:identifier>
<dc:title><![CDATA[Single-cell transcriptomics reveals the impact of sex and age in the healthy human liver]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-01-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.03.06.637075v1?rss=1">
<title>
<![CDATA[
Systematic evaluation of single-cell multimodal data integration for comprehensive human reference atlas. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.03.06.637075v1?rss=1"
</link>
<description><![CDATA[
The integration of multimodal single-cell data enables comprehensive organ reference atlases, yet its impact remains largely unexplored, particularly in complex tissues. We generated a benchmarking dataset for the renal cortex by integrating 3 and 5 scRNA-seq with joint snRNA-seq and snATAC-seq, profiling 119,744 high-quality nuclei/cells from 19 donors. To align cell identities and enable consistent comparisons, we developed the interpretable machine learning tool scOMM (single-cell Omics Multimodal Mapping) and systematically assessed integration strategies. "Horizontal" integration of scRNA and snRNA-seq improved cell-type identification, while "vertical" integration of snRNA-seq and snATAC-seq had an additive effect, enhancing resolution in homogeneous populations and difficult-to-identify states. Global integration was especially effective in identifying adaptive states and rare cell types, including WFDC2-expressing Thick Ascending Limb and Norn cells, previously undetected in kidney atlases. Our work establishes a robust framework for multimodal reference atlas generation, advancing single-cell analysis and extending its applicability to diverse tissues.
]]></description>
<dc:creator>Acera-Mateos, M.</dc:creator>
<dc:creator>Adiconis, X.</dc:creator>
<dc:creator>Li, J.-K.</dc:creator>
<dc:creator>Marchese, D.</dc:creator>
<dc:creator>Caratu, G.</dc:creator>
<dc:creator>Hon, C.-C.</dc:creator>
<dc:creator>Tiwari, P.</dc:creator>
<dc:creator>Kojima, M.</dc:creator>
<dc:creator>Vieth, B.</dc:creator>
<dc:creator>Murphy, M. A.</dc:creator>
<dc:creator>Simmons, S. K.</dc:creator>
<dc:creator>Lefevre, T.</dc:creator>
<dc:creator>Claes, I.</dc:creator>
<dc:creator>O'Connor, C. L.</dc:creator>
<dc:creator>Menon, R.</dc:creator>
<dc:creator>Otto, E. A.</dc:creator>
<dc:creator>Ando, Y.</dc:creator>
<dc:creator>Vandereyken, K.</dc:creator>
<dc:creator>Kretzler, M.</dc:creator>
<dc:creator>Bitzer, M.</dc:creator>
<dc:creator>Fraenkel, E.</dc:creator>
<dc:creator>Voet, T.</dc:creator>
<dc:creator>Enard, W.</dc:creator>
<dc:creator>Carnici, P.</dc:creator>
<dc:creator>Heyn, H.</dc:creator>
<dc:creator>Levin, J. Z.</dc:creator>
<dc:creator>Mereu, E.</dc:creator>
<dc:date>2025-03-06</dc:date>
<dc:identifier>doi:10.1101/2025.03.06.637075</dc:identifier>
<dc:title><![CDATA[Systematic evaluation of single-cell multimodal data integration for comprehensive human reference atlas.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-03-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.09.15.557967v1?rss=1">
<title>
<![CDATA[
Human HYPOMAP: A comprehensive spatio-cellular map of the human hypothalamus 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.09.15.557967v1?rss=1"
</link>
<description><![CDATA[
The hypothalamus is a brain region that plays a key role in coordinating fundamental biological functions. However, our understanding of the underlying cellular components and circuitry, have, until recently, emerged primarily from rodent studies. Here, we combine a single-nucleus sequencing database of 433,369 human hypothalamic cells, with spatial transcriptomics, to present a comprehensive spatio-cellular transcriptional map of the human hypothalamus, the  HYPOMAP. Analysing hypothalamic leptin melanocortin pathway neuronal populations that play a role in appetite control, we identify spatially distinct populations of arcuate nucleus POMC and AGRP neurons, and their receptors MC3R and MC4R. Next, we map the cells expressing incretin receptors, targets of the new generation of anti-obesity medications, and uncover transcriptionally distinct GLP1R and GIPR-expressing cellular populations. Finally, out of the 458 hypothalamic cell types in HYPOMAP, we find 182 neuronal clusters are significantly enriched in expression of BMI GWAS genes. This enrichment is driven by 375  effector genes, with rare deleterious variants in 6 of these; MC4R, PCSK1, POMC, CALCR, BSN and CORO1A, the last of which has previously not been linked to obesity; being significantly associated with changes in BMI at the population level. Thus, the HYPOMAP provides a detailed atlas of the human hypothalamus in a spatial context, and serves as an important resource to identify novel druggable targets for treating a wide range of conditions, including reproductive, circadian, and metabolic disorders.
]]></description>
<dc:creator>Tadross, J. A.</dc:creator>
<dc:creator>Steuernagal, L.</dc:creator>
<dc:creator>Dowsett, G.</dc:creator>
<dc:creator>Kentistou, K. A.</dc:creator>
<dc:creator>Lundh, S.</dc:creator>
<dc:creator>Porniece, M.</dc:creator>
<dc:creator>Klemm, P.</dc:creator>
<dc:creator>Rainbow, K.</dc:creator>
<dc:creator>Hvid, H.</dc:creator>
<dc:creator>Kania, K.</dc:creator>
<dc:creator>Polex-Wolf, J.</dc:creator>
<dc:creator>Knudsen, L. B.</dc:creator>
<dc:creator>Pyke, C.</dc:creator>
<dc:creator>Perry, J.</dc:creator>
<dc:creator>Lam, B. Y.</dc:creator>
<dc:creator>Bruening, J. C.</dc:creator>
<dc:creator>Yeo, G. S. H.</dc:creator>
<dc:date>2023-09-15</dc:date>
<dc:identifier>doi:10.1101/2023.09.15.557967</dc:identifier>
<dc:title><![CDATA[Human HYPOMAP: A comprehensive spatio-cellular map of the human hypothalamus]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-09-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.04.16.649149v1?rss=1">
<title>
<![CDATA[
A Single-Cell Atlas Of Human Pediatric Liver Reveals Age-Related Hepatic Gene Signatures 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.04.16.649149v1?rss=1"
</link>
<description><![CDATA[
Background & AimsThe liver plays a critical role in metabolism and immune function, yet the contributions of its heterogeneous cell types to these processes remain unclear. While most liver studies focus on adults, pediatric liver diseases often present differently, underscoring the need for age-specific research.

Approach & ResultsTo better understand cellular drivers of childhood liver diseases, we generated single-cell RNA-seq (scRNA-seq) maps of the normal pediatric liver and used this map to examine disease-related populations in biopsies from pediatric patients with Intestinal Failure-Associated Liver Disease (IFALD). The normal pediatric liver map consists of 42,660 cells from 9 donors aged 2-17 years. Compared to normal adult liver (26,372 cells; 7 donors, age 26-69) pediatric livers exhibited differences in myeloid populations. Specifically, pediatric Kupffer-like cells (MARCO+C1QA+VSIG4+) exhibited higher expression of immune activation genes, including CCL4, CCL3 and IL1B. In vitro stimulation confirmed more IL1-{beta} secreting myeloid cells in pediatric versus adult livers, supporting these findings. Using the pediatric atlas as a reference, we analyzed three IFALD biopsies (11,969 cells; 3 donors, ages 4 months-9 years) and identified increased expression of fibrosis-associated genes (e.g., LY96) in Kupffer-like cells. Additionally, mesenchymal cells in IFALD showed fibrotic gene modules resembling adult liver cells more than healthy pediatric cells. These signatures, undetectable when comparing IFALD to adult liver alone, highlighting the value of a pediatric map.

ConclusionsTaken together, our healthy pediatric liver atlas reveals distinct age-related signatures and provides background against which to interpret pediatric liver disease data.



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]]></description>
<dc:creator>Edgar, R. D.</dc:creator>
<dc:creator>Nakib, D.</dc:creator>
<dc:creator>Camat, D.</dc:creator>
<dc:creator>Chung, S.</dc:creator>
<dc:creator>Lumanto, P.</dc:creator>
<dc:creator>Atif, J.</dc:creator>
<dc:creator>Perciani, C. T.</dc:creator>
<dc:creator>Ma, X. Z.</dc:creator>
<dc:creator>Thoeni, C.</dc:creator>
<dc:creator>Selvakumaran, N.</dc:creator>
<dc:creator>Manuel, J.</dc:creator>
<dc:creator>Sayed, B.</dc:creator>
<dc:creator>Huysentruyt, K.</dc:creator>
<dc:creator>Ricciuto, A.</dc:creator>
<dc:creator>McGilvray, I.</dc:creator>
<dc:creator>Avitzur, Y.</dc:creator>
<dc:creator>Bader, G. D.</dc:creator>
<dc:creator>MacParland, S. A.</dc:creator>
<dc:date>2025-04-20</dc:date>
<dc:identifier>doi:10.1101/2025.04.16.649149</dc:identifier>
<dc:title><![CDATA[A Single-Cell Atlas Of Human Pediatric Liver Reveals Age-Related Hepatic Gene Signatures]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-04-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.10.15.618501v1?rss=1">
<title>
<![CDATA[
Multimodal learning of transcriptomes and text enables interactive single-cell RNA-seq data exploration with natural-language chats 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.10.15.618501v1?rss=1"
</link>
<description><![CDATA[
Single-cell RNA-seq characterizes biological samples at unprecedented scale and detail, but data interpretation remains challenging. Here we introduce CellWhisperer, a multimodal machine learning model and software that connects transcriptomes and text for interactive single-cell RNA-seq data analysis. CellWhisperer enables the chat-based interrogation of transcriptome data in English language. To train our model, we created an AI-curated dataset with over a million pairs of RNA-seq profiles and matched textual annotations across a broad range of human biology, and we established a multimodal embedding of matched transcriptomes and text using contrastive learning. Our model enables free-text search and annotation of transcriptome datasets by cell types, states, and other properties in a zero-shot manner and without the need for reference datasets. Moreover, Cell-Whisperer answers questions about cells and genes in natural-language chats, using a biologically fluent large language model that we fine-tuned to analyze bulk and single-cell transcriptome data across various biological applications. We integrated CellWhisperer with the widely used CELLxGENE browser, allowing users to in-teractively explore RNA-seq data through an integrated graphical and chat interface. Our method demonstrates a new way of working with transcriptome data, leveraging the power of natural language for single-cell data analysis and establishing an important building block for future AI-based bioinformatics research assistants.
]]></description>
<dc:creator>Schaefer, M.</dc:creator>
<dc:creator>Peneder, P.</dc:creator>
<dc:creator>Malzl, D.</dc:creator>
<dc:creator>Peycheva, M.</dc:creator>
<dc:creator>Burton, J.</dc:creator>
<dc:creator>Hakobyan, A.</dc:creator>
<dc:creator>Sharma, V.</dc:creator>
<dc:creator>Krausgruber, T.</dc:creator>
<dc:creator>Menche, J.</dc:creator>
<dc:creator>Tomazou, E. M.</dc:creator>
<dc:creator>Bock, C.</dc:creator>
<dc:date>2024-10-18</dc:date>
<dc:identifier>doi:10.1101/2024.10.15.618501</dc:identifier>
<dc:title><![CDATA[Multimodal learning of transcriptomes and text enables interactive single-cell RNA-seq data exploration with natural-language chats]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-10-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.12.23.629194v1?rss=1">
<title>
<![CDATA[
A single cell and spatial genomics atlas of human skin fibroblasts in health and disease 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.12.23.629194v1?rss=1"
</link>
<description><![CDATA[
Fibroblasts are critical cells that shape the architecture and cellular ecosystems in multiple tissues. Understanding fibroblast heterogeneity and their spatial context in health and disease has enormous clinical relevance. In this study, we constructed a spatially-resolved atlas of human skin fibroblasts from healthy skin and 23 skin disorders. We define 6 major skin fibroblast populations in health and a further three skin disease-specific fibroblast subtypes, and demonstrate the fibroblast composition in different types of skin disease. We characterise a human-specific fibroblastic reticular cell (FRC)-like subtype in the skin perivascular niche and postulate their origin from prenatal skin lymphoid tissue organiser (LTo)-like cells. We also show that inflammatory myofibroblasts (IL11+MMP1+CXCL5+IL7R+) are a conserved fibroblast subtype in inflammatory disorders and cancers across multiple human tissues. We provide a harmonised nomenclature for skin fibroblasts that integrates previous findings from human skin and other tissues.
]]></description>
<dc:creator>Steele, L.</dc:creator>
<dc:creator>Admane, C.</dc:creator>
<dc:creator>Chakala, K. P.</dc:creator>
<dc:creator>Foster, A.</dc:creator>
<dc:creator>Gopee, N. H.</dc:creator>
<dc:creator>Koplev, S.</dc:creator>
<dc:creator>Mazin, P.</dc:creator>
<dc:creator>Olabi, B.</dc:creator>
<dc:creator>Roberts, K.</dc:creator>
<dc:creator>Tudor, C.</dc:creator>
<dc:creator>Winheim, E.</dc:creator>
<dc:creator>Annusver, K.</dc:creator>
<dc:creator>Correa-Gallegos, D.</dc:creator>
<dc:creator>Forsthuber, A.</dc:creator>
<dc:creator>Francis, L.</dc:creator>
<dc:creator>Frech, S.</dc:creator>
<dc:creator>Ganier, C.</dc:creator>
<dc:creator>Layton, T.</dc:creator>
<dc:creator>Liu, Y.</dc:creator>
<dc:creator>Yuan, H.</dc:creator>
<dc:creator>Gudjonsson, J.</dc:creator>
<dc:creator>Lichtenberger, B. M.</dc:creator>
<dc:creator>Mahil, S.</dc:creator>
<dc:creator>Nanchahal, J.</dc:creator>
<dc:creator>O'Toole, E. A.</dc:creator>
<dc:creator>Plikus, M.</dc:creator>
<dc:creator>Rinkevich, Y.</dc:creator>
<dc:creator>Rognoni, E.</dc:creator>
<dc:creator>Smith, C.</dc:creator>
<dc:creator>Teichmann, S. A.</dc:creator>
<dc:creator>Kasper, M.</dc:creator>
<dc:creator>Lotfollahi, M.</dc:creator>
<dc:creator>Haniffa, M.</dc:creator>
<dc:date>2024-12-23</dc:date>
<dc:identifier>doi:10.1101/2024.12.23.629194</dc:identifier>
<dc:title><![CDATA[A single cell and spatial genomics atlas of human skin fibroblasts in health and disease]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-12-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.10.08.617279v1?rss=1">
<title>
<![CDATA[
Polybacterial Intracellular Macromolecules Shape Single-Cell Epikine Profiles in Upper Airway Mucosa 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.10.08.617279v1?rss=1"
</link>
<description><![CDATA[
The upper airway, particularly the nasal and oral mucosal epithelium, serves as a primary barrier for microbial interactions throughout life. Specialized niches like the anterior nares and the tooth are especially susceptible to dysbiosis and chronic inflammatory diseases. To investigate host-microbial interactions in mucosal epithelial cell types, we reanalyzed our single-cell RNA sequencing atlas of human oral mucosa, identifying polybacterial signatures (20% Gram-positive, 80% Gram-negative) within both epithelial- and stromal-resident cells. This analysis revealed unique responses of bacterial-associated epithelia when compared to two inflammatory disease states of mucosa. Single-cell RNA sequencing, in situ hybridization, and immunohistochemistry detected numerous persistent macromolecules from Gram-positive and Gram-negative bacteria within human oral keratinocytes (HOKs), including bacterial rRNA, mRNA and glycolipids. Epithelial cells with higher concentrations of 16S rRNA and glycolipids exhibited enhanced receptor-ligand signaling in vivo. HOKs with a spectrum of polybacterial intracellular macromolecular (PIM) concentrations were challenged with purified exogenous lipopolysaccharide, resulting in the synergistic upregulation of select innate (CXCL8, TNFSF15) and adaptive (CXCL17, CCL28) epikines. Notably, endogenous lipoteichoic acid, rather than lipopolysaccharide, directly correlated with epikine expression in vitro and in vivo. Application of the Drug2Cell algorithm to health and inflammatory disease data suggested altered drug efficacy predictions based on PIM detection. Our findings demonstrate that PIMs persist within mucosal epithelial cells at variable concentrations, linearly driving single-cell effector cytokine expression and influencing drug responses, underscoring the importance of understanding host-microbe interactions and the implications of PIMs on cell behavior in health and disease at single-cell resolution.

One-sentence summaryThis study reveals how persistent intracellular bacterial macromolecules in mucosal epithelial cells drive inflammatory signaling, offering new insights into microbial-host interactions and their potential impact on inflammatory disease treatment and drug efficacy.
]]></description>
<dc:creator>Easter, Q. T.</dc:creator>
<dc:creator>Alvarado-Martinez, Z.</dc:creator>
<dc:creator>Kunz, M.</dc:creator>
<dc:creator>Matuck, B.</dc:creator>
<dc:creator>Rupp, B.</dc:creator>
<dc:creator>Weaver, T.</dc:creator>
<dc:creator>Ren, Z.</dc:creator>
<dc:creator>Tata, A. T.</dc:creator>
<dc:creator>Caballero-Perez, J.</dc:creator>
<dc:creator>Oscarson, N.</dc:creator>
<dc:creator>Hasuike, A.</dc:creator>
<dc:creator>Ghodke, A.</dc:creator>
<dc:creator>Kimple, A.</dc:creator>
<dc:creator>Tata, P. R.</dc:creator>
<dc:creator>Randell, S. H.</dc:creator>
<dc:creator>Koo, H.</dc:creator>
<dc:creator>Ko, K. I.</dc:creator>
<dc:creator>Byrd, K. M.</dc:creator>
<dc:date>2024-10-09</dc:date>
<dc:identifier>doi:10.1101/2024.10.08.617279</dc:identifier>
<dc:title><![CDATA[Polybacterial Intracellular Macromolecules Shape Single-Cell Epikine Profiles in Upper Airway Mucosa]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-10-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.05.31.596861v1?rss=1">
<title>
<![CDATA[
Spatial Deconvolution of Cell Types and Cell States at Scale Utilizing TACIT 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.05.31.596861v1?rss=1"
</link>
<description><![CDATA[
Identifying cell types and states remains a time-consuming and error-prone challenge for spatial biology. While deep learning is increasingly used, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in health and disease. To address this, we developed TACIT, an unsupervised algorithm for cell annotation using predefined signatures that operates without training data, using unbiased thresholding to distinguish positive cells from background, focusing on relevant markers to identify ambiguous cells in multiomic assays. Using five datasets (5,000,000-cells; 51-cell types) from three niches (brain, intestine, gland), TACIT outperformed existing unsupervised methods in accuracy and scalability. Integration of TACIT-identified cell with a novel Shiny app revealed new phenotypes in two inflammatory gland diseases. Finally, using combined spatial transcriptomics and proteomics, we discover under- and overrepresented immune cell types and states in regions of interest, suggesting multimodality is essential for translating spatial biology to clinical applications.
]]></description>
<dc:creator>Huynh, K.</dc:creator>
<dc:creator>Tyc, K. M.</dc:creator>
<dc:creator>Matuck, B. F.</dc:creator>
<dc:creator>Easter, Q. T.</dc:creator>
<dc:creator>Pratapa, A.</dc:creator>
<dc:creator>Kumar, N. V.</dc:creator>
<dc:creator>Perez, P.</dc:creator>
<dc:creator>Kulchar, R.</dc:creator>
<dc:creator>Pranzatelli, T.</dc:creator>
<dc:creator>Souza, D.</dc:creator>
<dc:creator>Weaver, T. M.</dc:creator>
<dc:creator>Qu, X.</dc:creator>
<dc:creator>Alberto Valente Soares Junior, L.</dc:creator>
<dc:creator>Dolhnokoff, M.</dc:creator>
<dc:creator>Kleiner, D. E.</dc:creator>
<dc:creator>Hewitt, S. M.</dc:creator>
<dc:creator>Fernando Ferraz da Silva, L.</dc:creator>
<dc:creator>Rocha, V.</dc:creator>
<dc:creator>Warner, B. M.</dc:creator>
<dc:creator>Byrd, K. M.</dc:creator>
<dc:creator>Liu, J.</dc:creator>
<dc:date>2024-06-03</dc:date>
<dc:identifier>doi:10.1101/2024.05.31.596861</dc:identifier>
<dc:title><![CDATA[Spatial Deconvolution of Cell Types and Cell States at Scale Utilizing TACIT]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-06-03</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.08.07.669133v1?rss=1">
<title>
<![CDATA[
STARComm Scalably Detects Emergent Modules of Spatial Cell-Cell Communication in Inflammation and Cancer. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.08.07.669133v1?rss=1"
</link>
<description><![CDATA[
In humans, cell-cell communication orchestrates tissue organization, immune coordination, and repair, yet spatially mapping these interactions remains a challenge for biology. We introduce STARComm, a scalable-interpretable computational method that identifies Multicellular Communication Interaction Modules (MCIMs) by detecting spatially co-located receptor-ligand activity from high-plex spatial transcriptomics in 2D and 3D. Applied to an atlas of >14million cells across 8 cancers, STARComm revealed 24 conserved and tumor-specific MCIMs, including a fibro-immune module with targetable axes linked to immune exclusion and immunotherapy resistance. In chronic graft-versus-host disease, STARComm identified three salivary gland MCIMs predictive of patient death and two druggable axes (CXCL12-CXCR4, CCL5-SDC4), both with FDA-approved therapeutics. STARComm demonstrated that peripheral tissue profiling can forecast fatality nearly 3 years in advance using minor salivary glands. By enabling scalable biomarker discovery, drug targeting, and spatially resolved precision profiling, STARComm bridges the gap between spatial biology and clinical translation, advancing the field of spatial medicine.

SUMMARYDespite major advances in spatial biology, no framework has yet linked spatially resolved intercellular communication networks, independent of cell types, to clinical outcomes in human disease. Here, we present STARComm, a scalable method that identifies Multicellular Interaction MCIMs (MCIMs). Applying STARComm to minor salivary gland biopsies from patients with chronic graft-versus-host disease (GVHD), we identify MCIMs that not only distinguish healthy from diseased tissue but also stratify patient survival. High-risk MCIMs are enriched for actionable immune and stromal pathways, including those targetable with existing therapies. These findings establish the first outcome-linked spatial communication framework in any human disease and highlight the translational potential of oral tissues as minimally invasive platforms for real-time immune diagnostics, prognostic modeling, and therapeutic screening.
]]></description>
<dc:creator>Huynh, K.</dc:creator>
<dc:creator>Matuck, B. F.</dc:creator>
<dc:creator>de Souza, D.</dc:creator>
<dc:creator>Zhang, X.</dc:creator>
<dc:creator>Fatobene, G.</dc:creator>
<dc:creator>Soares Junior, L. A. V.</dc:creator>
<dc:creator>Ferraz da Silva, L. F.</dc:creator>
<dc:creator>Rocha, V.</dc:creator>
<dc:creator>Byrd, K.</dc:creator>
<dc:creator>Liu, J.</dc:creator>
<dc:date>2025-08-11</dc:date>
<dc:identifier>doi:10.1101/2025.08.07.669133</dc:identifier>
<dc:title><![CDATA[STARComm Scalably Detects Emergent Modules of Spatial Cell-Cell Communication in Inflammation and Cancer.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-08-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.07.14.664825v1?rss=1">
<title>
<![CDATA[
Identifying the Minimal Number of Protein Markers for Cell Type Annotation Using MiniMarS 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.07.14.664825v1?rss=1"
</link>
<description><![CDATA[
Over the past decade, there has been an explosion in the characterisation and discovery of cell populations using single-cell technologies. Single-cell multi-omics data, particularly those incorporating gene and protein expression, are increasingly commonplace and can lead to more refined characterisation of cell types. A common challenge for biologists is to isolate cells of interest using a minimal number of markers for cytometry experiments. Although several methods exist for marker selection, there is limited guidance on the relative performance of these methods, and a wrapper package that combines multiple methods is lacking. The method that performs best can vary depending on the dataset and it can be challenging for researchers to test multiple methods for a given dataset. To address these issues, we present MiniMarS (Minimal Marker Selection), an R package that serves as a wrapper for 10 different algorithms. It allows users to determine the best-performing algorithm for identifying the optimal number of markers that will delineate cell populations in their dataset. MiniMarS uses pre-annotated cells with protein features from CyTOF or sequencing-based assays such as CITE-seq and Abseq as input. Outputs include 1) the minimum number of protein markers required to identify the annotated cell populations using a range of marker selection algorithms, and 2) a range of metrics to evaluate the performance of each algorithm. MiniMarS effectively differentiated populations across various datasets, including those from human blood, bone marrow, thymus, mouse spleen, and lymph nodes, even after subsampling over 41,000 cells to 2,500 cells. MiniMarS also identified 15 markers from CITE-seq data, which were then used to successfully identify the same 11 cell subsets in a CyTOF dataset (F1 score>0.9). Additionally, we showed that by appropriately combining clusters, MiniMarS improves the F1 score of a rare population identification (<1% of total cells) by 28.7%. Together, these findings highlight the broad applicability of MiniMarS in identifying appropriate markers for distinguishing cell populations.
]]></description>
<dc:creator>Parikh, D.</dc:creator>
<dc:creator>Xue, A.</dc:creator>
<dc:creator>Liao, H.-C.</dc:creator>
<dc:creator>Wishart, C.</dc:creator>
<dc:creator>Ashhurst, T. M.</dc:creator>
<dc:creator>Putri, G.</dc:creator>
<dc:creator>Luciani, F.</dc:creator>
<dc:creator>Naik, S.</dc:creator>
<dc:creator>Salim, A.</dc:creator>
<dc:creator>Marsh-Wakefield, F.</dc:creator>
<dc:creator>Louie, R.</dc:creator>
<dc:date>2025-07-18</dc:date>
<dc:identifier>doi:10.1101/2025.07.14.664825</dc:identifier>
<dc:title><![CDATA[Identifying the Minimal Number of Protein Markers for Cell Type Annotation Using MiniMarS]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-07-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.10.30.621114v1?rss=1">
<title>
<![CDATA[
Spatiotemporal map of the developing human reproductive tract at single-cell resolution 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.10.30.621114v1?rss=1"
</link>
<description><![CDATA[
The human reproductive tract plays an essential role in species perpetuation. Its development involves complex processes of sex specification, tissue patterning and morphogenesis, which, if disrupted, can cause lifelong health issues, including infertility. Here, we generated an extensive single-cell and spatial multi-omic atlas of the human reproductive tract during prenatal development, which allowed us to answer questions that smaller-scale, organ-focused experiments could not address before. We identified potential regulators of sexual dimorphism in reproductive organs, pinpointing novel genes involved in urethral canalisation of the penis, with relevance to hypospadias. By combining histological features with gene expression data, we defined the transcription factors and cell signalling events required for the regionalisation of the Mullerian and Wolffian ducts. This led to a refinement of how the HOX code is established in the distinct reproductive organs, including increased expression of thoracic HOX genes in the rostral mesenchyme of the fallopian tube and epididymis. Our study further revealed that the epithelial regionalisation of the fallopian tube and epididymis required for sperm maturation in adulthood is established early in development. In contrast, later events in gestation or postnatally are necessary for the regionalisation of the uterocervical canal epithelium. By mapping sex-specific reproductive tract regionalisation and differentiation at the cellular level, our study offers valuable insights into the causes and potential treatments of reproductive disorders.
]]></description>
<dc:creator>Lorenzi, V.</dc:creator>
<dc:creator>Mazzeo, C. I.</dc:creator>
<dc:creator>Yayon, N.</dc:creator>
<dc:creator>Ruiz-Morales, E. R.</dc:creator>
<dc:creator>Sancho-Serra, C.</dc:creator>
<dc:creator>Wong, F. C. K.</dc:creator>
<dc:creator>Mareckova, M.</dc:creator>
<dc:creator>Tuck, L.</dc:creator>
<dc:creator>Roberts, K.</dc:creator>
<dc:creator>Li, T.</dc:creator>
<dc:creator>Jacques, M.-A.</dc:creator>
<dc:creator>He, X.</dc:creator>
<dc:creator>Barker, R.</dc:creator>
<dc:creator>Crespo, B.</dc:creator>
<dc:creator>Cakir, B.</dc:creator>
<dc:creator>Murray, S.</dc:creator>
<dc:creator>Prete, M.</dc:creator>
<dc:creator>Gu, Y.</dc:creator>
<dc:creator>Kelava, I.</dc:creator>
<dc:creator>Garcia-Alonso, L.</dc:creator>
<dc:creator>Marioni, J. C.</dc:creator>
<dc:creator>Tormo, R. V.</dc:creator>
<dc:date>2024-10-31</dc:date>
<dc:identifier>doi:10.1101/2024.10.30.621114</dc:identifier>
<dc:title><![CDATA[Spatiotemporal map of the developing human reproductive tract at single-cell resolution]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-10-31</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.07.29.667333v1?rss=1">
<title>
<![CDATA[
CD38⁺ Endothelial Remodeling Defines Spatially Diverse Vasculopathy Programs in Rapidly Advancing Oral Inflammation 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.07.29.667333v1?rss=1"
</link>
<description><![CDATA[
Oral inflammatory diseases affect nearly half of the global population. Among them, newly defined peri-implantitis and high-grade periodontitis represent rapidly advancing inflammatory disease types, marked by relatively rapid tissue destruction. Despite their prevalence, the cell mechanisms and spatial architecture driving this severity remain poorly understood. Focusing first on peri-implantitis versus low- and moderate-grade periodontitis, we applied microbial profiling, single-cell RNA sequencing (scRNA-seq), and spatial proteomics (sp-proteomics) to uncover shared pathogenic programs linked to accelerated niche breakdown. Furthermore, to preserve spatial fidelity, each tissue was anatomically orientated along the tooth- or implant- epithelial interface, analogous sites of disease origination. Laser capture microdissection followed by microbiome analysis of unique tissue compartments revealed reduced bacterial load and diversity in peri-implantitis stroma. We then expanded our version-1 Human Periodontal Atlas by integrating newly generated peri-implantitis scRNAseq data (36-total samples; 121395-cells), revealing widespread transcriptional alterations, including oxidative stress, hypoxic, and NAD+ metabolism-associated signatures, primarily in a subpopulation of TNFRSF6B+/ICAM1+post-capillary venules. We then performed high-resolution sp-proteomics (15-total samples; 337260-cells) and analyzed VEC states and associated neighborhoods via AstroSuite using newly developed tri-wise spatial analysis. This revealed CD34+-VEC loss and CD38+-VEC expansion almost exclusively in peri-implantitis. We extended this analysis to high-grade periodontitis. Mucosal biopsies from four lesion-affected and four unaffected sites within the same individuals (1:1 matched; 8-samples; 225137-cells) again demonstrated spatially restricted CD38+-VEC remodeling exclusively in affected tissues, with similar vasculopathy front patterning. The findings nominate spatially distinct vasculopathy patterning as a hallmark of rapidly advancing oral inflammation and a targetable therapeutic axis.
]]></description>
<dc:creator>Easter, Q. T.</dc:creator>
<dc:creator>Huynh, K.</dc:creator>
<dc:creator>Stolf, C.</dc:creator>
<dc:creator>Xie, J.</dc:creator>
<dc:creator>Matuck, B.</dc:creator>
<dc:creator>Hasuike, A.</dc:creator>
<dc:creator>Alvarado-Martinez, Z.</dc:creator>
<dc:creator>Chen, Z.</dc:creator>
<dc:creator>Aguiar Ribeiro, A.</dc:creator>
<dc:creator>Pareek, N.</dc:creator>
<dc:creator>Azcarate-Peril, A. M.</dc:creator>
<dc:creator>Wu, D.</dc:creator>
<dc:creator>Casarin, R.</dc:creator>
<dc:creator>Ko, K. I.</dc:creator>
<dc:creator>Liu, J.</dc:creator>
<dc:creator>Byrd, K. M.</dc:creator>
<dc:date>2025-08-01</dc:date>
<dc:identifier>doi:10.1101/2025.07.29.667333</dc:identifier>
<dc:title><![CDATA[CD38⁺ Endothelial Remodeling Defines Spatially Diverse Vasculopathy Programs in Rapidly Advancing Oral Inflammation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-08-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.08.14.669814v1?rss=1">
<title>
<![CDATA[
A molecular cell atlas of endocrine signalling in human neural organoids 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.08.14.669814v1?rss=1"
</link>
<description><![CDATA[
Hormonal signalling shapes the development of the human brain and its disruption is implicated in various neuropsychiatric conditions. However, a comprehensive and mechanistic understanding of how hormonal pathways orchestrate human neurodevelopment remains elusive. Here we present a multi-scale high resolution atlas of endocrine signalling in human neural organoids through systematic perturbations with agonists and inhibitors of seven key hormonal pathways: androgen (AND), estrogen (EST), glucocorticoid (GC), thyroid (THY), retinoic acid (RA), liver X (LX), and aryl hydrocarbon (AH). By integrating bulk and single-cell transcriptomics, high-throughput imaging and targeted steroidomics, we mapped the molecular and cellular consequences of their physiologically relevant perturbations. Retinoic acid exerted the most profound effect, promoting neuronal differentiation and maturation, consistent with its established role as a patterning factor. Our analysis further benchmarked neural organoids for in vitro endocrinology and neurotoxicology by confirming previously reported in vivo effects, such as induction of mTOR signalling by AND, alteration of disease relevant genes by GC and enhanced differentiation by TH. Furthermore, we observed that LX activation upregulates genes involved in cholesterol metabolism while AH inhibition promotes neuronal differentiation. We next uncovered extensive crosstalks between these endocrine pathways, as in the paradigmatic convergence induced by AND agonist and inhibitors of GC, TH, and LX, affecting genes related to protein folding and metabolic regulation, as also highlighted by weighted gene co-expression network analysis. Single-cell analyses pinpointed cell-type-specific responses to hormonal challenges, such as the caudalization of progenitors and neurons upon RA activation and the depletion of specific neurodevelopmental states upon AH activation. Finally, we dissected the cytoarchitectural and morphometric impact of hormonal perturbations and demonstrated that neural organoids possess active steroidogenic pathways that are functionally modulated by the tested compounds. This atlas provides a systematic quantification of the hormonal impact on human neurodevelopment, enabling the investigation of uncharted aspects in the developmental origins of neuropsychiatric traits. Through the empowering architecture of its knowledge base for iterative adoption by the community, this resource will thus be key to probe how environmental factors and genetic endocrine vulnerabilities contribute to neurodevelopmental outcomes, as well as to train advanced generative models for improving their predictive power on gene environment interactions in human neurodevelopment.
]]></description>
<dc:creator>Caporale, N.</dc:creator>
<dc:creator>Matassa, G.</dc:creator>
<dc:creator>Rigoli, M. T.</dc:creator>
<dc:creator>Castaldi, D.</dc:creator>
<dc:creator>Valenti, A.</dc:creator>
<dc:creator>Lessi, M.</dc:creator>
<dc:creator>Stucchi, S.</dc:creator>
<dc:creator>Muda, B.</dc:creator>
<dc:creator>Nagni, R.</dc:creator>
<dc:creator>Mainardi, L.</dc:creator>
<dc:creator>Melon, A.</dc:creator>
<dc:creator>Tintori, A.</dc:creator>
<dc:creator>Bulgheresi, D.</dc:creator>
<dc:creator>Kubacki, M.</dc:creator>
<dc:creator>Trattaro, S.</dc:creator>
<dc:creator>Evangelista, S.</dc:creator>
<dc:creator>Leonards, P.</dc:creator>
<dc:creator>Human Cell Atlas, B. N. O.</dc:creator>
<dc:creator>Villa, C. E.</dc:creator>
<dc:creator>Cheroni, C.</dc:creator>
<dc:creator>Testa, G.</dc:creator>
<dc:date>2025-08-15</dc:date>
<dc:identifier>doi:10.1101/2025.08.14.669814</dc:identifier>
<dc:title><![CDATA[A molecular cell atlas of endocrine signalling in human neural organoids]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-08-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.10.19.683341v1?rss=1">
<title>
<![CDATA[
The normal human lymph node cell classification and landscape defined by high-dimensional spatial proteomic. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.10.19.683341v1?rss=1"
</link>
<description><![CDATA[
Lymph nodes (LN) are key secondary lymphoid organs (SLO) for a coordinated immune response. They have been extensively characterized by numerous investigative techniques chiefly as single cell suspensions because they are composed of vagile yet crowded hematolymphoid elements, unfriendly to spatial tissue organization-saving techniques. We comprehensively classify in situ all cells of 19 human LN free of pathology with a 78-marker antibody panel, an hyperplexed cyclic staining method, MILAN, and an analytical bioinformatic pipeline, BRAQUE. A total of 77 cell types were classified, encompassing T, B, innate immune and stromal cells. CD4 and CD8 T-cells were classified into 27 unique subsets by leveraging the expression profiles of TCF7, the presence of co-inhibitory receptors and the spatial distribution. CD5 and TCF7 expression defined novel B-cell types. CD27+ mature B-cells occupied previously unrecognized nodal spaces non-overlapping with the cortex and the plasma-cell rich medullary cords. Type 2 conventional dendritic cells were located in nodular paracortical aggregates. Statistically controlled pairwise neighborhood analysis showed sparse cell-cell interactions, known and new neighbors, established and novel LN landscape niches. A high-dimensional proteomic interrogation of the normal human LN provides spatial allocation of known cell types, novel interactions and the landscape organization.
]]></description>
<dc:creator>Bolognesi, M. M.</dc:creator>
<dc:creator>Dall'Olio, L.</dc:creator>
<dc:creator>Mandelli, G. E.</dc:creator>
<dc:creator>Lorenzi, L.</dc:creator>
<dc:creator>Bosisio, F. M.</dc:creator>
<dc:creator>Haberman, A. M.</dc:creator>
<dc:creator>Bhagat, G.</dc:creator>
<dc:creator>Borghesi, S.</dc:creator>
<dc:creator>Faretta, M.</dc:creator>
<dc:creator>Castellani, G.</dc:creator>
<dc:creator>CATTORETTI, G.</dc:creator>
<dc:date>2025-10-20</dc:date>
<dc:identifier>doi:10.1101/2025.10.19.683341</dc:identifier>
<dc:title><![CDATA[The normal human lymph node cell classification and landscape defined by high-dimensional spatial proteomic.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-10-20</prism:publicationDate>
<prism:section></prism:section>
</item>
</rdf:RDF>
