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	<title>bioRxiv Channel: BICCN/BICAN</title>
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	This feed contains articles for bioRxiv Channel "BICCN/BICAN"
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	<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/2022.09.19.508480v1?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/2022.10.18.512442v1?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/2022.11.09.515833v1?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/2022.11.06.515349v1?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/2022.10.07.511366v1?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/2022.10.24.513555v1?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/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/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/2022.10.18.512724v1?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/2022.10.24.511199v1?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/2022.11.08.515739v1?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/2022.03.13.484171v1?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/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/2022.09.30.510346v1?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/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/2022.08.09.503303v1?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/2022.07.26.501598v1?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/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/2023.03.06.531121v1?rss=1">
<title>
<![CDATA[
A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.03.06.531121v1?rss=1"
</link>
<description><![CDATA[
The mammalian brain is composed of millions to billions of cells that are organized into numerous cell types with specific spatial distribution patterns and structural and functional properties. An essential step towards understanding brain function is to obtain a parts list, i.e., a catalog of cell types, of the brain. Here, we report a comprehensive and high-resolution transcriptomic and spatial cell type atlas for the whole adult mouse brain. The cell type atlas was created based on the combination of two single-cell-level, whole-brain-scale datasets: a single- cell RNA-sequencing (scRNA-seq) dataset of [~]7 million cells profiled, and a spatially resolved transcriptomic dataset of [~]4.3 million cells using MERFISH. The atlas is hierarchically organized into five nested levels of classification: 7 divisions, 32 classes, 306 subclasses, 1,045 supertypes and 5,200 clusters. We systematically analyzed the neuronal, non-neuronal, and immature neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The results reveal unique features of cell type organization in different brain regions, in particular, a dichotomy between the dorsal and ventral parts of the brain: the dorsal part contains relatively fewer yet highly divergent neuronal types, whereas the ventral part contains more numerous neuronal types that are more closely related to each other. We also systematically characterized cell-type specific expression of neurotransmitters, neuropeptides, and transcription factors. The study uncovered extraordinary diversity and heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types across the brain, suggesting they mediate a myriad of modes of intercellular communications. Finally, we found that transcription factors are major determinants of cell type classification in the adult mouse brain and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. The whole-mouse-brain transcriptomic and spatial cell type atlas establishes a benchmark reference atlas and a foundational resource for deep and integrative investigations of cell type and circuit function, development, and evolution of the mammalian brain.
]]></description>
<dc:creator>Yao, Z.</dc:creator>
<dc:creator>van Velthoven, C. T. J.</dc:creator>
<dc:creator>Kunst, M.</dc:creator>
<dc:creator>Zhang, M.</dc:creator>
<dc:creator>McMillen, D.</dc:creator>
<dc:creator>Lee, C.</dc:creator>
<dc:creator>Jung, W.</dc:creator>
<dc:creator>Goldy, J.</dc:creator>
<dc:creator>Abdelhak, A.</dc:creator>
<dc:creator>Baker, P.</dc:creator>
<dc:creator>Barkan, E.</dc:creator>
<dc:creator>Bertagnolli, D.</dc:creator>
<dc:creator>Campos, J.</dc:creator>
<dc:creator>Carey, D.</dc:creator>
<dc:creator>Casper, T.</dc:creator>
<dc:creator>Chakka, A. B.</dc:creator>
<dc:creator>Chakrabarty, R.</dc:creator>
<dc:creator>Chavan, S.</dc:creator>
<dc:creator>Chen, M.</dc:creator>
<dc:creator>Clark, M.</dc:creator>
<dc:creator>Close, J.</dc:creator>
<dc:creator>Crichton, K.</dc:creator>
<dc:creator>Daniel, S.</dc:creator>
<dc:creator>Dolbeare, T.</dc:creator>
<dc:creator>Ellingwood, L.</dc:creator>
<dc:creator>Gee, J.</dc:creator>
<dc:creator>Glandon, A.</dc:creator>
<dc:creator>Gloe, J.</dc:creator>
<dc:creator>Gould, J.</dc:creator>
<dc:creator>Gray, J.</dc:creator>
<dc:creator>Guilford, N.</dc:creator>
<dc:creator>Guzman, J.</dc:creator>
<dc:creator>Hirschstein, D.</dc:creator>
<dc:creator>Ho, W.</dc:creator>
<dc:creator>Jin, K.</dc:creator>
<dc:creator>Kroll, M.</dc:creator>
<dc:creator>Lathia, K.</dc:creator>
<dc:creator>Leon, A.</dc:creator>
<dc:creator>Long, B.</dc:creator>
<dc:creator>Maltzer, Z.</dc:creator>
<dc:creator>Martin, N.</dc:creator>
<dc:creator>McCue, R.</dc:creator>
<dc:creator>Meyerdierks, E.</dc:creator>
<dc:creator>Nguyen, T. N.</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>Slaug</dc:creator>
<dc:date>2023-03-06</dc:date>
<dc:identifier>doi:10.1101/2023.03.06.531121</dc:identifier>
<dc:title><![CDATA[A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-03-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.03.06.531348v1?rss=1">
<title>
<![CDATA[
A molecularly defined and spatially resolved cell atlas of the whole mouse brain 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.03.06.531348v1?rss=1"
</link>
<description><![CDATA[
In mammalian brains, tens of millions to billions of cells form complex interaction networks to enable a wide range of functions. The enormous diversity and intricate organization of cells in the brain have so far hindered our understanding of the molecular and cellular basis of its functions. Recent advances in spatially resolved single-cell transcriptomics have allowed systematic mapping of the spatial organization of molecularly defined cell types in complex tissues1-3. However, these approaches have only been applied to a few brain regions1-11 and a comprehensive cell atlas of the whole brain is still missing. Here, we imaged a panel of >1,100 genes in [~]8 million cells across the entire adult mouse brain using multiplexed error-robust fluorescence in situ hybridization (MERFISH)12 and performed spatially resolved, single-cell expression profiling at the whole-transcriptome scale by integrating MERFISH and single-cell RNA-sequencing (scRNA-seq) data. Using this approach, we generated a comprehensive cell atlas of >5,000 transcriptionally distinct cell clusters, belonging to [~]300 major cell types, in the whole mouse brain with high molecular and spatial resolution. Registration of the MERFISH images to the common coordinate framework (CCF) of the mouse brain further allowed systematic quantifications of the cell composition and organization in individual brain regions defined in the CCF. We further identified spatial modules characterized by distinct cell-type compositions and spatial gradients featuring gradual changes in the gene-expression profiles of cells. Finally, this high-resolution spatial map of cells, with a transcriptome-wide expression profile associated with each cell, allowed us to infer cell-type-specific interactions between several hundred pairs of molecularly defined cell types and predict potential molecular (ligand-receptor) basis and functional implications of these cell-cell interactions. These results provide rich insights into the molecular and cellular architecture of the brain and a valuable resource for future functional investigations of neural circuits and their dysfunction in diseases.
]]></description>
<dc:creator>Zhang, M.</dc:creator>
<dc:creator>Pan, X.</dc:creator>
<dc:creator>Jung, W.</dc:creator>
<dc:creator>Halpern, A.</dc:creator>
<dc:creator>Eichhorn, S. W.</dc:creator>
<dc:creator>Lei, Z.</dc:creator>
<dc:creator>Cohen, L.</dc:creator>
<dc:creator>Smith, K. A.</dc:creator>
<dc:creator>Tasic, B.</dc:creator>
<dc:creator>Yao, Z.</dc:creator>
<dc:creator>Zeng, H.</dc:creator>
<dc:creator>Zhuang, X.</dc:creator>
<dc:date>2023-03-07</dc:date>
<dc:identifier>doi:10.1101/2023.03.06.531348</dc:identifier>
<dc:title><![CDATA[A molecularly defined and spatially resolved cell atlas of the whole mouse brain]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-03-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.03.06.531307v1?rss=1">
<title>
<![CDATA[
The cell type composition of the adult mouse brain revealed by single cell and spatial genomics 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.03.06.531307v1?rss=1"
</link>
<description><![CDATA[
The function of the mammalian brain relies upon the specification and spatial positioning of diversely specialized cell types. Yet, the molecular identities of the cell types, and their positions within individual anatomical structures, remain incompletely known. To construct a comprehensive atlas of cell types in each brain structure, we paired high-throughput single-nucleus RNA-seq with Slide-seq-a recently developed spatial transcriptomics method with near-cellular resolution-across the entire mouse brain. Integration of these datasets revealed the cell type composition of each neuroanatomical structure. Cell type diversity was found to be remarkably high in the midbrain, hindbrain, and hypothalamus, with most clusters requiring a combination of at least three discrete gene expression markers to uniquely define them. Using these data, we developed a framework for genetically accessing each cell type, comprehensively characterized neuropeptide and neurotransmitter signaling, elucidated region-specific specializations in activity-regulated gene expression, and ascertained the heritability enrichment of neurological and psychiatric phenotypes. These data, available as an online resource (BrainCellData.org) should find diverse applications across neuroscience, including the construction of new genetic tools, and the prioritization of specific cell types and circuits in the study of brain diseases.
]]></description>
<dc:creator>Langlieb, J.</dc:creator>
<dc:creator>Sachdev, N.</dc:creator>
<dc:creator>Balderrama, K.</dc:creator>
<dc:creator>Nadaf, N.</dc:creator>
<dc:creator>Raj, M.</dc:creator>
<dc:creator>Murray, E.</dc:creator>
<dc:creator>Webber, J.</dc:creator>
<dc:creator>Vanderburg, C.</dc:creator>
<dc:creator>Gazestani, V.</dc:creator>
<dc:creator>Tward, D.</dc:creator>
<dc:creator>Mezias, C.</dc:creator>
<dc:creator>Li, X.</dc:creator>
<dc:creator>Norton, T.</dc:creator>
<dc:creator>Mitra, P. P.</dc:creator>
<dc:creator>Chen, F.</dc:creator>
<dc:creator>Macosko, E.</dc:creator>
<dc:date>2023-03-08</dc:date>
<dc:identifier>doi:10.1101/2023.03.06.531307</dc:identifier>
<dc:title><![CDATA[The cell type composition of the adult mouse brain revealed by single cell and spatial genomics]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-03-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.04.16.536509v1?rss=1">
<title>
<![CDATA[
Single-cell DNA Methylome and 3D Multi-omic Atlas of the Adult Mouse Brain 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.04.16.536509v1?rss=1"
</link>
<description><![CDATA[
Cytosine DNA methylation is essential in brain development and has been implicated in various neurological disorders. A comprehensive understanding of DNA methylation diversity across the entire brain in the context of the brains 3D spatial organization is essential for building a complete molecular atlas of brain cell types and understanding their gene regulatory landscapes. To this end, we employed optimized single-nucleus methylome (snmC-seq3) and multi-omic (snm3C-seq1) sequencing technologies to generate 301,626 methylomes and 176,003 chromatin conformation/methylome joint profiles from 117 dissected regions throughout the adult mouse brain. Using iterative clustering and integrating with companion whole-brain transcriptome and chromatin accessibility datasets, we constructed a methylation-based cell type taxonomy that contains 4,673 cell groups and 261 cross-modality-annotated subclasses. We identified millions of differentially methylated regions (DMRs) across the genome, representing potential gene regulation elements. Notably, we observed spatial cytosine methylation patterns on both genes and regulatory elements in cell types within and across brain regions. Brain-wide multiplexed error-robust fluorescence in situ hybridization (MERFISH2) data validated the association of this spatial epigenetic diversity with transcription and allowed the mapping of the DNA methylation and topology information into anatomical structures more precisely than our dissections. Furthermore, multi-scale chromatin conformation diversities occur in important neuronal genes, highly associated with DNA methylation and transcription changes. Brain-wide cell type comparison allowed us to build a regulatory model for each gene, linking transcription factors, DMRs, chromatin contacts, and downstream genes to establish regulatory networks. Finally, intragenic DNA methylation and chromatin conformation patterns predicted alternative gene isoform expression observed in a companion whole-brain SMART-seq3 dataset. Our study establishes the first brain-wide, single-cell resolution DNA methylome and 3D multi-omic atlas, providing an unparalleled resource for comprehending the mouse brains cellular-spatial and regulatory genome diversity.
]]></description>
<dc:creator>Liu, H.</dc:creator>
<dc:creator>Zeng, Q.</dc:creator>
<dc:creator>Zhou, J.</dc:creator>
<dc:creator>Bartlett, A.</dc:creator>
<dc:creator>Wang, B.-A.</dc:creator>
<dc:creator>Berube, P.</dc:creator>
<dc:creator>Tian, W.</dc:creator>
<dc:creator>Kenworthy, M.</dc:creator>
<dc:creator>Altshul, J.</dc:creator>
<dc:creator>Nery, J. R.</dc:creator>
<dc:creator>Chen, H.</dc:creator>
<dc:creator>Castanon, R. G.</dc:creator>
<dc:creator>Zu, S.</dc:creator>
<dc:creator>Li, Y. E.</dc:creator>
<dc:creator>Lucero, J.</dc:creator>
<dc:creator>Osteen, J. K.</dc:creator>
<dc:creator>Pinto-Duarte, A.</dc:creator>
<dc:creator>Lee, J.</dc:creator>
<dc:creator>Rink, J.</dc:creator>
<dc:creator>Cho, S.</dc:creator>
<dc:creator>Emerson, N.</dc:creator>
<dc:creator>Nunn, M.</dc:creator>
<dc:creator>O'Connor, C.</dc:creator>
<dc:creator>Yao, Z.</dc:creator>
<dc:creator>Tasic, B.</dc:creator>
<dc:creator>Smith, K. A.</dc:creator>
<dc:creator>Zeng, H.</dc:creator>
<dc:creator>Luo, C.</dc:creator>
<dc:creator>Dixon, J. R.</dc:creator>
<dc:creator>Ren, B.</dc:creator>
<dc:creator>Behrens, M. M.</dc:creator>
<dc:creator>Ecker, J. R.</dc:creator>
<dc:date>2023-04-18</dc:date>
<dc:identifier>doi:10.1101/2023.04.16.536509</dc:identifier>
<dc:title><![CDATA[Single-cell DNA Methylome and 3D Multi-omic Atlas of the Adult Mouse Brain]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-04-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.05.01.538832v1?rss=1">
<title>
<![CDATA[
Brain-wide Correspondence Between Neuronal Epigenomics and Long-Distance Projections 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.05.01.538832v1?rss=1"
</link>
<description><![CDATA[
Single-cell genetic and epigenetic analyses parse the brains billions of neurons into thousands of "cell-type" clusters, each residing in different brain structures. Many of these cell types mediate their unique functions by virtue of targeted long-distance axonal projections to allow interactions between specific cell types. Here we have used Epi-Retro-Seq to link single cell epigenomes and associated cell types to their long-distance projections for 33,034 neurons dissected from 32 different source regions projecting to 24 different targets (225 source [-&gt;]target combinations) across the whole mouse brain. We highlight uses of this large data set for interrogating both overarching principles relating projection cell types to their transcriptomic and epigenomic properties and for addressing and developing specific hypotheses about cell types and connections as they relate to genetics. We provide an overall synthesis of the data set with 926 statistical comparisons of the discriminability of neurons projecting to each target for every dissected source region. We integrate this dataset into the larger, annotated BICCN cell type atlas composed of millions of neurons to link projection cell types to consensus clusters. Integration with spatial transcriptomic data further assigns projection-enriched clusters to much smaller source regions than afforded by the original dissections. We exemplify these capabilities by presenting in-depth analyses of neurons with identified projections from the hypothalamus, thalamus, hindbrain, amygdala, and midbrain to provide new insights into the properties of those cell types, including differentially expressed genes, their associated cis-regulatory elements and transcription factor binding motifs, and neurotransmitter usage.
]]></description>
<dc:creator>Zhou, J.</dc:creator>
<dc:creator>Zhang, Z.</dc:creator>
<dc:creator>Wu, M.</dc:creator>
<dc:creator>Liu, H.</dc:creator>
<dc:creator>Pang, Y.</dc:creator>
<dc:creator>Bartlett, A.</dc:creator>
<dc:creator>Rivkin, A. C.</dc:creator>
<dc:creator>Lagos, W. N.</dc:creator>
<dc:creator>Williams, E.</dc:creator>
<dc:creator>Lee, C.-T.</dc:creator>
<dc:creator>Miyazaki, P. A.</dc:creator>
<dc:creator>Aldridge, A. I.</dc:creator>
<dc:creator>Zeng, Q.</dc:creator>
<dc:creator>Salinda, J. L. A.</dc:creator>
<dc:creator>Claffey, N.</dc:creator>
<dc:creator>Liem, M.</dc:creator>
<dc:creator>Fitzpatrick, C.</dc:creator>
<dc:creator>Boggeman, L.</dc:creator>
<dc:creator>Yao, Z.</dc:creator>
<dc:creator>Smith, K. A.</dc:creator>
<dc:creator>Tasic, B.</dc:creator>
<dc:creator>Altshul, J.</dc:creator>
<dc:creator>Kenworthy, M. A.</dc:creator>
<dc:creator>Valadon, C.</dc:creator>
<dc:creator>Nery, J. R.</dc:creator>
<dc:creator>Castanon, R. G.</dc:creator>
<dc:creator>Patne, N. S.</dc:creator>
<dc:creator>Vu, M.</dc:creator>
<dc:creator>Rashid, M.</dc:creator>
<dc:creator>Jacobs, M. W.</dc:creator>
<dc:creator>Ito-Cole, T.</dc:creator>
<dc:creator>Osteen, J.</dc:creator>
<dc:creator>Emerson, N.</dc:creator>
<dc:creator>Lee, J.</dc:creator>
<dc:creator>Cho, S.</dc:creator>
<dc:creator>Rink, J.</dc:creator>
<dc:creator>Huang, H.-H.</dc:creator>
<dc:creator>Pinto-Duartec, A.</dc:creator>
<dc:creator>Dominguez, B.</dc:creator>
<dc:creator>Smith, J. B.</dc:creator>
<dc:creator>O'Connor, C.</dc:creator>
<dc:creator>Zeng, H.</dc:creator>
<dc:creator>Lee, K.-F.</dc:creator>
<dc:creator>Mukamel, E. A.</dc:creator>
<dc:creator>Jin, X.</dc:creator>
<dc:creator>Behrens, M. M.</dc:creator>
<dc:creator>Eck</dc:creator>
<dc:date>2023-05-01</dc:date>
<dc:identifier>doi:10.1101/2023.05.01.538832</dc:identifier>
<dc:title><![CDATA[Brain-wide Correspondence Between Neuronal Epigenomics and Long-Distance Projections]]></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/2022.06.20.496914v1?rss=1">
<title>
<![CDATA[
Spatial Atlas of the Mouse Central Nervous System at Molecular Resolution 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.06.20.496914v1?rss=1"
</link>
<description><![CDATA[
Spatially charting molecular cell types at single-cell resolution across the three-dimensional (3D) volume of the brain is critical for illustrating the molecular basis of the brain anatomy and functions. Single-cell RNA sequencing (scRNA-seq) has profiled molecular cell types in the mouse brain1, 2, but cannot capture their spatial organization. Here, we employed an in situ sequencing technique, STARmap PLUS3, 4, to map more than one million high-quality cells across the whole adult mouse brain and the spinal cord, profiling 1,022 genes at subcellular resolution with a voxel size of 194 X 194 X 345 nm in 3D. We developed computational pipelines to segment, cluster, and annotate 231 molecularly defined cell types and 64 tissue regions with single-cell resolution. To create a transcriptome-wide spatial atlas, we further integrated the STARmap PLUS measurements with a published scRNA-seq atlas1, imputing 11,844 genes at the single-cell level. Finally, we engineered a highly expressed RNA barcoding system to delineate the tropism of a brain-wide transgene delivery tool, AAV-PHP.eB5, 6, revealing its single-cell resolved transduction efficiency across the molecular cell types and tissue regions of the whole mouse brain. Together, our datasets and annotations provide a comprehensive, high-resolution single-cell resource that integrates a spatial molecular atlas, cell taxonomy, brain anatomy, and genetic manipulation accessibility of the mammalian central nervous system (CNS).
]]></description>
<dc:creator>Shi, H.</dc:creator>
<dc:creator>He, Y.</dc:creator>
<dc:creator>Zhou, Y.</dc:creator>
<dc:creator>Huang, J.</dc:creator>
<dc:creator>Wang, B.</dc:creator>
<dc:creator>Tang, Z.</dc:creator>
<dc:creator>Tan, P.</dc:creator>
<dc:creator>Wu, M.</dc:creator>
<dc:creator>Lin, Z.</dc:creator>
<dc:creator>Ren, J.</dc:creator>
<dc:creator>Thapa, Y.</dc:creator>
<dc:creator>Tang, X.</dc:creator>
<dc:creator>Liu, A.</dc:creator>
<dc:creator>Liu, J.</dc:creator>
<dc:creator>Wang, X.</dc:creator>
<dc:date>2022-06-22</dc:date>
<dc:identifier>doi:10.1101/2022.06.20.496914</dc:identifier>
<dc:title><![CDATA[Spatial Atlas of the Mouse Central Nervous System at Molecular Resolution]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-06-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.04.02.535281v1?rss=1">
<title>
<![CDATA[
Single-cell long-read mRNA isoform regulation is pervasive across mammalian brain regions, cell types, and development 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.04.02.535281v1?rss=1"
</link>
<description><![CDATA[
RNA isoforms influence cell identity and function. Until recently, technological limitations prevented a genome-wide appraisal of isoform influence on cell identity in various parts of the brain. Using enhanced long-read single-cell isoform sequencing, we comprehensively analyze RNA isoforms in multiple mouse brain regions, cell subtypes, and developmental timepoints from postnatal day 14 (P14) to adult (P56). For 75% of genes, full-length isoform expression varies along one or more axes of phenotypic origin, underscoring the pervasiveness of isoform regulation across multiple scales. As expected, splicing varies strongly between cell types. However, certain gene classes including neurotransmitter release and reuptake as well as synapse turnover, harbor significant variability in the same cell type across anatomical regions, suggesting differences in network activity may influence cell-type identity. Glial brain-region specificity in isoform expression includes strong poly(A)-site regulation, whereas neurons have stronger TSS regulation. Furthermore, developmental patterns of cell-type specific splicing are especially pronounced in the murine adolescent transition from P21 to P28. The same cell type traced across development shows more isoform variability than across adult anatomical regions, indicating a coordinated modulation of functional programs dictating neural development. As most cell-type specific exons in P56 mouse hippocampus behave similarly in newly generated data from human hippocampi, these principles may be extrapolated to human brain. However, human brains have evolved additional cell-type specificity in splicing, suggesting gain-of-function isoforms. Taken together, we present a detailed single-cell atlas of full-length brain isoform regulation across development and anatomical regions, providing a previously unappreciated degree of isoform variability across multiple scales of the brain.
]]></description>
<dc:creator>Joglekar, A.</dc:creator>
<dc:creator>Hu, W.</dc:creator>
<dc:creator>Zhang, B.</dc:creator>
<dc:creator>Narykov, O.</dc:creator>
<dc:creator>Diekhans, M.</dc:creator>
<dc:creator>Balacco, J.</dc:creator>
<dc:creator>Ndhlovu, L.</dc:creator>
<dc:creator>Milner, T. A.</dc:creator>
<dc:creator>Fedrigo, O.</dc:creator>
<dc:creator>Jarvis, E. D.</dc:creator>
<dc:creator>Sheynkman, G. M.</dc:creator>
<dc:creator>Korkin, D.</dc:creator>
<dc:creator>Ross, M. E.</dc:creator>
<dc:creator>Tilgner, H. U.</dc:creator>
<dc:date>2023-04-04</dc:date>
<dc:identifier>doi:10.1101/2023.04.02.535281</dc:identifier>
<dc:title><![CDATA[Single-cell long-read mRNA isoform regulation is pervasive across mammalian brain regions, cell types, and development]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-04-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.03.28.534622v1?rss=1">
<title>
<![CDATA[
A Universal Method for Crossing Molecular and Atlas Modalities using Simplex-Based Image Varifolds and Quadratic Programming 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.03.28.534622v1?rss=1"
</link>
<description><![CDATA[
This paper explicates a solution to the problem of building correspondences between molecular-scale transcriptomics and tissue-scale atlases. The central model represents spatial transcriptomics as generalized functions encoding molecular position and high-dimensional transcriptomic-based (gene, cell type) identity. We map onto low-dimensional atlas ontologies by modeling each atlas compartment as a homogeneous random field with unknown transcriptomic feature distribution. The algorithm presented solves simultaneously for the minimizing geodesic diffeomorphism of coordinates and latent atlas transcriptomic feature fractions by alternating LDDMM optimization for coordinate transformations and quadratic programming for the latent transcriptomic variables. We demonstrate the universality of the algorithm in mapping tissue atlases to gene-based and cell-based MERFISH datasets as well as to other tissue scale atlases. The joint estimation of diffeomorphisms and latent feature distributions allows integration of diverse molecular and cellular datasets into a single coordinate system and creates an avenue of comparison amongst atlas ontologies for continued future development.
]]></description>
<dc:creator>Stouffer, K. M.</dc:creator>
<dc:creator>Trouve, A.</dc:creator>
<dc:creator>Younes, L.</dc:creator>
<dc:creator>Kunst, M.</dc:creator>
<dc:creator>Ng, L.</dc:creator>
<dc:creator>Zeng, H.</dc:creator>
<dc:creator>Anant, M.</dc:creator>
<dc:creator>Fan, J.</dc:creator>
<dc:creator>Kim, Y.</dc:creator>
<dc:creator>Miller, M. I.</dc:creator>
<dc:date>2023-03-29</dc:date>
<dc:identifier>doi:10.1101/2023.03.28.534622</dc:identifier>
<dc:title><![CDATA[A Universal Method for Crossing Molecular and Atlas Modalities using Simplex-Based Image Varifolds and Quadratic Programming]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-03-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.10.21.513201v1?rss=1">
<title>
<![CDATA[
MorphNet Predicts Cell Morphology from Single-Cell Gene Expression 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.10.21.513201v1?rss=1"
</link>
<description><![CDATA[
Gene expression and morphology both play a key role in determining the types and functions of cells, but the relationship between molecular and morphological features is largely uncharacterized. We present MorphNet, a computational approach that can draw pictures of a cells morphology from its gene expression profile. Our approach leverages paired morphology and molecular data to train a neural network that can predict nuclear or whole-cell morphology from gene expression. We employ state-of-the-art data augmentation techniques that allow training using as few as 103 images. We find that MorphNet can generate novel, realistic morphological images that retain the complex relationship between gene expression and cell appearance. We then train MorphNet to generate nuclear morphology from gene expression using brain-wide MERFISH data. In addition, we show that MorphNet can generate neuron morphologies with realistic axonal and dendritic structures. MorphNet generalizes to unseen brain regions, allowing prediction of neuron morphologies across the entire mouse isocortex and even non-cortical regions. We show that MorphNet performs meaningful latent space interpolation, allowing prediction of the effects of gene expression variation on morphology. Finally, we provide a web server that allows users to predict neuron morphologies for their own scRNA-seq data. MorphNet represents a powerful new approach for linking gene expression and morphology.
]]></description>
<dc:creator>Lee, H.</dc:creator>
<dc:creator>Welch, J. D.</dc:creator>
<dc:date>2022-10-21</dc:date>
<dc:identifier>doi:10.1101/2022.10.21.513201</dc:identifier>
<dc:title><![CDATA[MorphNet Predicts Cell Morphology from Single-Cell Gene Expression]]></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/2023.04.07.536039v1?rss=1">
<title>
<![CDATA[
Evolution of neuronal cell classes and types in the vertebrate retina 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.04.07.536039v1?rss=1"
</link>
<description><![CDATA[
The basic plan of the retina is conserved across vertebrates, yet species differ profoundly in their visual needs (Baden et al., 2020). One might expect that retinal cell types evolved to accommodate these varied needs, but this has not been systematically studied. Here, we generated and integrated single-cell transcriptomic atlases of the retina from 17 species: humans, two non-human primates, four rodents, three ungulates, opossum, ferret, tree shrew, a teleost fish, a bird, a reptile and a lamprey. Molecular conservation of the six retinal cell classes (photoreceptors, horizontal cells, bipolar cells, amacrine cells, retinal ganglion cells [RGCs] and Muller glia) is striking, with transcriptomic differences across species correlated with evolutionary distance. Major subclasses are also conserved, whereas variation among types within classes or subclasses is more pronounced. However, an integrative analysis revealed that numerous types are shared across species based on conserved gene expression programs that likely trace back to the common ancestor of jawed vertebrates. The degree of variation among types increases from the outer retina (photoreceptors) to the inner retina (RGCs), suggesting that evolution acts preferentially to shape the retinal output. Finally, we identified mammalian orthologs of midget RGCs, which comprise >80% of RGCs in the human retina, subserve high-acuity vision, and were believed to be primate-specific (Berson, 2008); in contrast, the mouse orthologs comprise <2% of mouse RGCs. Projections both primate and mouse orthologous types are overrepresented in the thalamus, which supplies the primary visual cortex. We suggest that midget RGCs are not primate innovations, but descendants of evolutionarily ancient types that decreased in size and increased in number as primates evolved, thereby facilitating high visual acuity and increased cortical processing of visual information.
]]></description>
<dc:creator>Hahn, J.</dc:creator>
<dc:creator>Monavarfeshani, A.</dc:creator>
<dc:creator>Qiao, M.</dc:creator>
<dc:creator>Kao, A.</dc:creator>
<dc:creator>Kölsch, Y.</dc:creator>
<dc:creator>Kumar, A.</dc:creator>
<dc:creator>Kunze, V. P.</dc:creator>
<dc:creator>Rasys, A. M.</dc:creator>
<dc:creator>Richardson, R.</dc:creator>
<dc:creator>Baier, H.</dc:creator>
<dc:creator>Lucas, R. J.</dc:creator>
<dc:creator>Li, W.</dc:creator>
<dc:creator>Meister, M.</dc:creator>
<dc:creator>Trachtenberg, J. T.</dc:creator>
<dc:creator>Yan, W.</dc:creator>
<dc:creator>Peng, Y.-R.</dc:creator>
<dc:creator>Sanes, J.</dc:creator>
<dc:creator>Shekhar, K.</dc:creator>
<dc:date>2023-04-08</dc:date>
<dc:identifier>doi:10.1101/2023.04.07.536039</dc:identifier>
<dc:title><![CDATA[Evolution of neuronal cell classes and types in the vertebrate retina]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-04-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.04.08.536119v1?rss=1">
<title>
<![CDATA[
Comparative single cell epigenomic analysis of gene regulatory programs in the rodent and primate neocortex 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.04.08.536119v1?rss=1"
</link>
<description><![CDATA[
Sequence divergence of cis-regulatory elements drives species-specific traits, but how this manifests in the evolution of the neocortex at the molecular and cellular level remains to be elucidated. We investigated the gene regulatory programs in the primary motor cortex of human, macaque, marmoset, and mouse with single-cell multiomics assays, generating gene expression, chromatin accessibility, DNA methylome, and chromosomal conformation profiles from a total of over 180,000 cells. For each modality, we determined species-specific, divergent, and conserved gene expression and epigenetic features at multiple levels. We find that cell type-specific gene expression evolves more rapidly than broadly expressed genes and that epigenetic status at distal candidate cis-regulatory elements (cCREs) evolves faster than promoters. Strikingly, transposable elements (TEs) contribute to nearly 80% of the human-specific cCREs in cortical cells. Through machine learning, we develop sequence-based predictors of cCREs in different species and demonstrate that the genomic regulatory syntax is highly preserved from rodents to primates. Lastly, we show that epigenetic conservation combined with sequence similarity helps uncover functional cis-regulatory elements and enhances our ability to interpret genetic variants contributing to neurological disease and traits.
]]></description>
<dc:creator>Zemke, N. R.</dc:creator>
<dc:creator>Armand, E. J.</dc:creator>
<dc:creator>Wang, W.</dc:creator>
<dc:creator>Lee, S.</dc:creator>
<dc:creator>Zhou, J.</dc:creator>
<dc:creator>Li, Y. E.</dc:creator>
<dc:creator>Liu, H.</dc:creator>
<dc:creator>Tian, W.</dc:creator>
<dc:creator>Nery, J. R.</dc:creator>
<dc:creator>Castanon, R. G.</dc:creator>
<dc:creator>Bartlett, A.</dc:creator>
<dc:creator>Osteen, J. K.</dc:creator>
<dc:creator>Li, D.</dc:creator>
<dc:creator>Zhuo, X.</dc:creator>
<dc:creator>Xu, V.</dc:creator>
<dc:creator>Miller, M.</dc:creator>
<dc:creator>Krienen, F. M.</dc:creator>
<dc:creator>Zhang, Q.</dc:creator>
<dc:creator>Taskin, N.</dc:creator>
<dc:creator>Ting, J.</dc:creator>
<dc:creator>Feng, G.</dc:creator>
<dc:creator>McCarroll, S. A.</dc:creator>
<dc:creator>Callaway, E. M.</dc:creator>
<dc:creator>Wang, T.</dc:creator>
<dc:creator>Behrens, M.</dc:creator>
<dc:creator>Lein, E. S.</dc:creator>
<dc:creator>Ecker, J. R.</dc:creator>
<dc:creator>Ren, B.</dc:creator>
<dc:date>2023-04-08</dc:date>
<dc:identifier>doi:10.1101/2023.04.08.536119</dc:identifier>
<dc:title><![CDATA[Comparative single cell epigenomic analysis of gene regulatory programs in the rodent and primate neocortex]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-04-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.03.31.535112v1?rss=1">
<title>
<![CDATA[
Preservation of co-expression defines the primary tissue fidelity of human neural organoids 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.03.31.535112v1?rss=1"
</link>
<description><![CDATA[
Human neural organoid models offer an exciting opportunity for studying often inaccessible human-specific brain development; however, it remains unclear how precisely organoids recapitulate fetal/primary tissue biology. Here, we characterize field-wide replicability and biological fidelity through a meta-analysis of single-cell RNA-sequencing data for first and second trimester human primary brain (2.95 million cells, 51 datasets) and neural organoids (1.63 million cells, 130 datasets). We quantify the degree to which primary tissue cell-type marker expression and co-expression are recapitulated in organoids across 12 different protocol types. By quantifying gene-level preservation of primary tissue co-expression, we show neural organoids lie on a spectrum ranging from virtually no signal to co-expression near indistinguishable from primary tissue data, demonstrating high fidelity is within the scope of current methods. Additionally, we show neural organoids preserve the cell-type specific co-expression of developing rather than adult cells, confirming organoids are an appropriate model for primary tissue development. Overall, quantifying the preservation of primary tissue co-expression is a powerful tool for uncovering unifying axes of variation across heterogeneous neural organoid experiments.
]]></description>
<dc:creator>Werner, J.</dc:creator>
<dc:creator>Gillis, J.</dc:creator>
<dc:date>2023-03-31</dc:date>
<dc:identifier>doi:10.1101/2023.03.31.535112</dc:identifier>
<dc:title><![CDATA[Preservation of co-expression defines the primary tissue fidelity of human neural organoids]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-03-31</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.02.26.581612v1?rss=1">
<title>
<![CDATA[
in silico transcriptome dissection of neocortical excitatory neurogenesis via joint matrix decomposition and transfer learning 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.02.26.581612v1?rss=1"
</link>
<description><![CDATA[
Vast quantities of multi-omic data have been produced to characterize the development and diversity of cell types in the cerebral cortex of humans and other mammals. To more fully harness the collective discovery potential of these data, we have assembled gene-level transcriptomic data from 188 published studies of neocortical development, including the transcriptomes of [~]30 million single-cells, extensive spatial transcriptomic experiments and RNA sequencing of sorted cells and bulk tissues: nemoanalytics.org/landing/neocortex. Applying joint matrix decomposition (SJD) to mouse, macaque and human data in this collection, we defined transcriptome dynamics that are conserved across mammalian neurogenesis and which elucidate the evolution of outer, or basal, radial glial cells. Decomposition of adult human neocortical data identified layer-specific signatures in mature neurons and, in combination with transfer learning methods in NeMO Analytics, enabled the charting of their early developmental emergence and protracted maturation across years of postnatal life. Interrogation of data from cerebral organoids demonstrated that while broad molecular elements of in vivo development are recapitulated in vitro, many layer-specific transcriptomic programs in neuronal maturation are absent. We invite computational biologists and cell biologists without coding expertise to use NeMO Analytics in their research and to fuel it with emerging data (carlocolantuoni.org).
]]></description>
<dc:creator>Sonthalia, S.</dc:creator>
<dc:creator>Li, G.</dc:creator>
<dc:creator>Blanco, X. M.</dc:creator>
<dc:creator>Casella, A.</dc:creator>
<dc:creator>Liu, J.</dc:creator>
<dc:creator>Stein-O'Brien, G.</dc:creator>
<dc:creator>Caffo, B.</dc:creator>
<dc:creator>Adkins, S.</dc:creator>
<dc:creator>Orvis, J.</dc:creator>
<dc:creator>Hertzano, R.</dc:creator>
<dc:creator>Mahurkar, A.</dc:creator>
<dc:creator>Gillis, J. A.</dc:creator>
<dc:creator>Werner, J.</dc:creator>
<dc:creator>Ma, S.</dc:creator>
<dc:creator>Micali, N.</dc:creator>
<dc:creator>Sestan, N.</dc:creator>
<dc:creator>Rakic, P.</dc:creator>
<dc:creator>Baro, G. S.</dc:creator>
<dc:creator>Ament, S. A.</dc:creator>
<dc:creator>Colantuoni, C.</dc:creator>
<dc:date>2024-02-28</dc:date>
<dc:identifier>doi:10.1101/2024.02.26.581612</dc:identifier>
<dc:title><![CDATA[in silico transcriptome dissection of neocortical excitatory neurogenesis via joint matrix decomposition and transfer learning]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-02-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.09.14.557789v1?rss=1">
<title>
<![CDATA[
Developmental Mouse Brain Common Coordinate Framework 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.09.14.557789v1?rss=1"
</link>
<description><![CDATA[
3D standard reference brains serve as key resources to understand the spatial organization of the brain and promote interoperability across different studies. However, unlike the adult mouse brain, the lack of standard 3D reference atlases for developing mouse brains has hindered advancement of our understanding of brain development. Here, we present a multimodal 3D developmental common coordinate framework (DevCCF) spanning mouse embryonic day (E) 11.5, E13.5, E15.5, E18.5, and postnatal day (P) 4, P14, and P56 with anatomical segmentations defined by a developmental ontology. At each age, the DevCCF features undistorted morphologically averaged atlas templates created from Magnetic Resonance Imaging and co-registered high-resolution templates from light sheet fluorescence microscopy. Expert-curated 3D anatomical segmentations at each age adhere to an updated prosomeric model and can be explored via an interactive 3D web-visualizer. As a use case, we employed the DevCCF to unveil the emergence of GABAergic neurons in embryonic brains. Moreover, we integrated the Allen CCFv3 into the P56 template with stereotaxic coordinates and mapped spatial transcriptome cell-type data with the developmental ontology. In summary, the DevCCF is an openly accessible resource that can be used for large-scale data integration to gain a comprehensive understanding of brain development.
]]></description>
<dc:creator>Kronman, F. A.</dc:creator>
<dc:creator>Liwang, J. K.</dc:creator>
<dc:creator>Betty, R.</dc:creator>
<dc:creator>Vanselow, D. J.</dc:creator>
<dc:creator>Wu, Y.-t.</dc:creator>
<dc:creator>Tustison, N. J.</dc:creator>
<dc:creator>Bhandiwad, A.</dc:creator>
<dc:creator>Manjila, S. B.</dc:creator>
<dc:creator>Minteer, J. A.</dc:creator>
<dc:creator>Shin, D.</dc:creator>
<dc:creator>Lee, C. H.</dc:creator>
<dc:creator>Patil, R.</dc:creator>
<dc:creator>Duda, J. T.</dc:creator>
<dc:creator>Puelles, L.</dc:creator>
<dc:creator>Gee, J. C.</dc:creator>
<dc:creator>Zhang, J.</dc:creator>
<dc:creator>Ng, L.</dc:creator>
<dc:creator>Kim, Y.</dc:creator>
<dc:date>2023-09-15</dc:date>
<dc:identifier>doi:10.1101/2023.09.14.557789</dc:identifier>
<dc:title><![CDATA[Developmental Mouse Brain Common Coordinate Framework]]></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/2024.01.16.575956v1?rss=1">
<title>
<![CDATA[
Molecular and cellular dynamics of the developing human neocortex at single-cell resolution 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.01.16.575956v1?rss=1"
</link>
<description><![CDATA[
The development of the human neocortex is a highly dynamic process and involves complex cellular trajectories controlled by cell-type-specific gene regulation1. Here, we collected paired single-nucleus chromatin accessibility and transcriptome data from 38 human neocortical samples encompassing both the prefrontal cortex and primary visual cortex. These samples span five main developmental stages, ranging from the first trimester to adolescence. In parallel, we performed spatial transcriptomic analysis on a subset of the samples to illustrate spatial organization and intercellular communication. This atlas enables us to catalog cell type-, age-, and area-specific gene regulatory networks underlying neural differentiation. Moreover, combining single-cell profiling, progenitor purification, and lineage-tracing experiments, we have untangled the complex lineage relationships among progenitor subtypes during the transition from neurogenesis to gliogenesis in the human neocortex. We identified a tripotential intermediate progenitor subtype, termed Tri-IPC, responsible for the local production of GABAergic neurons, oligodendrocyte precursor cells, and astrocytes. Remarkably, most glioblastoma cells resemble Tri-IPCs at the transcriptomic level, suggesting that cancer cells hijack developmental processes to enhance growth and heterogeneity. Furthermore, by integrating our atlas data with large-scale GWAS data, we created a disease-risk map highlighting enriched ASD risk in second-trimester intratelencephalic projection neurons. Our study sheds light on the gene regulatory landscape and cellular dynamics of the developing human neocortex.
]]></description>
<dc:creator>Wang, L.</dc:creator>
<dc:creator>Wang, C.</dc:creator>
<dc:creator>Moriano, J. A.</dc:creator>
<dc:creator>Chen, S.</dc:creator>
<dc:creator>Zhang, S.</dc:creator>
<dc:creator>Mukhtar, T.</dc:creator>
<dc:creator>Wang, S.</dc:creator>
<dc:creator>Cebrian-Silla, A.</dc:creator>
<dc:creator>Bi, Q.</dc:creator>
<dc:creator>Augustin, J. J.</dc:creator>
<dc:creator>de Oliveira, L. G.</dc:creator>
<dc:creator>Song, M.</dc:creator>
<dc:creator>Ge, X.</dc:creator>
<dc:creator>Zuo, G.</dc:creator>
<dc:creator>Paredes, M. F.</dc:creator>
<dc:creator>Huang, E. J.</dc:creator>
<dc:creator>Alvarez-Buylla, A.</dc:creator>
<dc:creator>Duan, X.</dc:creator>
<dc:creator>Li, J.</dc:creator>
<dc:creator>Kriegstein, A. R.</dc:creator>
<dc:date>2024-01-16</dc:date>
<dc:identifier>doi:10.1101/2024.01.16.575956</dc:identifier>
<dc:title><![CDATA[Molecular and cellular dynamics of the developing human neocortex at single-cell resolution]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-01-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.05.19.540391v1?rss=1">
<title>
<![CDATA[
A Hypothalamic Circuit Underlying the Dynamic Control of Social Homeostasis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.05.19.540391v1?rss=1"
</link>
<description><![CDATA[
Social grouping increases survival in many species, including humans1,2. By contrast, social isolation generates an aversive state (loneliness) that motivates social seeking and heightens social interaction upon reunion3-5. The observed rebound in social interaction triggered by isolation suggests a homeostatic process underlying the control of social drive, similar to that observed for physiological needs such as hunger, thirst or sleep3,6. In this study, we assessed social responses in multiple mouse strains and identified the FVB/NJ line as exquisitely sensitive to social isolation. Using FVB/NJ mice, we uncovered two previously uncharacterized neuronal populations in the hypothalamic preoptic nucleus that are activated during social isolation and social rebound and that orchestrate the behavior display of social need and social satiety, respectively. We identified direct connectivity between these two populations of opposite function and with brain areas associated with social behavior, emotional state, reward, and physiological needs, and showed that animals require touch to assess the presence of others and fulfill their social need, thus revealing a brain-wide neural system underlying social homeostasis. These findings offer mechanistic insight into the nature and function of circuits controlling instinctive social need and for the understanding of healthy and diseased brain states associated with social context.
]]></description>
<dc:creator>Liu, D.</dc:creator>
<dc:creator>Rahman, M.</dc:creator>
<dc:creator>Johnson, A.</dc:creator>
<dc:creator>Tsutsui-Kimura, I.</dc:creator>
<dc:creator>Pena, N.</dc:creator>
<dc:creator>Talay, M.</dc:creator>
<dc:creator>Logeman, B. L.</dc:creator>
<dc:creator>Finkbeiner, S.</dc:creator>
<dc:creator>Choi, S.</dc:creator>
<dc:creator>Capo-Battaglia, A.</dc:creator>
<dc:creator>Abdus-Saboor, I.</dc:creator>
<dc:creator>Ginty, D. D.</dc:creator>
<dc:creator>Uchida, N.</dc:creator>
<dc:creator>Watabe-Uchida, M.</dc:creator>
<dc:creator>Dulac, C.</dc:creator>
<dc:date>2023-05-19</dc:date>
<dc:identifier>doi:10.1101/2023.05.19.540391</dc:identifier>
<dc:title><![CDATA[A Hypothalamic Circuit Underlying the Dynamic Control of Social Homeostasis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-05-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.10.02.616246v1?rss=1">
<title>
<![CDATA[
Continuous cell type diversification throughout the embryonic and postnatal mouse visual cortex development 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.10.02.616246v1?rss=1"
</link>
<description><![CDATA[
The mammalian cortex is composed of a highly diverse set of cell types and develops through a series of temporally regulated events that build out the cell type and circuit foundation for cortical function. The mechanisms underlying the development of different cell types remain elusive. Single-cell transcriptomics provides the capacity to systematically study cell types across the entire temporal range of cortical development. Here, we present a comprehensive and high-resolution transcriptomic and epigenomic cell type atlas of the developing mouse visual cortex. The atlas was built from a single-cell RNA-sequencing dataset of 568,674 high-quality single-cell transcriptomes and a single-nucleus Multiome dataset of 194,545 high-quality nuclei providing both transcriptomic and chromatin accessibility profiles, densely sampled throughout the embryonic and postnatal developmental stages from E11.5 to P56. We computationally reconstructed a transcriptomic developmental trajectory map of all excitatory, inhibitory, and non-neuronal cell types in the visual cortex, identifying branching points marking the emergence of new cell types at specific developmental ages and defining molecular signatures of cellular diversification. In addition to neurogenesis, gliogenesis and early postmitotic maturation in the embryonic stage which gives rise to all the cell classes and nearly all subclasses, we find that increasingly refined cell types emerge throughout the postnatal differentiation process, including the late emergence of many cell types during the eye-opening stage (P11-P14) and the onset of critical period (P21), suggesting continuous cell type diversification at different stages of cortical development. Throughout development, we find cooperative dynamic changes in gene expression and chromatin accessibility in specific cell types, identifying both chromatin peaks potentially regulating the expression of specific genes and transcription factors potentially regulating specific peaks. Furthermore, a single gene can be regulated by multiple peaks associated with different cell types and/or different developmental stages. Collectively, our study provides the most detailed dynamic molecular map directly associated with individual cell types and specific developmental events that reveals the molecular logic underlying the continuous refinement of cell type identities in the developing visual cortex.
]]></description>
<dc:creator>Gao, Y.</dc:creator>
<dc:creator>van Velthoven, C. T. J.</dc:creator>
<dc:creator>Lee, C.</dc:creator>
<dc:creator>Thomas, E. D.</dc:creator>
<dc:creator>Bertagnolli, D.</dc:creator>
<dc:creator>Carey, D.</dc:creator>
<dc:creator>Casper, T.</dc:creator>
<dc:creator>Chakka, A. B.</dc:creator>
<dc:creator>Chakrabarty, R.</dc:creator>
<dc:creator>Clark, M.</dc:creator>
<dc:creator>Desierto, M. J.</dc:creator>
<dc:creator>Ferrer, R.</dc:creator>
<dc:creator>Gloe, J.</dc:creator>
<dc:creator>Goldy, J.</dc:creator>
<dc:creator>Guilford, N.</dc:creator>
<dc:creator>Guzman, J.</dc:creator>
<dc:creator>Halterman, C. R.</dc:creator>
<dc:creator>Hirschstein, D.</dc:creator>
<dc:creator>Ho, W.</dc:creator>
<dc:creator>James, K.</dc:creator>
<dc:creator>McCue, R.</dc:creator>
<dc:creator>Meyerdierks, E.</dc:creator>
<dc:creator>Nguy, B.</dc:creator>
<dc:creator>Pena, N.</dc:creator>
<dc:creator>Pham, T.</dc:creator>
<dc:creator>Shapovalova, N. V.</dc:creator>
<dc:creator>Sulc, J.</dc:creator>
<dc:creator>Torkelson, A.</dc:creator>
<dc:creator>Tran, A.</dc:creator>
<dc:creator>Tung, H.</dc:creator>
<dc:creator>Wang, J.</dc:creator>
<dc:creator>Ronellenfitch, K.</dc:creator>
<dc:creator>Levi, B.</dc:creator>
<dc:creator>Hawrylycz, M. J.</dc:creator>
<dc:creator>Pagan, C.</dc:creator>
<dc:creator>Dee, N.</dc:creator>
<dc:creator>Smith, K. A.</dc:creator>
<dc:creator>Tasic, B.</dc:creator>
<dc:creator>Yao, Z.</dc:creator>
<dc:creator>Zeng, H.</dc:creator>
<dc:date>2024-10-06</dc:date>
<dc:identifier>doi:10.1101/2024.10.02.616246</dc:identifier>
<dc:title><![CDATA[Continuous cell type diversification throughout the embryonic and postnatal mouse visual cortex development]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-10-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.06.18.599583v1?rss=1">
<title>
<![CDATA[
The transcriptomic and spatial organization of telencephalic GABAergic neuronal types 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.06.18.599583v1?rss=1"
</link>
<description><![CDATA[
The telencephalon of the mammalian brain comprises multiple regions and circuit pathways that play adaptive and integrative roles in a variety of brain functions. There is a wide array of GABAergic neurons in the telencephalon; they play a multitude of circuit functions, and dysfunction of these neurons has been implicated in diverse brain disorders. In this study, we conducted a systematic and in-depth analysis of the transcriptomic and spatial organization of GABAergic neuronal types in all regions of the mouse telencephalon and their developmental origins. This was accomplished by utilizing 611,423 single-cell transcriptomes from the comprehensive and high-resolution transcriptomic and spatial cell type atlas for the adult whole mouse brain we have generated, supplemented with an additional single-cell RNA-sequencing dataset containing 99,438 high-quality single-cell transcriptomes collected from the pre- and postnatal developing mouse brain. We present a hierarchically organized adult telencephalic GABAergic neuronal cell type taxonomy of 7 classes, 52 subclasses, 284 supertypes, and 1,051 clusters, as well as a corresponding developmental taxonomy of 450 clusters across different ages. Detailed charting efforts reveal extraordinary complexity where relationships among cell types reflect both spatial locations and developmental origins. Transcriptomically and developmentally related cell types can often be found in distant and diverse brain regions indicating that long-distance migration and dispersion is a common characteristic of nearly all classes of telencephalic GABAergic neurons. Additionally, we find various spatial dimensions of both discrete and continuous variations among related cell types that are correlated with gene expression gradients. Lastly, we find that cortical, striatal and some pallidal GABAergic neurons undergo extensive postnatal diversification, whereas septal and most pallidal GABAergic neuronal types emerge simultaneously during the embryonic stage with limited postnatal diversification. Overall, the telencephalic GABAergic cell type taxonomy can serve as a foundational reference for molecular, structural and functional studies of cell types and circuits by the entire community.
]]></description>
<dc:creator>van Velthoven, C. T. J.</dc:creator>
<dc:creator>Gao, Y.</dc:creator>
<dc:creator>Kunst, M.</dc:creator>
<dc:creator>Lee, C.</dc:creator>
<dc:creator>McMillen, D.</dc:creator>
<dc:creator>Chakka, A. B.</dc:creator>
<dc:creator>Casper, T.</dc:creator>
<dc:creator>Clark, M.</dc:creator>
<dc:creator>Chakrabarty, R.</dc:creator>
<dc:creator>Daniel, S.</dc:creator>
<dc:creator>Dolbeare, T.</dc:creator>
<dc:creator>Ferrer, R.</dc:creator>
<dc:creator>Gloe, J.</dc:creator>
<dc:creator>Goldy, J.</dc:creator>
<dc:creator>Guzman, J.</dc:creator>
<dc:creator>Halterman, C.</dc:creator>
<dc:creator>Ho, W.</dc:creator>
<dc:creator>Huang, M.</dc:creator>
<dc:creator>James, K.</dc:creator>
<dc:creator>McCue, R.</dc:creator>
<dc:creator>Nguy, B.</dc:creator>
<dc:creator>Pham, T.</dc:creator>
<dc:creator>Ronellenfitch, K.</dc:creator>
<dc:creator>Thomas, E. D.</dc:creator>
<dc:creator>Torkelson, A.</dc:creator>
<dc:creator>Pagan, C.</dc:creator>
<dc:creator>Kruse, L.</dc:creator>
<dc:creator>Dee, N.</dc:creator>
<dc:creator>Ng, L.</dc:creator>
<dc:creator>Waters, J.</dc:creator>
<dc:creator>Smith, K. A.</dc:creator>
<dc:creator>Tasic, B.</dc:creator>
<dc:creator>Yao, Z.</dc:creator>
<dc:creator>Zeng, H.</dc:creator>
<dc:date>2024-06-18</dc:date>
<dc:identifier>doi:10.1101/2024.06.18.599583</dc:identifier>
<dc:title><![CDATA[The transcriptomic and spatial organization of telencephalic GABAergic neuronal types]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-06-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.07.29.605664v1?rss=1">
<title>
<![CDATA[
Conservation, alteration, and redistribution of mammalian striatal interneurons 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.07.29.605664v1?rss=1"
</link>
<description><![CDATA[
Mammalian brains vary in size, structure, and function, but the extent to which evolutionarily novel cell types contribute to this variation remains unresolved1-4. Recent studies suggest there is a primate-specific population of striatal inhibitory interneurons, the TAC3 interneurons5. However, there has not yet been a detailed analysis of the spatial and phylogenetic distribution of this population. Here, we profile single cell gene expression in the developing pig (an ungulate) and ferret (a carnivore), representing 94 million years divergence from primates, and assign newborn inhibitory neurons to initial classes first specified during development6. We find that the initial class of TAC3 interneurons represents an ancestral striatal population that is also deployed towards the cortex in pig and ferret. In adult mouse, we uncover a rare population expressing Tac2, the ortholog of TAC3, in ventromedial striatum, prompting a reexamination of developing mouse striatal interneuron initial classes by targeted enrichment of their precursors. We conclude that the TAC3 interneuron initial class is conserved across Boreoeutherian mammals, with the mouse population representing Th striatal interneurons, a subset of which expresses Tac2. This study suggests that initial classes of telencephalic inhibitory neurons are largely conserved and that during evolution, neuronal types in the mammalian brain change through redistribution and fate refinement, rather than by derivation of novel precursors early in development.
]]></description>
<dc:creator>Corrigan, E. K.</dc:creator>
<dc:creator>DeBerardine, M.</dc:creator>
<dc:creator>Poddar, A.</dc:creator>
<dc:creator>Turrero Garcia, M.</dc:creator>
<dc:creator>Schmitz, M. T.</dc:creator>
<dc:creator>Harwell, C.</dc:creator>
<dc:creator>Paredes, M.</dc:creator>
<dc:creator>Krienen, F. M.</dc:creator>
<dc:creator>Pollen, A. A.</dc:creator>
<dc:date>2024-07-30</dc:date>
<dc:identifier>doi:10.1101/2024.07.29.605664</dc:identifier>
<dc:title><![CDATA[Conservation, alteration, and redistribution of mammalian striatal interneurons]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-07-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.08.18.553878v1?rss=1">
<title>
<![CDATA[
Dynamics of chromatin accessibility during human first-trimester neurodevelopment 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.08.18.553878v1?rss=1"
</link>
<description><![CDATA[
The human brain is capable of highly complex functions that develops through a tightly organized cascade of patterning events, expressed transcription factors and changes in chromatin accessibility. While extensive datasets exist describing gene expression across the developing brain with single-cell resolution, similar atlases of chromatin accessibility have been primarily focused on the forebrain. Here, we focus on the chromatin landscape and paired gene expression across the developing human brain to provide a comprehensive single cell atlas during the first trimester (6 - 13 post-conceptional weeks). We identified 135 clusters across half a million nuclei and using the multiomic measurements linked candidate cis-regulatory elements (cCREs) to gene expression. We found an increase in the number of accessible regions driven both by age and neuronal differentiation. Using a convolutional neural network we identified putative functional TF-binding sites in enhancers characterizing neuronal subtypes and we applied this model to cCREs upstream of ESRRB to elucidate its activation mechanism. Finally, by linking disease-associated SNPs to cCREs we validated putative pathogenic mechanisms in several diseases and identified midbrain-derived GABAergic neurons as being the most vulnerable to major depressive disorder related mutations. Together, our findings provide a higher degree of detail to some key gene regulatory mechanisms underlying the emergence of cell types during the first trimester. We anticipate this resource to be a valuable reference for future studies related to human neurodevelopment, such as identifying cell type specific enhancers that can be used for highly specific targeting in in vitro models.
]]></description>
<dc:creator>Mannens, C. C. A.</dc:creator>
<dc:creator>Hu, L.</dc:creator>
<dc:creator>Lonnerberg, P.</dc:creator>
<dc:creator>Schipper, M.</dc:creator>
<dc:creator>Reagor, C.</dc:creator>
<dc:creator>Li, X.</dc:creator>
<dc:creator>He, X.</dc:creator>
<dc:creator>Barker, R. A.</dc:creator>
<dc:creator>Sundstrom, E.</dc:creator>
<dc:creator>Posthuma, D.</dc:creator>
<dc:creator>Linnarsson, S.</dc:creator>
<dc:date>2023-08-20</dc:date>
<dc:identifier>doi:10.1101/2023.08.18.553878</dc:identifier>
<dc:title><![CDATA[Dynamics of chromatin accessibility during human first-trimester neurodevelopment]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-08-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.12.17.628811v1?rss=1">
<title>
<![CDATA[
A three-dimensional histological cell atlas of the developing human brain 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.12.17.628811v1?rss=1"
</link>
<description><![CDATA[
The human brain is believed to contain a full complement of neurons by the time of birth together with a substantial amount of the connectivity architecture, even though a significant amount of growth occurs postnatally. The developmental process leading to this outcome is not well understood in humans in comparison with model organisms. Previous magnetic resonance imaging (MRI) studies give three-dimensional coverage but not cellular resolution. In contrast, sparsely sampled histological or spatial omics analyses have provided cellular resolution but not dense whole brain coverage. To address the unmet need to provide a quantitative spatiotemporal map of developing human brain at cellular resolution, we leveraged tape-transfer assisted serial section histology to obtain contiguous histological series and unbiased imaging with dense coverage. Interleaved 20 thick Nissl and H&E series and MRI volumes are co-registered into multimodal reference volumes with 60 isotropic resolution, together with atlas annotations and a stereotactic coordinate system based on skull landmarks. The histological atlas volumes have significantly more contrast and texture than the MRI volumes. We computationally detect cells brain-wide to obtain quantitative characterization of the cytoarchitecture of the developing brain at 13-14 and 20-21 gestational weeks, providing the first comprehensive regional cell counts and characterizing the differential growth of the different brain compartments. Morphological characteristics permit segmentation of cell types from histology. We detected and quantified brain-wide distribution of mitotic figures representing dividing cells, providing an unprecedented spatiotemporal atlas of proliferative dynamics in the developing human brain. Further, we characterized the abundance and distribution of Cajal-Retzius cells, a transient cell population that plays essential roles in organizing glutamatergic cortical neurons into layers. Together, our study provides an unprecedented quantitative window into the developing human brain and the reference volumes and coordinate space should be useful for integrating spatial omics data sets with dense histological context.
]]></description>
<dc:creator>Jayakumar, J.</dc:creator>
<dc:creator>Sivaprakasam, M.</dc:creator>
<dc:creator>Verma, R.</dc:creator>
<dc:creator>Bota, M.</dc:creator>
<dc:creator>Joseph, J.</dc:creator>
<dc:creator>Mulay, S.</dc:creator>
<dc:creator>Kumutha, J.</dc:creator>
<dc:creator>Srinivasan, C.</dc:creator>
<dc:creator>S, S.</dc:creator>
<dc:creator>S, L.</dc:creator>
<dc:creator>Kumar, H. E.</dc:creator>
<dc:creator>Bhaduri, A.</dc:creator>
<dc:creator>Nowakowski, T. J.</dc:creator>
<dc:creator>Roy, P. K.</dc:creator>
<dc:creator>Savoia, S.</dc:creator>
<dc:creator>Banerjee, S.</dc:creator>
<dc:creator>Tward, D.</dc:creator>
<dc:creator>Mitra, P. P.</dc:creator>
<dc:date>2024-12-18</dc:date>
<dc:identifier>doi:10.1101/2024.12.17.628811</dc:identifier>
<dc:title><![CDATA[A three-dimensional histological cell atlas of the developing human brain]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-12-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.07.28.605493v1?rss=1">
<title>
<![CDATA[
Spatial dynamics of mammalian brain development and neuroinflammation by multimodal tri-omics mapping 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.07.28.605493v1?rss=1"
</link>
<description><![CDATA[
The ability to spatially map multiple layers of the omics information over different time points allows for exploring the mechanisms driving brain development, differentiation, arealization, and alterations in disease. Herein we developed and applied spatial tri-omic sequencing technologies, DBiT ARP-seq (spatial ATAC-RNA-Protein-seq) and DBiT CTRP-seq (spatial CUT&Tag- RNA-Protein-seq) together with multiplexed immunofluorescence imaging (CODEX) to map spatial dynamic remodeling in brain development and neuroinflammation. A spatiotemporal tri-omic atlas of the mouse brain was obtained at different stages from postnatal day P0 to P21, and compared to the regions of interest in the human developing brains. Specifically, in the cortical area, we discovered temporal persistence and spatial spreading of chromatin accessibility for the layer-defining transcription factors. In corpus callosum, we observed dynamic chromatin priming of myelin genes across the subregions. Together, it suggests a role for layer specific projection neurons to coordinate axonogenesis and myelination. We further mapped the brain of a lysolecithin (LPC) neuroinflammation mouse model and observed common molecular programs in development and neuroinflammation. Microglia, exhibiting both conserved and distinct programs for inflammation and resolution, are transiently activated not only at the core of the LPC lesion, but also at distal locations presumably through neuronal circuitry. Thus, this work unveiled common and differential mechanisms in brain development and neuroinflammation, resulting in a valuable data resource to investigate brain development, function and disease.
]]></description>
<dc:creator>Zhang, D.</dc:creator>
<dc:creator>Rubio Rodriguez-Kirby, L. A.</dc:creator>
<dc:creator>Lin, Y.</dc:creator>
<dc:creator>Song, M.</dc:creator>
<dc:creator>Wang, L.</dc:creator>
<dc:creator>Wang, L.</dc:creator>
<dc:creator>Kanatani, S.</dc:creator>
<dc:creator>Jimenez-Beristain, T.</dc:creator>
<dc:creator>Dang, Y.</dc:creator>
<dc:creator>Zhong, M.</dc:creator>
<dc:creator>Kukanja, P.</dc:creator>
<dc:creator>Wang, S.</dc:creator>
<dc:creator>Chen, X. L.</dc:creator>
<dc:creator>Gao, F.</dc:creator>
<dc:creator>Wang, D.</dc:creator>
<dc:creator>Hang, X.</dc:creator>
<dc:creator>Lou, X.</dc:creator>
<dc:creator>Liu, Y.</dc:creator>
<dc:creator>Chen, J.</dc:creator>
<dc:creator>Sestan, N.</dc:creator>
<dc:creator>Uhlen, P.</dc:creator>
<dc:creator>Kriegstein, A.</dc:creator>
<dc:creator>Zhao, H.</dc:creator>
<dc:creator>Castelo-Branco, G.</dc:creator>
<dc:creator>Fan, R.</dc:creator>
<dc:date>2024-07-28</dc:date>
<dc:identifier>doi:10.1101/2024.07.28.605493</dc:identifier>
<dc:title><![CDATA[Spatial dynamics of mammalian brain development and neuroinflammation by multimodal tri-omics mapping]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-07-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.06.02.543460v1?rss=1">
<title>
<![CDATA[
Multimodal analysis of neuronal maturation in the developing primate prefrontal cortex 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.06.02.543460v1?rss=1"
</link>
<description><![CDATA[
The prefrontal cortex (PFC) is critical for myriad high-cognitive functions and is associated with several neuropsychiatric disorders. Here, using Patch-seq and single-nucleus multiomic analyses, we identified genes and regulatory networks governing the maturation of distinct neuronal populations in the PFC of rhesus macaque. We discovered that specific electrophysiological properties exhibited distinct maturational kinetics and identified key genes underlying these properties. We unveiled that RAPGEF4 is important for the maturation of resting membrane potential and inward sodium current in both macaque and human. We demonstrated that knockdown of CHD8, a high-confidence autism risk gene, in human and macaque organotypic slices led to impaired maturation, via downregulation of key genes, including RAPGEF4. Restoring the expression of RAPGEF4 rescued the proper electrophysiological maturation of CHD8-deficient neurons. Our study revealed regulators of neuronal maturation during a critical period of PFC development in primates and implicated such regulators in molecular processes underlying autism.
]]></description>
<dc:creator>Gao, Y.</dc:creator>
<dc:creator>Dong, Q.</dc:creator>
<dc:creator>Arachchilage, K. H.</dc:creator>
<dc:creator>Risgaard, R.</dc:creator>
<dc:creator>Sheng, J.</dc:creator>
<dc:creator>Syed, M.</dc:creator>
<dc:creator>Schmidt, D. K.</dc:creator>
<dc:creator>Jin, T.</dc:creator>
<dc:creator>Liu, S.</dc:creator>
<dc:creator>Doherty, D.</dc:creator>
<dc:creator>Glass, I.</dc:creator>
<dc:creator>Levine, J. E.</dc:creator>
<dc:creator>Wang, D.</dc:creator>
<dc:creator>Chang, Q.</dc:creator>
<dc:creator>Zhao, X.</dc:creator>
<dc:creator>Sousa, A. M.</dc:creator>
<dc:date>2023-06-02</dc:date>
<dc:identifier>doi:10.1101/2023.06.02.543460</dc:identifier>
<dc:title><![CDATA[Multimodal analysis of neuronal maturation in the developing primate prefrontal cortex]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-06-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.12.15.694496v1?rss=1">
<title>
<![CDATA[
Cross-species consensus atlas of the primate basal ganglia 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.12.15.694496v1?rss=1"
</link>
<description><![CDATA[
The basal ganglia (BG) are conserved brain regions essential for motor control, learning, emotion, and cognition, and are implicated in neurological and psychiatric disease. Yet a unified cross-species taxonomy of BG cell types is lacking, limiting translation of BG circuit mechanisms, interpretation of human genetic risk, and development of cell type-targeted tools. We present a multiomic consensus atlas of 1.8 million nuclei from human, macaque, and marmoset spanning eight BG structures. Integrating cross-species gene expression, open chromatin, and spatial profiling enables definition of conserved and divergent cell types. Alignment to existing mouse and human atlases identifies 61 homologous cell types conserved over 80 million years. We identify a STRd D2 StrioMat Hybrid medium spiny neuron (MSN) type with molecular, electrophysiological, and morphological features that clarify hybrid MSN identities. Comparative cis-regulatory analysis reveals conserved sequence grammars that encode cell identity and inform viral targeting strategies, providing a foundational resource for BG evolution, function, and disease.
]]></description>
<dc:creator>Johansen, N. J.</dc:creator>
<dc:creator>Fu, Y.</dc:creator>
<dc:creator>Schmitz, M.</dc:creator>
<dc:creator>Dubuc, A.</dc:creator>
<dc:creator>Kempynck, N.</dc:creator>
<dc:creator>Wirthlin, M.</dc:creator>
<dc:creator>Garcia, A. D.</dc:creator>
<dc:creator>Hewitt, M.</dc:creator>
<dc:creator>Turner, M. A.</dc:creator>
<dc:creator>Seeman, S. C.</dc:creator>
<dc:creator>Long, B.</dc:creator>
<dc:creator>Liu, X.-P.</dc:creator>
<dc:creator>Dan, S.</dc:creator>
<dc:creator>DeBerardine, M.</dc:creator>
<dc:creator>Kapen, I.</dc:creator>
<dc:creator>Yanny, A. M.</dc:creator>
<dc:creator>Avola, A.</dc:creator>
<dc:creator>Barlow, S. T.</dc:creator>
<dc:creator>Bertagnolli, D.</dc:creator>
<dc:creator>Bhandiwad, A.</dc:creator>
<dc:creator>Budzillo, A.</dc:creator>
<dc:creator>Caballero, V. E. N.</dc:creator>
<dc:creator>Caceres, L.</dc:creator>
<dc:creator>Casper, T.</dc:creator>
<dc:creator>Chakka, A. B.</dc:creator>
<dc:creator>Chakrabarty, R.</dc:creator>
<dc:creator>Clark, M.</dc:creator>
<dc:creator>Daniel, S.</dc:creator>
<dc:creator>Eggermont, J.</dc:creator>
<dc:creator>Ferrer, R.</dc:creator>
<dc:creator>French, L.</dc:creator>
<dc:creator>Gloe, J.</dc:creator>
<dc:creator>Goldy, J.</dc:creator>
<dc:creator>Guilford, N.</dc:creator>
<dc:creator>Guzman, J.</dc:creator>
<dc:creator>Hirschstein, D.</dc:creator>
<dc:creator>Ho, W.</dc:creator>
<dc:creator>James, K.</dc:creator>
<dc:creator>Jones, D. L.</dc:creator>
<dc:creator>Jungert, M.</dc:creator>
<dc:creator>Kannan, M.</dc:creator>
<dc:creator>Kedzierska, K. Z.</dc:creator>
<dc:creator>Kroes, T.</dc:creator>
<dc:creator>Leytze, M.</dc:creator>
<dc:creator>Manning, A.</dc:creator>
<dc:creator>McCu</dc:creator>
<dc:date>2025-12-16</dc:date>
<dc:identifier>doi:10.64898/2025.12.15.694496</dc:identifier>
<dc:title><![CDATA[Cross-species consensus atlas of the primate basal ganglia]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-12-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.11.22.688128v1?rss=1">
<title>
<![CDATA[
A cross-species spatial transcriptomic atlas of the human and non-human primate basal ganglia 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.11.22.688128v1?rss=1"
</link>
<description><![CDATA[
The basal ganglia are interconnected subcortical nuclei with complex topographical organization that orchestrate goal-directed behaviors and are implicated in neurodegenerative movement disorders. We generated a cellular-resolution, spatial transcriptomic atlas of the basal ganglia in human, rhesus macaque, and common marmoset, sampling over one million cells in each species. By integrating spatial data with a cross-species, consensus snRNA-seq cell type taxonomy, this atlas reveals conserved principles of molecular organization within and across structures. The cellular architecture is complex but highly stereotyped, with gene expression gradients superimposed onto discrete compartments. Extensive spatial sampling illuminates 3D gradients of molecular organization in the striatum and reveals cell type-specific core and shell compartments in the primate internal globus pallidus, which is conserved with mouse. This unified, cross-species spatial transcriptomic atlas will be a foundational resource for characterizing the molecular and functional organization of the basal ganglia and their roles in health and disease.
]]></description>
<dc:creator>Hewitt, M. N.</dc:creator>
<dc:creator>Turner, M. A.</dc:creator>
<dc:creator>Johansen, N.</dc:creator>
<dc:creator>McMillen, D. A.</dc:creator>
<dc:creator>Dan, S.</dc:creator>
<dc:creator>DeBerardine, M.</dc:creator>
<dc:creator>Ruiz, A.</dc:creator>
<dc:creator>Huang, M.</dc:creator>
<dc:creator>Quon, J.</dc:creator>
<dc:creator>Fu, Y.</dc:creator>
<dc:creator>Kapen, I.</dc:creator>
<dc:creator>Barta, S.</dc:creator>
<dc:creator>Martin, N.</dc:creator>
<dc:creator>Cuevas, N. V.</dc:creator>
<dc:creator>Olsen, P.</dc:creator>
<dc:creator>Nagra, J.</dc:creator>
<dc:creator>Campos, J.</dc:creator>
<dc:creator>VanNess, M. M.</dc:creator>
<dc:creator>Ransford, S.</dc:creator>
<dc:creator>Juneau, Z.</dc:creator>
<dc:creator>Hastings, S.</dc:creator>
<dc:creator>Ching, L.</dc:creator>
<dc:creator>Kunst, M.</dc:creator>
<dc:creator>Basu, S.</dc:creator>
<dc:creator>Hollt, T.</dc:creator>
<dc:creator>Li, C.</dc:creator>
<dc:creator>Lelieveldt, B.</dc:creator>
<dc:creator>Yazdani, F.</dc:creator>
<dc:creator>Zhang, Q.</dc:creator>
<dc:creator>Levandowski, K.</dc:creator>
<dc:creator>Feng, G.</dc:creator>
<dc:creator>Rosen, B.</dc:creator>
<dc:creator>Glasser, M. F.</dc:creator>
<dc:creator>Hayashi, T.</dc:creator>
<dc:creator>Garcia, A. D.</dc:creator>
<dc:creator>Kana, O.</dc:creator>
<dc:creator>Maltzer, Z. M.</dc:creator>
<dc:creator>Campagnola, L.</dc:creator>
<dc:creator>Jarsky, T.</dc:creator>
<dc:creator>Kruse, L.</dc:creator>
<dc:creator>Freiwald, W.</dc:creator>
<dc:creator>Keene, C. D.</dc:creator>
<dc:creator>Van Essen, D. C.</dc:creator>
<dc:creator>Ariza, J.</dc:creator>
<dc:creator>Waters, J.</dc:creator>
<dc:creator>Krienen, F. M.</dc:creator>
<dc:creator>Bakken, T. E</dc:creator>
<dc:date>2025-11-24</dc:date>
<dc:identifier>doi:10.1101/2025.11.22.688128</dc:identifier>
<dc:title><![CDATA[A cross-species spatial transcriptomic atlas of the human and non-human primate basal ganglia]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-11-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2026.02.12.705594v1?rss=1">
<title>
<![CDATA[
A Multimodal Single-Cell Epigenomic and 3D Genome Atlas of the Human Basal Ganglia 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2026.02.12.705594v1?rss=1"
</link>
<description><![CDATA[
The basal ganglia are a group of forebrain nuclei critical for motor control and reward processing, and their dysfunction contributes to neurological and neuropsychiatric disorders. Here, we present the first multimodal single-cell epigenomic atlas of the human basal ganglia across major subregions and cell types. We jointly profiled DNA methylation and 3D chromatin conformation in 197,003 nuclei from eight basal ganglia subregions using multi-omic sequencing (snm3C-seq), and integrated these data with existing DNA methylation and chromatin conformation sequencing datasets to build a unified atlas of 261,331 cells spanning 31 subclasses and 59 groups. This atlas reveals extensive cell-type- and region-specific differential methylation, enriched for distinct transcription factor motifs, and validated by MERFISH spatial transcriptomics, which uncovered epigenetic gradients linked to transcriptional output. Compared to neuronal cells, non-neuronal cells exhibit distinct 3D genome organization including smaller chromatin compartments, increased long-range inter-compartment contacts, shorter loops, and stronger CG hypomethylation in A compartments. We further identified genes that display compartment switches, are strongly correlated with compartment scores, and exhibit differential domain boundaries and chromatin looping across basal ganglia cell types. We identified multiple medium spiny neuron subtypes defined by distinct hypomethylated signature genes, with 3D genome embeddings emphasizing dorsal, ventral, and hybrid populations. By integrating chromatin accessibility and histone modification profiles, we reconstructed cell-type-resolved enhancer-promoter links and gene regulatory networks, providing a comprehensive epigenomic framework for interpreting genetic risk loci and regulatory architecture in the human basal ganglia.
]]></description>
<dc:creator>Ding, W.</dc:creator>
<dc:creator>Klein, A.</dc:creator>
<dc:creator>Baez-Becerra, C. T.</dc:creator>
<dc:creator>Rink, J. A.</dc:creator>
<dc:creator>Bartlett, A.</dc:creator>
<dc:creator>Zeng, Q.</dc:creator>
<dc:creator>Wang, R.</dc:creator>
<dc:creator>Castanon, R. G.</dc:creator>
<dc:creator>Nery, J. R.</dc:creator>
<dc:creator>Osgood, E.</dc:creator>
<dc:creator>Owens, W.</dc:creator>
<dc:creator>Petrella, A.</dc:creator>
<dc:creator>Chen, C.</dc:creator>
<dc:creator>Acerbo, A. S.</dc:creator>
<dc:creator>Barcoma, A. S.</dc:creator>
<dc:creator>Liu, J.</dc:creator>
<dc:creator>Russo, K. G.</dc:creator>
<dc:creator>Knutson, K. W.</dc:creator>
<dc:creator>Young, C. K.</dc:creator>
<dc:creator>Willier, J. K.</dc:creator>
<dc:creator>Barragan, C.</dc:creator>
<dc:creator>Arzavala, J.</dc:creator>
<dc:creator>Cho, S.</dc:creator>
<dc:creator>Altshul, J.</dc:creator>
<dc:creator>Chan, D.</dc:creator>
<dc:creator>Soma, E.</dc:creator>
<dc:creator>Luo, J.</dc:creator>
<dc:creator>Jain, M.</dc:creator>
<dc:creator>Velazquez, S.</dc:creator>
<dc:creator>Schenker-Ahmed, N.</dc:creator>
<dc:creator>Sundaram, G. V.</dc:creator>
<dc:creator>Manning, A. C.</dc:creator>
<dc:creator>Sanchez, Y.</dc:creator>
<dc:creator>Bikkina, A.</dc:creator>
<dc:creator>Fu, S.</dc:creator>
<dc:creator>OConnor, C.</dc:creator>
<dc:creator>Liem, M.</dc:creator>
<dc:creator>Marrin, M. V.</dc:creator>
<dc:creator>Rose, C.</dc:creator>
<dc:creator>Alt, S. N.</dc:creator>
<dc:creator>Berry, J.</dc:creator>
<dc:creator>Kern, C.</dc:creator>
<dc:creator>Boone, E.</dc:creator>
<dc:creator>Tian, W.</dc:creator>
<dc:creator>Wu, Y.</dc:creator>
<dc:creator>Hariharan, M.</dc:creator>
<dc:creator>Fu, Y.</dc:creator>
<dc:creator>Xie, Y.</dc:creator>
<dc:creator>L</dc:creator>
<dc:date>2026-02-14</dc:date>
<dc:identifier>doi:10.64898/2026.02.12.705594</dc:identifier>
<dc:title><![CDATA[A Multimodal Single-Cell Epigenomic and 3D Genome Atlas of the Human Basal Ganglia]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2026-02-14</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2026.02.03.703645v1?rss=1">
<title>
<![CDATA[
Single-cell Multiome Analysis of Chromatin State and Transcriptome in the Human Basal Ganglia 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2026.02.03.703645v1?rss=1"
</link>
<description><![CDATA[
The basal ganglia play essential roles in motor control, emotion, learning and reward processing. Their dysfunction contributes to many neurological and psychiatric disorders. However, the gene regulatory programs defining basal ganglia cell-type identity and function remain poorly understood, limiting interpretation of disease-associated non-coding variants. Here, we present the first single-cell multiome atlas of histone modifications and transcriptomes across eight basal ganglia regions from neurotypical adult human donors. Joint profiling reveals cell-type-specific deployment of active and repressive cis-regulatory elements and gene regulatory networks, and suggests a combinatorial homeobox transcription factor code underlying cell identity. Integration with matched spatial transcriptomic MERFISH data uncovers regional heterogeneity of epigenomic landscapes. Comparative analysis between human and mouse medium spiny neurons uncovers conservation of core gene regulatory features. This atlas interprets non-coding risk variants of neuropsychiatric disorders and supports the development of a deep learning model to predict gene regulation and functional effects of disease-associated variants.

HIGHLIGHTSO_LIJoint single-cell profiling of transcriptomes and three histone modifications across eight human basal ganglia regions characterizes active and repressive chromatin states at cell-type resolution.
C_LIO_LICell-type-specific gene regulatory programs decode combinatorial homeobox TF grammar governing the identity and diversification of basal ganglia neurons.
C_LIO_LIIntergrative analyses link noncoding neuropsychiatric risk variants to specific cell types, regulatory elements, and candidate target genes.
C_LIO_LIA sequence-to-function deep-learning model predicts gene regulation from DNA sequence and prioritizes functional disease-associated variants.
C_LI
]]></description>
<dc:creator>Chang, L.</dc:creator>
<dc:creator>Li, K.</dc:creator>
<dc:creator>Xie, Y.</dc:creator>
<dc:creator>Zhong, G.</dc:creator>
<dc:creator>Rink, J. A.</dc:creator>
<dc:creator>Baez-Becerra, C. T.</dc:creator>
<dc:creator>Lie, A.</dc:creator>
<dc:creator>Indralingam, H. S.</dc:creator>
<dc:creator>Dong, K.</dc:creator>
<dc:creator>Loe, T.</dc:creator>
<dc:creator>Wang, Z.</dc:creator>
<dc:creator>Zu, S.</dc:creator>
<dc:creator>Kern, J. C.</dc:creator>
<dc:creator>Zhao, Z.</dc:creator>
<dc:creator>Boone, E.</dc:creator>
<dc:creator>Flores, J.</dc:creator>
<dc:creator>Monell, A.</dc:creator>
<dc:creator>Olness, J.</dc:creator>
<dc:creator>Barragan, C.</dc:creator>
<dc:creator>Osgood, E.</dc:creator>
<dc:creator>Owens, W.</dc:creator>
<dc:creator>Schenker-Ahmed, N.</dc:creator>
<dc:creator>Zhang, W.</dc:creator>
<dc:creator>Liu, D.</dc:creator>
<dc:creator>Barcoma, A. S.</dc:creator>
<dc:creator>Willier, J. K.</dc:creator>
<dc:creator>Knutson, K. W.</dc:creator>
<dc:creator>Russo, K. G.</dc:creator>
<dc:creator>Liu, J.</dc:creator>
<dc:creator>Cho, S.</dc:creator>
<dc:creator>Arzavala, J.</dc:creator>
<dc:creator>Young, C. K.</dc:creator>
<dc:creator>Sundaram, G. V.</dc:creator>
<dc:creator>Manning, A. C.</dc:creator>
<dc:creator>Sanchez, Y.</dc:creator>
<dc:creator>Bikkina, A.</dc:creator>
<dc:creator>Berry, J.</dc:creator>
<dc:creator>Gao, X.</dc:creator>
<dc:creator>OConnor, C.</dc:creator>
<dc:creator>Liem, M.</dc:creator>
<dc:creator>Marrin, M. V.</dc:creator>
<dc:creator>Rose, C.</dc:creator>
<dc:creator>Alt, S. N.</dc:creator>
<dc:creator>Zhu, C.</dc:creator>
<dc:creator>Zemke, N. R.</dc:creator>
<dc:creator>Ding, W.</dc:creator>
<dc:creator>Klein, A.</dc:creator>
<dc:creator>Fu, Y.</dc:creator>
<dc:creator>Johansen, N.</dc:creator>
<dc:creator>B</dc:creator>
<dc:date>2026-02-04</dc:date>
<dc:identifier>doi:10.64898/2026.02.03.703645</dc:identifier>
<dc:title><![CDATA[Single-cell Multiome Analysis of Chromatin State and Transcriptome in the Human Basal Ganglia]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2026-02-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.12.02.691876v1?rss=1">
<title>
<![CDATA[
Multiscale Spatial Transcriptomic Atlas of Human Basal Ganglia Cell-Type and Cellular Community Organization 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.12.02.691876v1?rss=1"
</link>
<description><![CDATA[
We generated a multi-region, subcellular-resolution spatial transcriptomic atlas of the human basal ganglia by integrating MERFISH+ and Stereo-seq across four neurotypical donors. These datasets profiled [~]7 million cells spanning the caudate, putamen, nucleus accumbens, and globus pallidus, resolving 60 transcriptionally distinct cell types. We show region-selective, molecular and spatial diversification of medium-spiny-neuron cell types and multiple non-neuronal populations with distinct molecular identities and spatial localizations. Subcellular RNA localization captures somatic size and projection-inferred signatures that reflect direct and indirect pathway topology. Cellular community analyses reveal the enrichment of sub-clusters of astrocytes and oligodendrocytes at striosome-matrix borders, while primate-expanded interneurons are confined to matrix territories. Cross-species mapping uncovers orthologous striosome-matrix organization and conserved dorsolateral-ventromedial gene expression gradients. This atlas provides a foundational molecular and spatial framework for studying human basal ganglia architecture, offering a multi-centimeter scale resource that links cell types, spatial architecture, and subcellular transcript topography across multiple nuclei.

HighlightsO_LIOur multi-centimeter scale spatial taxonomy identifies the precise locations of 60 neuronal and glial cell types of human basal ganglia.
C_LIO_LIMERFISH+ and Stereo-seq platforms map consistent spatial modules that align with classical neuroanatomical nuclei.
C_LIO_LID1D2 hybrid MSNs and primate-expanded interneurons show regional and domain specific organization
C_LIO_LISubcellular RNA localization reports soma morphology and projection-inferred signatures.
C_LI
]]></description>
<dc:creator>Berackey, B. T.</dc:creator>
<dc:creator>Tan, Z.</dc:creator>
<dc:creator>Wu, G.</dc:creator>
<dc:creator>Das, S. C.</dc:creator>
<dc:creator>Li, R.</dc:creator>
<dc:creator>Esser, B.</dc:creator>
<dc:creator>Ye, Q.</dc:creator>
<dc:creator>Nafisi, M.</dc:creator>
<dc:creator>Park, S. S.</dc:creator>
<dc:creator>Sequeira Mendieta, P. A.</dc:creator>
<dc:creator>Berry, J.</dc:creator>
<dc:creator>Mamdani, F.</dc:creator>
<dc:creator>Zhu, Q.</dc:creator>
<dc:creator>Holmes, T. C.</dc:creator>
<dc:creator>Li, D.</dc:creator>
<dc:creator>Wang, T.</dc:creator>
<dc:creator>Behrens, M. M.</dc:creator>
<dc:creator>Ren, B.</dc:creator>
<dc:creator>Ecker, J. R.</dc:creator>
<dc:creator>Bintu, B.</dc:creator>
<dc:creator>Xu, X.</dc:creator>
<dc:date>2025-12-05</dc:date>
<dc:identifier>doi:10.64898/2025.12.02.691876</dc:identifier>
<dc:title><![CDATA[Multiscale Spatial Transcriptomic Atlas of Human Basal Ganglia Cell-Type and Cellular Community Organization]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-12-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2026.02.02.703166v1?rss=1">
<title>
<![CDATA[
Single-Cell Atlas of Transcription and Chromatin States Reveals Regulatory Programs in the Human Brain 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2026.02.02.703166v1?rss=1"
</link>
<description><![CDATA[
Directly measuring chromatin states alongside transcription is essential for understanding how cell-type-specific regulatory programs are established and maintained in the adult human brain. We present a large-scale single-cell multimodal atlas generated by jointly profiling transcriptome with active (H3K27ac) and repressive (H3K27me3) histone modifications across 18 brain regions. We profile >750,000 nuclei spanning 160 cell types and integrate these data with chromatin accessibility, DNA methylation, 3D genome architecture, and spatial transcriptome. This framework annotates >500,000 regulatory elements and resolves cell-type-specific chromatin states. We link enhancers to target genes, infer gene regulatory networks, and classify chromatin interactions, revealing neuron-enriched long-range Polycomb repression of developmental genes. Integrating these maps with GWAS data and sequence-based model prioritizes noncoding variants, effector genes, and vulnerable cell types for neuropsychiatric disorders. Finally, cross-species comparisons show conserved activation but more divergent repression. Together, this study provides a functional reference for interpreting noncoding variants, epigenetic memory, and brain organization.

HIGHLIGHTSO_LIJoint single-cell profiling of transcriptomes with active or repressive histone modification in >750,000 nuclei across adult human brain.
C_LIO_LIChromatin state annotation of >500,000 candidate cis-regulatory elements distinguishes active enhancers from accessible and Polycomb-repressed regions.
C_LIO_LICell-type-resolved regulatory networks and sequence-based deep learning model prioritize functional neuropsychiatric risk variants.
C_LIO_LISpatial epigenomic imputation reveals laminar layer-specific Polycomb repression programs.
C_LIO_LIIntegration with 3D genome architecture reveals neuron-specific super long-range chromatin loops silencing early developmental genes.
C_LIO_LIEvolutionary analysis uncovers conserved active regulatory grammar but divergent repressive landscape.
C_LI
]]></description>
<dc:creator>Xie, Y.</dc:creator>
<dc:creator>Chang, L.</dc:creator>
<dc:creator>Zhong, G.</dc:creator>
<dc:creator>Rink, J. A.</dc:creator>
<dc:creator>Baez-Becerra, T.</dc:creator>
<dc:creator>Armand, E.</dc:creator>
<dc:creator>Ding, W.</dc:creator>
<dc:creator>Li, K.</dc:creator>
<dc:creator>Boone, E.</dc:creator>
<dc:creator>Lie, A.</dc:creator>
<dc:creator>Indralingam, H. S.</dc:creator>
<dc:creator>Dong, K.</dc:creator>
<dc:creator>Loe, T.</dc:creator>
<dc:creator>Huang, B.</dc:creator>
<dc:creator>Wang, Z.</dc:creator>
<dc:creator>Barcoma, A. S.</dc:creator>
<dc:creator>Willier, J. K.</dc:creator>
<dc:creator>Knutson, K. W.</dc:creator>
<dc:creator>Liu, J.</dc:creator>
<dc:creator>Cho, S.</dc:creator>
<dc:creator>Cao, S.</dc:creator>
<dc:creator>Russo, K. G.</dc:creator>
<dc:creator>Young, C. K.</dc:creator>
<dc:creator>Arzavala, J.</dc:creator>
<dc:creator>Sanchez, Y.</dc:creator>
<dc:creator>Bikkina, A.</dc:creator>
<dc:creator>Schenker-Ahmed, N.</dc:creator>
<dc:creator>Kern, C.</dc:creator>
<dc:creator>Zhao, Z.</dc:creator>
<dc:creator>Klein, A.</dc:creator>
<dc:creator>Flores, J.</dc:creator>
<dc:creator>Tai, C.-Y.</dc:creator>
<dc:creator>Olness, J.</dc:creator>
<dc:creator>Monell, A.</dc:creator>
<dc:creator>Moghadami, S.</dc:creator>
<dc:creator>Barragan, C.</dc:creator>
<dc:creator>Chen, C.</dc:creator>
<dc:creator>Owens, W.</dc:creator>
<dc:creator>OConnor, C.</dc:creator>
<dc:creator>Liem, M.</dc:creator>
<dc:creator>Marrin, M. V.</dc:creator>
<dc:creator>Rose, C.</dc:creator>
<dc:creator>Alt, S. N.</dc:creator>
<dc:creator>Emerson, N.</dc:creator>
<dc:creator>Osteen, J.</dc:creator>
<dc:creator>Lucero, J.</dc:creator>
<dc:creator>Li, D.</dc:creator>
<dc:creator>Hodge, R. D.</dc:creator>
<dc:creator>Wang, T.</dc:creator>
<dc:creator>Keene,</dc:creator>
<dc:date>2026-02-03</dc:date>
<dc:identifier>doi:10.64898/2026.02.02.703166</dc:identifier>
<dc:title><![CDATA[Single-Cell Atlas of Transcription and Chromatin States Reveals Regulatory Programs in the Human Brain]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2026-02-03</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.11.22.689869v1?rss=1">
<title>
<![CDATA[
Spatial patterning of transcriptional and regulatory programs in the primate subcortex 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.11.22.689869v1?rss=1"
</link>
<description><![CDATA[
Mammalian brain cell identity is shaped by intrinsic factors and external context. We present a spatially resolved transcriptomic and gene regulatory atlas of cell types found across all subcortical brain regions in a primate - the marmoset monkey. Dense sampling and cross-species integrations revealed spatially precise neuronal assemblies, including in complex structures such as hypothalamus. We find chromatin accessibility and transcriptional identity are spatially tuned within and across subcortical structures. Spatial gradients within subfields of the hippocampal formation are predominantly orchestrated by graded transcription factors coupled with graded enhancers. The primate-expanded population of GABAergic neurons in the thalamus shares transcriptional and regulatory syntax with neurons in superior colliculus, reflecting an evolutionary adaptation compared with rodents. We show that unexpected transcriptional convergence, such as between striatal GABAergic medium spiny neurons and telencephalic glutamatergic neurons, can arise when distinct gene regulatory networks impinge on the same downstream genes.
]]></description>
<dc:creator>Dan, S.</dc:creator>
<dc:creator>Turner, M. A.</dc:creator>
<dc:creator>DeBerardine, M.</dc:creator>
<dc:creator>Caceres, L.</dc:creator>
<dc:creator>Nieto, V. E.</dc:creator>
<dc:creator>Schembri, J.</dc:creator>
<dc:creator>Levandowski, K.</dc:creator>
<dc:creator>Cardenas, C.</dc:creator>
<dc:creator>Wang, N.</dc:creator>
<dc:creator>Li, C.</dc:creator>
<dc:creator>Basu, S.</dc:creator>
<dc:creator>Zheng, H.</dc:creator>
<dc:creator>Liu, X.</dc:creator>
<dc:creator>Hollt, T.</dc:creator>
<dc:creator>Feliciano, J.</dc:creator>
<dc:creator>Zhang, Q.</dc:creator>
<dc:creator>Nakamoto, R.</dc:creator>
<dc:creator>McMillen, D. A.</dc:creator>
<dc:creator>Martin, N.</dc:creator>
<dc:creator>Cuevas, N. V.</dc:creator>
<dc:creator>Olsen, P.</dc:creator>
<dc:creator>Nagra, J.</dc:creator>
<dc:creator>Campos, J.</dc:creator>
<dc:creator>VanNess, M. M.</dc:creator>
<dc:creator>Waters, J.</dc:creator>
<dc:creator>Ransford, S.</dc:creator>
<dc:creator>Juneau, Z.</dc:creator>
<dc:creator>Hastings, S.</dc:creator>
<dc:creator>Barta, S.</dc:creator>
<dc:creator>Ruiz, A.</dc:creator>
<dc:creator>Ariza, J.</dc:creator>
<dc:creator>Raphael, B. J.</dc:creator>
<dc:creator>Zeng, H.</dc:creator>
<dc:creator>Lein, E. S.</dc:creator>
<dc:creator>Lelieveldt, B.</dc:creator>
<dc:creator>Feng, G.</dc:creator>
<dc:creator>Long, B.</dc:creator>
<dc:creator>Krienen, F. M.</dc:creator>
<dc:date>2025-11-24</dc:date>
<dc:identifier>doi:10.1101/2025.11.22.689869</dc:identifier>
<dc:title><![CDATA[Spatial patterning of transcriptional and regulatory programs in the primate subcortex]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-11-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2026.02.26.708019v1?rss=1">
<title>
<![CDATA[
Morphoelectric Diversity and Specialization of Neuronal Cell Types in the Primate Striatum 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2026.02.26.708019v1?rss=1"
</link>
<description><![CDATA[
The basal ganglia are evolutionary ancient subcortical nuclei that form interconnected loops with the neocortex and limbic system to regulate movement, learning, habit formation, emotion, and motivation. Their dysfunction contributes to major neurological and psychiatric disorders, yet most cellular-level insights derive from rodent studies, leaving knowledge gaps in humans and translationally relevant primate species. To address this, we generated multi-modal Patch-seq data linking transcriptomic identity with morphological and electrophysiological properties in macaque striatum, the input nucleus of the basal ganglia. We found underappreciated diversity among medium spiny neurons, including non-canonical types, and variation aligned with functional gradients. Interneurons also exhibited spatial variation and even greater morphoelectric diversity, highlighting their functional modularity. Despite broad evolutionary conservation, we identified primate-specific features and key differences from rodent striatal neurons. By integrating molecular classification with cellular properties that shape network function, our findings provide insights into the functional organization of the primate striatum.
]]></description>
<dc:creator>Liu, X.-P.</dc:creator>
<dc:creator>Dalley, R.</dc:creator>
<dc:creator>Johansen, N.</dc:creator>
<dc:creator>Budzillo, A.</dc:creator>
<dc:creator>Thijssen, J.</dc:creator>
<dc:creator>Miller, J. A.</dc:creator>
<dc:creator>Walling-Bell, S.</dc:creator>
<dc:creator>Sawchuk, S.</dc:creator>
<dc:creator>Alfiler, L.</dc:creator>
<dc:creator>Andrade, J.</dc:creator>
<dc:creator>Ayala, A.</dc:creator>
<dc:creator>Barta, S.</dc:creator>
<dc:creator>Berry, K.</dc:creator>
<dc:creator>Bertagnolli, D.</dc:creator>
<dc:creator>Bhandiwad, A.</dc:creator>
<dc:creator>Bixby, M.</dc:creator>
<dc:creator>Blake, K.</dc:creator>
<dc:creator>Brouner, K.</dc:creator>
<dc:creator>Cardenas, T.</dc:creator>
<dc:creator>Casper, T.</dc:creator>
<dc:creator>Chakka, A. B.</dc:creator>
<dc:creator>Chartrand, T.</dc:creator>
<dc:creator>Daniel, S.</dc:creator>
<dc:creator>Donadio, N.</dc:creator>
<dc:creator>Dotson, N. I.</dc:creator>
<dc:creator>Egdorf, T.</dc:creator>
<dc:creator>Enstrom, R.</dc:creator>
<dc:creator>Fu, Y.</dc:creator>
<dc:creator>Gary, A.</dc:creator>
<dc:creator>Goldy, J.</dc:creator>
<dc:creator>Gorham, M.</dc:creator>
<dc:creator>Hadley, K.</dc:creator>
<dc:creator>Huang, A.</dc:creator>
<dc:creator>Hunker, A. C.</dc:creator>
<dc:creator>Jordan, A.</dc:creator>
<dc:creator>Juneau, Z. C.</dc:creator>
<dc:creator>Jungert, M.</dc:creator>
<dc:creator>Kannan, M.</dc:creator>
<dc:creator>Kapen, I.</dc:creator>
<dc:creator>Khem, S.</dc:creator>
<dc:creator>Koch, M.</dc:creator>
<dc:creator>Kocsis, K.</dc:creator>
<dc:creator>Kutsal, R.</dc:creator>
<dc:creator>Leon, G.</dc:creator>
<dc:creator>Mallory, M.</dc:creator>
<dc:creator>Malone, J.</dc:creator>
<dc:creator>McCutcheon, A.</dc:creator>
<dc:creator>McGr</dc:creator>
<dc:date>2026-02-26</dc:date>
<dc:identifier>doi:10.64898/2026.02.26.708019</dc:identifier>
<dc:title><![CDATA[Morphoelectric Diversity and Specialization of Neuronal Cell Types in the Primate Striatum]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2026-02-26</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.12.19.695583v1?rss=1">
<title>
<![CDATA[
Circuit specific specialization of human basal ganglia astrocytes 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.12.19.695583v1?rss=1"
</link>
<description><![CDATA[
Astrocytes shape synapses and circuits, yet human basal ganglia astrocyte diversity is incompletely defined. We built a multimodal atlas by integrating single-nucleus RNA-sequencing and chromatin accessibility with DNA methylation, 3D chromatin conformation, and spatial transcriptomics, then mapped basal ganglia programs onto a whole-brain reference. Astrocytes segregated into three anatomical subgroups spanning striatal gray matter, extra-striatal gray matter, and white matter, with subgroup-biased neurotransmitter transporters and synapse-associated programs consistent with differences in dominant afferent input. Within striatum, dorsal and ventral astrocyte populations aligned with distinct microcircuits and were conserved in nonhuman primates. A deep learning sequence model identified subgroup-associated enhancer code and, when benchmarked against published enhancer-AAV datasets, supported the design of candidate viral tools to target basal ganglia astrocyte programs in vivo. Together, these data define major axes of human astrocyte specialization and provide a framework for cell type-specific dissection of basal ganglia function.
]]></description>
<dc:creator>Fu, Y.</dc:creator>
<dc:creator>Johansen, N. J.</dc:creator>
<dc:creator>Kempynck, N.</dc:creator>
<dc:creator>Ding, W.</dc:creator>
<dc:creator>Turner, M. A.</dc:creator>
<dc:creator>Garcia, A. D.</dc:creator>
<dc:creator>Schmitz, M. T.</dc:creator>
<dc:creator>Close, J.</dc:creator>
<dc:creator>Kapen, I.</dc:creator>
<dc:creator>Hewitt, M.</dc:creator>
<dc:creator>Seeman, S. C.</dc:creator>
<dc:creator>Long, B.</dc:creator>
<dc:creator>Ding, S.-L.</dc:creator>
<dc:creator>Ho, W.</dc:creator>
<dc:creator>Mahoney, J.</dc:creator>
<dc:creator>Mich, J. K.</dc:creator>
<dc:creator>Levi, B. P.</dc:creator>
<dc:creator>Klein, A.</dc:creator>
<dc:creator>Behrens, M. M.</dc:creator>
<dc:creator>Ecker, J.</dc:creator>
<dc:creator>Aerts, S.</dc:creator>
<dc:creator>Hodge, R. D.</dc:creator>
<dc:creator>Lein, E. S.</dc:creator>
<dc:creator>Bakken, T. E.</dc:creator>
<dc:date>2025-12-22</dc:date>
<dc:identifier>doi:10.64898/2025.12.19.695583</dc:identifier>
<dc:title><![CDATA[Circuit specific specialization of human basal ganglia astrocytes]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-12-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2026.02.23.706695v1?rss=1">
<title>
<![CDATA[
A Cross-Species Enhancer-AAV Toolkit for Cell Type-Specific Targeting Across the Basal Ganglia 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2026.02.23.706695v1?rss=1"
</link>
<description><![CDATA[
The mammalian basal ganglia (BG) orchestrate motor, cognitive, and affective functions, yet cell type-specific genetic access remains limited, especially beyond rodents. Key structures implicated in movement and psychiatric disorders, including pallidum, subthalamic nucleus, and dopaminergic midbrain, lack scalable tools for cross-species targeting. Here, we present a comprehensive enhancer-AAV library enabling selective labeling and manipulation of major BG neuronal populations: striatal projection neuron subtypes, pallidal and subthalamic neurons, and midbrain dopaminergic and GABAergic populations. Using an evolutionarily informed discovery pipeline, we identified enhancers targeting canonical, non-canonical, and disease-relevant cell types, with validation demonstrating robust cross-species conservation of specificity between mouse and macaque. Computational modeling revealed sequence features predictive of in vivo performance, including motif grammar, chromatin accessibility, and evolutionary conservation, and identified distinct regulatory architectures across glial, projection, and interneuron lineages. This work establishes a comprehensive cross-species viral toolkit for the BG, unlocking previously inaccessible cell types for circuit dissection.
]]></description>
<dc:creator>Wirthlin, M. E.</dc:creator>
<dc:creator>Hunker, A. C.</dc:creator>
<dc:creator>Somasundaram, S.</dc:creator>
<dc:creator>Lerma, M. N.</dc:creator>
<dc:creator>Laird, W. D.</dc:creator>
<dc:creator>Omstead, V.</dc:creator>
<dc:creator>Taskin, N.</dc:creator>
<dc:creator>Kempynck, N.</dc:creator>
<dc:creator>Schmitz, M. T.</dc:creator>
<dc:creator>Gao, Y.</dc:creator>
<dc:creator>Thomas, E.</dc:creator>
<dc:creator>Hooper, M.</dc:creator>
<dc:creator>Ben-Simon, Y.</dc:creator>
<dc:creator>Martinez, R. A.</dc:creator>
<dc:creator>Opitz-Araya, X.</dc:creator>
<dc:creator>Mich, J. K.</dc:creator>
<dc:creator>Oster, A.</dc:creator>
<dc:creator>Dwivedi, D.</dc:creator>
<dc:creator>Groce, E.</dc:creator>
<dc:creator>Roth, J.</dc:creator>
<dc:creator>Thyagarajan, B.</dc:creator>
<dc:creator>Way, S.</dc:creator>
<dc:creator>Amaya, A.</dc:creator>
<dc:creator>Ayala, A.</dc:creator>
<dc:creator>Barta, S.</dc:creator>
<dc:creator>Bertagnolli, D.</dc:creator>
<dc:creator>Bixby, M.</dc:creator>
<dc:creator>Cardenas, T.</dc:creator>
<dc:creator>Casper, T.</dc:creator>
<dc:creator>Clark, M.</dc:creator>
<dc:creator>Donadio, N.</dc:creator>
<dc:creator>Dotson, N. I.</dc:creator>
<dc:creator>Egdorf, T.</dc:creator>
<dc:creator>Peterson, E. L.</dc:creator>
<dc:creator>Gloe, J.</dc:creator>
<dc:creator>Goldy, J.</dc:creator>
<dc:creator>Grasso, C.</dc:creator>
<dc:creator>Han, W.</dc:creator>
<dc:creator>Hastings, S. D.</dc:creator>
<dc:creator>Hewitt, M.</dc:creator>
<dc:creator>Hirschstein, D.</dc:creator>
<dc:creator>Ho, W.</dc:creator>
<dc:creator>Huang, A.</dc:creator>
<dc:creator>Johnson, T.</dc:creator>
<dc:creator>Jones, D.</dc:creator>
<dc:creator>Jordan, A.</dc:creator>
<dc:creator>Jun</dc:creator>
<dc:date>2026-02-24</dc:date>
<dc:identifier>doi:10.64898/2026.02.23.706695</dc:identifier>
<dc:title><![CDATA[A Cross-Species Enhancer-AAV Toolkit for Cell Type-Specific Targeting Across the Basal Ganglia]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2026-02-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2026.01.20.700641v1?rss=1">
<title>
<![CDATA[
Enhancer-based AAV approach for selective AADC delivery reduces motor symptoms and dyskinesia in Parkinson's mouse models 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2026.01.20.700641v1?rss=1"
</link>
<description><![CDATA[
Profound degeneration of dopamine (DA) neurons and reduced DA levels in the brain is recognized as an underlying cause of Parkinsons Disease (PD). The standard treatment for PD is levodopa (L-DOPA), but its effectiveness wanes over time and prolonged usage can lead to L-DOPA-induced-dyskinesia (LID). An adeno-associated virus (AAV)-based strategy to overexpress aromatic l-amino acid decarboxylase (AADC) in the striatum combined with L-DOPA therapy shows promise for symptomatic improvement but requires an invasive delivery approach. Here, we generated enhancer AAVs to drive AADC expression in key cell types and paired them with a blood-brain barrier (BBB)-penetrant capsid. We characterized the AAVs in mouse following multiple routes of administration and found that cell-type specific viral treatment ameliorated motor deficits and LID in PD disease models. This cell type-specific viral rescue strategy showed similar or better phenotypic rescue compared to a ubiquitous targeting approach and improved mortality. Additionally, we characterized the expression of an AAV-AADC vector capable of mouse phenotypic rescue in non-human primate (NHP) following two routes of administration. This novel therapeutic strategy in combination with L-DOPA may enable a less invasive and better tolerated approach to treat motor deficits in PD patients.
]]></description>
<dc:creator>Chowdhury, A.</dc:creator>
<dc:creator>Fraser, A.</dc:creator>
<dc:creator>Departee, M.</dc:creator>
<dc:creator>Taskin, N.</dc:creator>
<dc:creator>Quinlan, M. A.</dc:creator>
<dc:creator>Mich, J. K.</dc:creator>
<dc:creator>Omstead, V.</dc:creator>
<dc:creator>Lerma, N.</dc:creator>
<dc:creator>Opitz-Araya, X.</dc:creator>
<dc:creator>Hughes, A. C.</dc:creator>
<dc:creator>Kussick, E.</dc:creator>
<dc:creator>Martinez, R.</dc:creator>
<dc:creator>Reding, M.</dc:creator>
<dc:creator>Liang, E.</dc:creator>
<dc:creator>Shulga, L.</dc:creator>
<dc:creator>Rette, D.</dc:creator>
<dc:creator>Huang, C.</dc:creator>
<dc:creator>Casian, B.</dc:creator>
<dc:creator>Leibly, M.</dc:creator>
<dc:creator>Helback, O.</dc:creator>
<dc:creator>Barcelli, T.</dc:creator>
<dc:creator>Wood, T.</dc:creator>
<dc:creator>Uribe, N.</dc:creator>
<dc:creator>Bacon, C.</dc:creator>
<dc:creator>Bowlus, J.</dc:creator>
<dc:creator>Newman, D.</dc:creator>
<dc:creator>Kutsal, R.</dc:creator>
<dc:creator>Khem, S.</dc:creator>
<dc:creator>Donadio, N.</dc:creator>
<dc:creator>Yao, S.</dc:creator>
<dc:creator>Ronellenfitch, K.</dc:creator>
<dc:creator>Wright, V.</dc:creator>
<dc:creator>Gudsnuk, K.</dc:creator>
<dc:creator>Horwitz, G. D.</dc:creator>
<dc:creator>Levi, B. P.</dc:creator>
<dc:creator>Lein, E. S.</dc:creator>
<dc:creator>Ting, J. T.</dc:creator>
<dc:creator>Daigle, T. L.</dc:creator>
<dc:date>2026-01-21</dc:date>
<dc:identifier>doi:10.64898/2026.01.20.700641</dc:identifier>
<dc:title><![CDATA[Enhancer-based AAV approach for selective AADC delivery reduces motor symptoms and dyskinesia in Parkinson's mouse models]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2026-01-21</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2026.01.10.694705v1?rss=1">
<title>
<![CDATA[
The Caudate Nucleus Exhibits Distinct Pathology and Cell Type-Specific Responses Across Alzheimer's Disease 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2026.01.10.694705v1?rss=1"
</link>
<description><![CDATA[
A{beta} presence in the caudate nucleus (Ca) partially defines Thal stage III in Alzheimers disease (AD), but little is known about ADs cellular impact on the region. Leveraging a public basal ganglia taxonomy of cellular populations, we generated a cellular resolution atlas of AD-associated pathological changes in Ca. Unlike cortex, we found that Ca AD pathology is dominated by two key features: phosphorylated tau (pTau)-containing neuropil threads enriched near oligodendrocytes in white matter tracts and amyloid-{beta} diffuse plaques enriched in gray matter. Although AD pathology in affected cortical regions results in neuronal loss, we find no AD-driven reductions in neuron proportions in Ca. However, there were observable changes in multiple cellular populations. Protoplasmic astrocytes and FLT1+/IL1B+ microglia increased in abundance with global pTau levels. We also observe gene expression changes in fast-spiking PTHLH-PVALB interneurons indicative of disrupted signaling pathways and altered intrinsic physiological properties. This work provides a cellular-resolution framework for understanding AD pathology in Ca.
]]></description>
<dc:creator>Kana, O. Z.</dc:creator>
<dc:creator>Postupna, N.</dc:creator>
<dc:creator>Agrawal, A.</dc:creator>
<dc:creator>Long, B.</dc:creator>
<dc:creator>Travaglini, K.</dc:creator>
<dc:creator>Gabitto, M.</dc:creator>
<dc:creator>Lai, H.-Y.</dc:creator>
<dc:creator>Mahoney, J. T.</dc:creator>
<dc:creator>Xiao, M.</dc:creator>
<dc:creator>Bajwa, T.</dc:creator>
<dc:creator>Hulsey-Vincent, H.</dc:creator>
<dc:creator>Oyaizu, S. L.</dc:creator>
<dc:creator>Mena, G. E.</dc:creator>
<dc:creator>Hong, J.</dc:creator>
<dc:creator>Gelfand, E. C.</dc:creator>
<dc:creator>Kaplan, E. S.</dc:creator>
<dc:creator>Ariza, J.</dc:creator>
<dc:creator>Smith, K. A.</dc:creator>
<dc:creator>McMillen, D. A.</dc:creator>
<dc:creator>Chakka, A. B.</dc:creator>
<dc:creator>Goldy, J.</dc:creator>
<dc:creator>Melief, E. J.</dc:creator>
<dc:creator>Barta, S.</dc:creator>
<dc:creator>Oyama, A.</dc:creator>
<dc:creator>Ruiz, A.</dc:creator>
<dc:creator>Pom, C. A.</dc:creator>
<dc:creator>Ayala, A.</dc:creator>
<dc:creator>Bixby, M.</dc:creator>
<dc:creator>Huang, A.</dc:creator>
<dc:creator>Martin, N.</dc:creator>
<dc:creator>Cuevas, N. V.</dc:creator>
<dc:creator>Olsen, P.</dc:creator>
<dc:creator>Nagra, J.</dc:creator>
<dc:creator>Chakrabarty, R.</dc:creator>
<dc:creator>Tieu, M.</dc:creator>
<dc:creator>Cardenas, T.</dc:creator>
<dc:creator>Torkelson, A.</dc:creator>
<dc:creator>Bertagnolli, D.</dc:creator>
<dc:creator>Guzman, J.</dc:creator>
<dc:creator>Ferrer, R.</dc:creator>
<dc:creator>Waters, J.</dc:creator>
<dc:creator>Grabowski, T. J.</dc:creator>
<dc:creator>Crane, P. K.</dc:creator>
<dc:creator>Gatto, N. M.</dc:creator>
<dc:creator>Miller, J. A.</dc:creator>
<dc:creator>Hodg</dc:creator>
<dc:date>2026-01-10</dc:date>
<dc:identifier>doi:10.64898/2026.01.10.694705</dc:identifier>
<dc:title><![CDATA[The Caudate Nucleus Exhibits Distinct Pathology and Cell Type-Specific Responses Across Alzheimer's Disease]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2026-01-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.11.05.686845v1?rss=1">
<title>
<![CDATA[
Disruption of Cell-Type-Specific Molecular Programs of Medium Spiny Neurons in Autism 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.11.05.686845v1?rss=1"
</link>
<description><![CDATA[
Autism spectrum disorders (ASD) are highly heritable neurodevelopmental conditions with major contributions from rare genetic variants. Most studies have focused on cortical mechanisms; even growing evidence implicates subcortical circuits in ASD etiology. To systematically map developmental and molecular alterations beyond the cortex, we profiled lineage relationships across five brain regions in an ASD mouse model. Most prominent changes emerged in the striatum, a hub for learning and motor control. Furthermore, we performed single-nucleus multiomic profiling of human putamen from ASD and neurotypical donors revealed cell-type-specific transcriptomic and regulatory alterations. Differential expression converged on synaptic and energy metabolic dysfunctions in D1 striosome medium spiny neurons (MSNs), coupled with astrocytic remodeling of synaptic support. Gene regulatory network analysis identified EGR3 and EGR1 as key transcriptional regulators of ASD-associated programs of D1 MSNs. Together, these results establish the striatum as a central node of ASD convergence and provide a multiomic resource for dissecting its subcortical mechanisms.
]]></description>
<dc:creator>Yuan, G.</dc:creator>
<dc:creator>Suresh, V.</dc:creator>
<dc:creator>Wigdor, E.</dc:creator>
<dc:creator>Hao, Y.</dc:creator>
<dc:creator>Leonard, R.</dc:creator>
<dc:creator>Steyert, M.</dc:creator>
<dc:creator>Griffiths, M.</dc:creator>
<dc:creator>Evans, C.</dc:creator>
<dc:creator>Rohani, N.</dc:creator>
<dc:creator>Weiss, J.</dc:creator>
<dc:creator>Lassen, F. H.</dc:creator>
<dc:creator>Schafer, N.</dc:creator>
<dc:creator>Dong, S.</dc:creator>
<dc:creator>Palmer, D. S.</dc:creator>
<dc:creator>Sanders, S. J.</dc:creator>
<dc:creator>Nowakowski, T. J.</dc:creator>
<dc:date>2025-11-07</dc:date>
<dc:identifier>doi:10.1101/2025.11.05.686845</dc:identifier>
<dc:title><![CDATA[Disruption of Cell-Type-Specific Molecular Programs of Medium Spiny Neurons in Autism]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-11-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.12.31.697063v1?rss=1">
<title>
<![CDATA[
Progenitor Diversity and Architecture of the Human Ganglionic Eminences Shaping the Basal Ganglia 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.12.31.697063v1?rss=1"
</link>
<description><![CDATA[
The embryonic medial and lateral ganglionic eminences (MGE, LGE) are the principal sources of most neurons and glia for the basal ganglia. In primates, the MGE has a distinctive cytoarchitecture characterized by doublecortin enriched cellular nests (DENs), yet the architectonic organization underlying DEN formation, the molecular heterogeneity of ganglionic eminence progenitors and their lineage relationships, remain poorly understood. Here, using paired single-nucleus transcriptomics and chromatin accessibility profiling of the three GEs, we identify distinct progenitor populations, delineate their gene regulatory networks, and reconstruct their lineage trajectories. Live imaging reveals a unipolar outer radial glia-like population (GE-oRG) that undergoes mitotic somal translocation. Spatial transcriptomics identifies a distinct CRABP1+/ANGPT2+ domain within the MGE. Integrated spatial and electron microscopy demonstrates a periphery-to-center gradient of differentiation in the MGE. Leveraging DEN-forming MGE organoids derived from PCDH19 knockout human pluripotent stem cell lines, we identify the protocadherin, PCDH19, as a key regulator of DEN formation.
]]></description>
<dc:creator>Siebert, C. V.</dc:creator>
<dc:creator>Song, M.</dc:creator>
<dc:creator>Moriano, J. A.</dc:creator>
<dc:creator>Li, Z.</dc:creator>
<dc:creator>Silla, A. C.</dc:creator>
<dc:creator>Walker, M.</dc:creator>
<dc:creator>Chen, S.</dc:creator>
<dc:creator>Baltazar, J.</dc:creator>
<dc:creator>Oliveira, L. G. d.</dc:creator>
<dc:creator>Shankar, M.</dc:creator>
<dc:creator>Xie, Y.</dc:creator>
<dc:creator>Suraparaju, P.</dc:creator>
<dc:creator>Wang, S.</dc:creator>
<dc:creator>Bi, Q.</dc:creator>
<dc:creator>Xie, Y.</dc:creator>
<dc:creator>Ren, Y.</dc:creator>
<dc:creator>Garcia, M. T.</dc:creator>
<dc:creator>Wang, L.</dc:creator>
<dc:creator>Zuo, G.</dc:creator>
<dc:creator>Smidt, M. P.</dc:creator>
<dc:creator>Hoekman, M. F. M.</dc:creator>
<dc:creator>Harwell, C.</dc:creator>
<dc:creator>Parent, J.</dc:creator>
<dc:creator>Rubenstein, J.</dc:creator>
<dc:creator>Alvarez-Buylla, A.</dc:creator>
<dc:creator>Kriegstein, A.</dc:creator>
<dc:date>2026-01-02</dc:date>
<dc:identifier>doi:10.64898/2025.12.31.697063</dc:identifier>
<dc:title><![CDATA[Progenitor Diversity and Architecture of the Human Ganglionic Eminences Shaping the Basal Ganglia]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2026-01-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2026.01.28.702385v1?rss=1">
<title>
<![CDATA[
A Single-Cell and Spatial 3D Multi-omic Atlas of Developing Human Basal Ganglia and Inhibitory Neurons 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2026.01.28.702385v1?rss=1"
</link>
<description><![CDATA[
The human basal ganglia (BG), subcortical nuclei fundamental to motor regulation and cognitive modulation, is constructed from neurons produced during gestation in the adjacent ganglionic eminences (GEs). GEs are transient structures in the ventral prenatal brain that also generate GABAergic inhibitory neurons which migrate to destinations in the BG, cortex and other destinations. This study aims to elucidate the epigenomic and 3D-genomic dynamics involved in the specification and maturation of GEs and GE-derived neurons, using single-nucleus methyl-3C sequencing (snm3C-seq), highly-multiplexed spatial transcriptomics, and chromatin+RNA single-molecule imaging. Our multi-modal data support a heterogeneous temporal progression across GE subregions, with the lateral GE (LGE) showing declining neurogenic activity in mid-gestation and caudal GE (CGE) exhibiting ongoing developmental progression through infancy. We identified regulatory programs that specify subtypes of BG principal cells, medium spiny neurons (MSN), via synchronized maturation of the 3D-epigenome. In infant brains, we found a transient short-range enriched (SE) chromatin conformation during the transition between oligodendrocyte progenitors (OPCs) and oligodendrocytes (ODCs), and a temporary shift toward Long-range Enriched (LE) chromatin conformation in projection neurons, extending previous works showing the differentiation of neurons and glial cells is associated with permanent SE and LE conformation, respectively. Lastly, we found that gene regulatory regions active in MSNs were enriched in loci associated with genetic risk for neuropsychiatric disease. Our study delineates the highly complex, lineage-specific 3D genomic dynamics in ventral progenitors and basal ganglia populations of the perinatal human brain.

HighlightsO_LIJoint 3D genome and DNA methylome analysis of ventral brain progenitor zones
C_LIO_LIHeterogeneous developmental progressions of the ganglionic eminences
C_LIO_LIDistinct development dynamics and regulatory landscape of MSNs and interneurons
C_LIO_LITransient remodeling of the 3D-genome in neurons and oligodendrocyte progenitors
C_LI
]]></description>
<dc:creator>Heffel, M. G.</dc:creator>
<dc:creator>Xu, H.</dc:creator>
<dc:creator>Pastor-Alonso, O.</dc:creator>
<dc:creator>Li, X.</dc:creator>
<dc:creator>Baig, M. S.</dc:creator>
<dc:creator>Irfan Ghoor, R.</dc:creator>
<dc:creator>Li, R.</dc:creator>
<dc:creator>Kern, C.</dc:creator>
<dc:creator>Kum, J.</dc:creator>
<dc:creator>Zhang, Y.</dc:creator>
<dc:creator>Paino, J.</dc:creator>
<dc:creator>Tsai, M. J.</dc:creator>
<dc:creator>Tai, C.-Y.</dc:creator>
<dc:creator>Tucker, G.</dc:creator>
<dc:creator>Zhao, Z.</dc:creator>
<dc:creator>Hou, A.</dc:creator>
<dc:creator>von Behren, Z.</dc:creator>
<dc:creator>Bhade, M.</dc:creator>
<dc:creator>Li, S.</dc:creator>
<dc:creator>Sandoval, K.</dc:creator>
<dc:creator>Scholes, J.</dc:creator>
<dc:creator>Codrea, F.</dc:creator>
<dc:creator>Calimlim, J.</dc:creator>
<dc:creator>Liao, E. K.</dc:creator>
<dc:creator>Leung, G.</dc:creator>
<dc:creator>Kim, J.</dc:creator>
<dc:creator>Eskin, E.</dc:creator>
<dc:creator>Flint, J.</dc:creator>
<dc:creator>Cotter, J. A.</dc:creator>
<dc:creator>Pasaniuc, B.</dc:creator>
<dc:creator>Bintu, B.</dc:creator>
<dc:creator>Zhu, Q.</dc:creator>
<dc:creator>Mukamel, E. A.</dc:creator>
<dc:creator>Ernst, J.</dc:creator>
<dc:creator>Paredes, M. F.</dc:creator>
<dc:creator>Luo, C.</dc:creator>
<dc:date>2026-01-29</dc:date>
<dc:identifier>doi:10.64898/2026.01.28.702385</dc:identifier>
<dc:title><![CDATA[A Single-Cell and Spatial 3D Multi-omic Atlas of Developing Human Basal Ganglia and Inhibitory Neurons]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2026-01-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.12.23.696304v1?rss=1">
<title>
<![CDATA[
UC Irvine Brain Initiative Cell Atlas Network (BICAN) Brain Procurement Program for the Center for Multiomic Human Brain Cell Atlas Project 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.12.23.696304v1?rss=1"
</link>
<description><![CDATA[
High-quality neurotypical postmortem human brain tissue is essential but difficult to obtain for constructing comprehensive human brain cell atlases. Here we describe the establishment of UC Irvines Brain Procurement Program, a coordinated initiative to collect and process neurotypical donor brains for multiomic mapping studies within the NIH BRAIN Initiative Cell Atlas Network (BICAN) consortium. Through partnerships with the Orange County Coroners Office, the UC Irvine Willed Body Program, UCI Medical Center, and the Childrens Hospital of Orange County, we have developed standardized workflows encompassing donor identification, postmortem brain recovery and processing, region-of-interest dissection, neurotypical donor selection, and data management. Our experience demonstrates the feasibility of a community-based, multi-institutional procurement framework while highlighting challenges in recruiting neurotypical donors and ensuring demographic representation reflective of Southern California. We further identify opportunities to strengthen outreach and donation pathways. This program provides a scalable model for advancing population-reflective, high-quality human brain cell atlas efforts.

HighlightsO_LIEstablish a multi-site pipeline for procuring high-quality neurotypical human brains.
C_LIO_LIDemonstrate feasibility of donor collection across childhood to adulthood.
C_LIO_LIIdentify barriers and propose strategies to improve broad donor recruitment.
C_LI
]]></description>
<dc:creator>Xu, X.</dc:creator>
<dc:creator>Berry, J. V.</dc:creator>
<dc:creator>Tran, B. M.</dc:creator>
<dc:creator>Wu, G.</dc:creator>
<dc:creator>Tan, Z.</dc:creator>
<dc:creator>Mendoza, E.</dc:creator>
<dc:creator>Kim, M.</dc:creator>
<dc:creator>Arat, Z.</dc:creator>
<dc:creator>Lin, P. S. H.</dc:creator>
<dc:creator>Valenzuela, L.</dc:creator>
<dc:creator>Holmes, T. C.</dc:creator>
<dc:creator>Virovka, A.</dc:creator>
<dc:creator>Thaxton, M.</dc:creator>
<dc:creator>Nguyen, M.</dc:creator>
<dc:creator>Stein, S.</dc:creator>
<dc:creator>Sigaroudi, V. R.</dc:creator>
<dc:creator>De La Rosa, A.</dc:creator>
<dc:creator>Gawronski, B.</dc:creator>
<dc:creator>Silva, J.</dc:creator>
<dc:creator>Gonzalez, L.</dc:creator>
<dc:creator>Wright, S.</dc:creator>
<dc:creator>Wood, K.</dc:creator>
<dc:creator>Hui, G. K.</dc:creator>
<dc:creator>Anousaya, D.</dc:creator>
<dc:creator>Gangeddula, V. R.</dc:creator>
<dc:creator>Gama, N.</dc:creator>
<dc:creator>Gama, J.</dc:creator>
<dc:creator>Gross, T.</dc:creator>
<dc:creator>Bunney, B.</dc:creator>
<dc:creator>Stein, R.</dc:creator>
<dc:creator>Sequeira, P. A.</dc:creator>
<dc:creator>Mamdani, F.</dc:creator>
<dc:creator>Cartagena, P.</dc:creator>
<dc:creator>Bunney, W. E.</dc:creator>
<dc:creator>Lou, J.</dc:creator>
<dc:creator>Park, H. L.</dc:creator>
<dc:creator>Wilson, A.</dc:creator>
<dc:creator>Brooks, H. M.</dc:creator>
<dc:creator>Allhusen, V.</dc:creator>
<dc:creator>Crawford, J.</dc:creator>
<dc:creator>Knight, J. M.</dc:creator>
<dc:creator>Ren, B.</dc:creator>
<dc:creator>Ecker, J.</dc:creator>
<dc:creator>Behrens, M. M.</dc:creator>
<dc:creator>Keene, C. D.</dc:creator>
<dc:creator>Stark, C.</dc:creator>
<dc:creator>Head, E.</dc:creator>
<dc:creator>Yon</dc:creator>
<dc:date>2025-12-26</dc:date>
<dc:identifier>doi:10.64898/2025.12.23.696304</dc:identifier>
<dc:title><![CDATA[UC Irvine Brain Initiative Cell Atlas Network (BICAN) Brain Procurement Program for the Center for Multiomic Human Brain Cell Atlas Project]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-12-26</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.12.06.692733v1?rss=1">
<title>
<![CDATA[
Tissue-to-Bytes: A Catalytic Digital Twin Platform for Consortium-Scale Integration of Single-Cell Omics Data in BRAIN Initiative Cell Atlas Network 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.12.06.692733v1?rss=1"
</link>
<description><![CDATA[
The NIH BRAIN Initiative Cell Atlas Network (BICAN) is a collaborative effort among neuroscientists, computational biologists, and data scientists to create reference brain cell atlases to be used by the research community as a molecular and anatomical foundational framework for the study of human brain function and disorders. Coordinated sharing and management of the scarce human brain tissue, across lifespans and among the eleven participating BICAN centers and laboratories with anatomical precision, is a formidable undertaking. Creating and operating the necessary data production pipelines with de novo metadata standards prospectively established to adhere to the highest possible levels of Findable, Accessible, Interoperable, and Reusable (FAIR) data principles, present unparalleled challenges that place BICAN into uncharted territories. A digital twin paradigm has been formulated and operationalized to manage advanced single-cell molecular techniques and complexity of data production workflows that are used in the BICAN consortium. The Neuroanatomy-anchored Information Management Platform (NIMP) implements the digital twin paradigm from conception to design, to production. NIMP is an agile, extensible, catalytic infrastructure for integrative and collaborative BICAN consortium-scale FAIR data generation. Digital-twin thinking ensures that NIMP provides tissue-to-bytes, end-to-end integration of data production pipelines spanning brain banks, laboratories, sequencing centers, and data archives. NIMPs digital twin characteristics are manifested in the coupling of real-world workflows occurring in the BICAN laboratories with their digital counterparts moving progressively through verifiable provenance lineage paths. The implementation of NIMP digital twin features are grounded on advanced informatics strategies that include codification of anatomical contexts; standardized tissue request and sharing workflows, library preparation, sequencing, and single-cell omics data generation and deposition. These workflows and processes are governed by standardized Common Data Elements (CDEs); tracked by blockchain-inspired resource-identifiers (ID); ingested and edited using role-based access control (RBAC); and accessed by query-embedded dashboarding and atlas-enabled interactive resource exploration with dynamically rendered Sankey diagrams. In three years, NIMP has managed data and metadata for 762 human donors, 8,449 brain slabs, and 21,781 sequencing libraries across 11 laboratories, resulting in an estimated total of over 18 million cells for Basal Ganglia alone. The NIMP digital twin paradigm not only serves BICAN; it provides a blueprint for future-proof data coordination at consortium scale.
]]></description>
<dc:creator>Tao, S.</dc:creator>
<dc:creator>Li, X.</dc:creator>
<dc:creator>Huang, Y.</dc:creator>
<dc:creator>Abeysinghe, R.</dc:creator>
<dc:creator>Tong, L.</dc:creator>
<dc:creator>Lin, S.</dc:creator>
<dc:creator>Chou, W.-C.</dc:creator>
<dc:creator>Edgu-Fry, E.</dc:creator>
<dc:creator>Smith, K. A.</dc:creator>
<dc:creator>Ng, L.</dc:creator>
<dc:creator>Cui, L.</dc:creator>
<dc:creator>ZHANG, G.-Q.</dc:creator>
<dc:date>2025-12-09</dc:date>
<dc:identifier>doi:10.64898/2025.12.06.692733</dc:identifier>
<dc:title><![CDATA[Tissue-to-Bytes: A Catalytic Digital Twin Platform for Consortium-Scale Integration of Single-Cell Omics Data in BRAIN Initiative Cell Atlas Network]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-12-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2026.01.29.702575v1?rss=1">
<title>
<![CDATA[
An Integrated Single-Cell and Epigenomic Resource for Comparative Analysis of the Basal Ganglia 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2026.01.29.702575v1?rss=1"
</link>
<description><![CDATA[
The basal ganglia regulate motor, cognitive, and affective behaviors, and their dysfunction underlies diverse neurological and psychiatric disorders. Comprehensive, accessible multi-omics resources are needed to understand the regulatory mechanisms governing basal ganglia cell types. Here we present an open, interactive web-based platform for exploring single-cell multi-omics datasets from basal ganglia, generated using 10X Multiome, snm3C-seq, and Paired-Tag technologies from the BICAN (NIH BRAIN Initiative Cell Atlas Network) consortium. The platform is available at https://basalganglia.epigenomes.net/ and enables integrated visualization of gene expression, chromatin accessibility, DNA methylation, histone modifications, and chromatin conformation across cell types and human, macaque, marmoset, and mouse species, with direct genome browser support and comparative epigenomic functionality. Representative analyses demonstrate cell-type-specific regulatory landscapes, conserved and species-specific regulatory elements, and links between epigenomic regulation and transcription. This resource provides a scalable, community-oriented foundation for advancing basal ganglia biology and interpreting regulatory mechanisms relevant to brain function and disease.

HighlightsO_LIIntegrated single-cell epigenomic resource for basal ganglia
C_LIO_LIInteractive genome browser enables multi-omics and cross-species exploration
C_LIO_LIReveals cell-type-specific and species-specific regulatory landscapes
C_LIO_LISupports community access to complex brain epigenomic datasets
C_LI
]]></description>
<dc:creator>Zhang, W.</dc:creator>
<dc:creator>Ding, W.</dc:creator>
<dc:creator>Li, K.</dc:creator>
<dc:creator>Chang, L.</dc:creator>
<dc:creator>Klein, A.</dc:creator>
<dc:creator>Baez-Becerra, C. T.</dc:creator>
<dc:creator>Rink, J. A.</dc:creator>
<dc:creator>Bartlett, A.</dc:creator>
<dc:creator>Chen, H.</dc:creator>
<dc:creator>Schenker, N.</dc:creator>
<dc:creator>Johansen, N.</dc:creator>
<dc:creator>Mollenkopf, T.</dc:creator>
<dc:creator>Fu, Y.</dc:creator>
<dc:creator>Yang, X.</dc:creator>
<dc:creator>Liu, S.</dc:creator>
<dc:creator>Seng, C.</dc:creator>
<dc:creator>Miao, B.</dc:creator>
<dc:creator>Liu, T.</dc:creator>
<dc:creator>Zhu, Q.</dc:creator>
<dc:creator>Hodge, R. D.</dc:creator>
<dc:creator>Bakken, T. E.</dc:creator>
<dc:creator>Lein, E. S.</dc:creator>
<dc:creator>Hawrylycz, M.</dc:creator>
<dc:creator>Xu, X.</dc:creator>
<dc:creator>Behrens, M. M.</dc:creator>
<dc:creator>Ren, B.</dc:creator>
<dc:creator>Ecker, J. R.</dc:creator>
<dc:creator>Wang, T.</dc:creator>
<dc:creator>Li, D.</dc:creator>
<dc:date>2026-01-30</dc:date>
<dc:identifier>doi:10.64898/2026.01.29.702575</dc:identifier>
<dc:title><![CDATA[An Integrated Single-Cell and Epigenomic Resource for Comparative Analysis of the Basal Ganglia]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2026-01-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.11.29.691308v1?rss=1">
<title>
<![CDATA[
multiVIB: A unified probabilistic contrastive learning framework for atlas-scale integration of single-cell multi-omics data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.11.29.691308v1?rss=1"
</link>
<description><![CDATA[
Comprehensive brain cell atlases are essential for understanding neural functions and enabling translational insights. As single-cell technologies proliferate across experimental platforms, species, and modalities, these atlases must scale accordingly, calling for data integration framework that aligns heterogeneous datasets without erasing biologically meaningful variations. Existing tools typically address narrow integration settings, forcing researchers to assemble ad hoc workflows that may generate artifacts. Here, we introduce multiVIB, a unified probabilistic contrastive learning framework that handles diverse integration scenarios. We show that multiVIB achieves state-of-the-art performance while mitigating spurious alignments. Applied to atlas-scale datasets from the BRAIN Initiative, multiVIB demonstrates robust and scalable integration, including integration of diverse data modalities and reliable preservation of species-specific variations in cross-species integration. These capabilities position multiVIB as a scalable, biologically faithful foundation for constructing next-generation brain cell atlases with the growing landscape of single-cell data.
]]></description>
<dc:creator>Xu, Y.</dc:creator>
<dc:creator>Fleming, S. J.</dc:creator>
<dc:creator>Wang, B.</dc:creator>
<dc:creator>Schoenbeck, E. G.</dc:creator>
<dc:creator>Babadi, M.</dc:creator>
<dc:creator>Huo, B.-X.</dc:creator>
<dc:date>2025-12-01</dc:date>
<dc:identifier>doi:10.1101/2025.11.29.691308</dc:identifier>
<dc:title><![CDATA[multiVIB: A unified probabilistic contrastive learning framework for atlas-scale integration of single-cell multi-omics data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-12-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2026.02.04.703852v1?rss=1">
<title>
<![CDATA[
A consensus spinal cord cell type atlas across mouse, macaque, and human 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2026.02.04.703852v1?rss=1"
</link>
<description><![CDATA[
The spinal cord contains evolutionarily conserved cell types critical for motor function, sensory processing, and autonomic regulation, many of which are implicated in diverse neurological diseases and injuries. Yet the field lacks a comprehensive molecular characterization of cellular diversity in human, macaque, and mouse spinal cord. Here, we present a unified, cross-species cell type atlas based on the integration of single-nucleus gene expression, chromatin accessibility, and spatial transcriptomic data from segments within cervical, thoracic, lumbar, and sacral regions, including motor neurons (MNs) sampled across the entire rostro-caudal axis of the macaque spinal cord. Leveraging the spatial distributions of our molecularly defined cell types, we generated a cell type-guided anatomical map of spinal cord laminae and nuclei. We identified both conserved and species-specific cellular features, including gene expression patterns across distinct MN subtypes in the primate spinal cord. Cross-species cis-regulatory analysis and deep learning sequence models dissected the enhancer logic underlying viral targeting, uncovering conserved transcription factor grammar encoding cellular identity. Together, these results establish a unifying molecular and anatomical taxonomy of spinal cord cell types across species.
]]></description>
<dc:creator>Schmitz, M. T.</dc:creator>
<dc:creator>Johansen, N. J.</dc:creator>
<dc:creator>Kempynck, N.</dc:creator>
<dc:creator>Kapen, I.</dc:creator>
<dc:creator>Fu, Y.</dc:creator>
<dc:creator>Hewitt, M.</dc:creator>
<dc:creator>Seeman, S. C.</dc:creator>
<dc:creator>Kussick, E.</dc:creator>
<dc:creator>Gautier, O.</dc:creator>
<dc:creator>Leone, M. J.</dc:creator>
<dc:creator>Ding, S.-L.</dc:creator>
<dc:creator>Gao, Y.</dc:creator>
<dc:creator>Bhandiwad, A.</dc:creator>
<dc:creator>Ariza, J.</dc:creator>
<dc:creator>Ayala, A.</dc:creator>
<dc:creator>Barta, S.</dc:creator>
<dc:creator>Blum, J. A.</dc:creator>
<dc:creator>Cano-Gomez, L.</dc:creator>
<dc:creator>Cardenas, T.</dc:creator>
<dc:creator>Chakka, A. B.</dc:creator>
<dc:creator>Cuevas, N. V.</dc:creator>
<dc:creator>Donadio, N.</dc:creator>
<dc:creator>Fancher, K.</dc:creator>
<dc:creator>Ferrer, R.</dc:creator>
<dc:creator>Goldy, J.</dc:creator>
<dc:creator>Hastings, S. D.</dc:creator>
<dc:creator>Hirschstein, D.</dc:creator>
<dc:creator>Ho, W.</dc:creator>
<dc:creator>Huang, C.</dc:creator>
<dc:creator>Juneau, Z. C.</dc:creator>
<dc:creator>Kim, S. R.</dc:creator>
<dc:creator>Lewis, Z. R.</dc:creator>
<dc:creator>Liang, E.</dc:creator>
<dc:creator>Martin, N. X.</dc:creator>
<dc:creator>Nagra, J.</dc:creator>
<dc:creator>Newman, D.</dc:creator>
<dc:creator>Noh, M.-C.</dc:creator>
<dc:creator>Olsen, P.</dc:creator>
<dc:creator>Oyama, A.</dc:creator>
<dc:creator>Pena, N.</dc:creator>
<dc:creator>Poldsam, H.</dc:creator>
<dc:creator>Ray, P. L.</dc:creator>
<dc:creator>Reding, M.</dc:creator>
<dc:creator>Rimorin, C.</dc:creator>
<dc:creator>Ruiz, A.</dc:creator>
<dc:creator>Shapovalova, N. V.</dc:creator>
<dc:creator>Shulga,</dc:creator>
<dc:date>2026-02-05</dc:date>
<dc:identifier>doi:10.64898/2026.02.04.703852</dc:identifier>
<dc:title><![CDATA[A consensus spinal cord cell type atlas across mouse, macaque, and human]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2026-02-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2026.01.08.698257v1?rss=1">
<title>
<![CDATA[
Human Neocortical Glutamatergic Neurons Revealed Through Multimodal Profiling 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2026.01.08.698257v1?rss=1"
</link>
<description><![CDATA[
The human neocortex underlies higher cognition and is the engine of complex thought. Yet our understanding of its neuronal diversity is limited by sparse access to tissue, inconsistent sampling across studies, and a lack of multiple modality data. Although single-cell transcriptomic taxonomies are an important framework for characterizing cell type diversity, transcriptomic information alone cannot reveal the cellular properties that define neuronal computations. To address this, we performed Patch-seq, a method for collecting Morphology, Electrophysiology, and Transcriptomic data from a single neuron. We focused on glutamatergic, neocortical, excitatory neurons, the principal long-range projecting neurons of the cortex, and systematically integrated their morphoelectric features with transcriptomic identity. In combination with spatial transcriptomic data, we interrogated 39 of 42 transcriptomically-defined neuron types with a layer-centric perspective. Morphoelectric properties, such as cortical depth, apical dendrite structure, and excitability clearly distinguish transcriptomic subclasses and support many finer transcriptomic types. Morphoelectric properties are influenced by spatial location in supragranular layers, while deeper layers exhibit greater heterogeneity. Cross-species comparisons reveal conserved subclass organization but pronounced differences in apical dendrite arborization between mouse and human, and surprising similarities between human and macaque. Together, these datasets provide a unified multimodal reference that advances our understanding of human cortical circuitry and establishes a foundation for experimental and computational studies of human brain function and disease.
]]></description>
<dc:creator>Dalley, R.</dc:creator>
<dc:creator>Walling-Bell, S.</dc:creator>
<dc:creator>Miller, J. A.</dc:creator>
<dc:creator>Waleboer, F.</dc:creator>
<dc:creator>Thijssen, J.</dc:creator>
<dc:creator>McCutcheon, A.</dc:creator>
<dc:creator>Gelfand, E.</dc:creator>
<dc:creator>Mallory, M.</dc:creator>
<dc:creator>Radaelli, C.</dc:creator>
<dc:creator>Liu, X.-P.</dc:creator>
<dc:creator>Sawchuk, S.</dc:creator>
<dc:creator>Alfiler, L.</dc:creator>
<dc:creator>Andrade, J.</dc:creator>
<dc:creator>Ariza, J.</dc:creator>
<dc:creator>Ayala, A.</dc:creator>
<dc:creator>Baker, K.</dc:creator>
<dc:creator>Barkan, E.</dc:creator>
<dc:creator>Barta, S.</dc:creator>
<dc:creator>Berry, K.</dc:creator>
<dc:creator>Bertagnolli, D.</dc:creator>
<dc:creator>Bickley, K.</dc:creator>
<dc:creator>Bixby, M.</dc:creator>
<dc:creator>Blake, K.</dc:creator>
<dc:creator>Bomben, J.</dc:creator>
<dc:creator>Brouner, K.</dc:creator>
<dc:creator>Budzillo, A.</dc:creator>
<dc:creator>Campos, J.</dc:creator>
<dc:creator>Cardenas, T.</dc:creator>
<dc:creator>Carey, D.</dc:creator>
<dc:creator>Casper, T.</dc:creator>
<dc:creator>Chakka, A. B.</dc:creator>
<dc:creator>Clark, M.</dc:creator>
<dc:creator>Coopmans, T. V.</dc:creator>
<dc:creator>Crichton, K.</dc:creator>
<dc:creator>Dee, N.</dc:creator>
<dc:creator>Dotson, N. I.</dc:creator>
<dc:creator>Driessens, S. L. W.</dc:creator>
<dc:creator>Egdorf, T.</dc:creator>
<dc:creator>Ellingwood, L.</dc:creator>
<dc:creator>English, C.</dc:creator>
<dc:creator>Enstrom, R.</dc:creator>
<dc:creator>de Frates, R.</dc:creator>
<dc:creator>Galakhova, A. A.</dc:creator>
<dc:creator>Gary, A.</dc:creator>
<dc:creator>Gloe, J.</dc:creator>
<dc:creator>Gold</dc:creator>
<dc:date>2026-01-08</dc:date>
<dc:identifier>doi:10.64898/2026.01.08.698257</dc:identifier>
<dc:title><![CDATA[Human Neocortical Glutamatergic Neurons Revealed Through Multimodal Profiling]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2026-01-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.11.16.686778v1?rss=1">
<title>
<![CDATA[
Fast Optimization of Robust Transcriptomics Embeddings using Probabilistic Inference Autoencoder Networks for multi-Omics 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.11.16.686778v1?rss=1"
</link>
<description><![CDATA[
Advances in single-cell genomics technologies enable the routine acquisition of atlases with millions of cells. These datasets often include multiple covariates, such as donors, sequencing platforms, developmental timepoints, and species, which provide new opportunities for discovery and new challenges. To mitigate unwanted sources of variation, dataset integration is the starting point for most analyses. However, existing methods struggle with integrating large complex datasets. To address these limitations, we developed PIANO, a variational autoencoder framework that uses a negative binomial generalized linear model for stronger batch correction, and code compilation for ten times faster training than existing tools. We first demonstrate performant integration compared to commonly used methods on single-species datasets. We then show PIANO enables superior analyses of multiple atlases, solving challenging integration tasks across sequencing platforms, development, and species, while simultaneously preserving desired biological signals. Our contributions include a novel, high-performance integration method and recommendations for integration applications.
]]></description>
<dc:creator>Wang, N.</dc:creator>
<dc:creator>Turner, D.</dc:creator>
<dc:creator>Feinberg, H.</dc:creator>
<dc:creator>Nieto Caballero, V. E.</dc:creator>
<dc:creator>Yuan, D.</dc:creator>
<dc:creator>Scott, N.</dc:creator>
<dc:creator>Cardenas, C.</dc:creator>
<dc:creator>DeBerardine, M.</dc:creator>
<dc:creator>Dan, S.</dc:creator>
<dc:creator>Caceres, L.</dc:creator>
<dc:creator>Schembri, J.</dc:creator>
<dc:creator>Yao, Z.</dc:creator>
<dc:creator>Lee, C.</dc:creator>
<dc:creator>Pillow, J. W.</dc:creator>
<dc:creator>Krienen, F. M.</dc:creator>
<dc:date>2025-11-16</dc:date>
<dc:identifier>doi:10.1101/2025.11.16.686778</dc:identifier>
<dc:title><![CDATA[Fast Optimization of Robust Transcriptomics Embeddings using Probabilistic Inference Autoencoder Networks for multi-Omics]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-11-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.09.12.557406v1?rss=1">
<title>
<![CDATA[
A Meta-Atlas of the Developing Human Cortex Identifies Modules Driving Cell Subtype Specification 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.09.12.557406v1?rss=1"
</link>
<description><![CDATA[
Human brain development requires the generation of hundreds of diverse cell types, a process targeted by recent single-cell transcriptomic profiling efforts. Through a meta-analysis of seven of these published datasets, we have generated 225 meta-modules - gene co-expression networks that can describe mechanisms underlying cortical development. Several meta-modules have potential roles in both establishing and refining cortical cell type identities, and we validated their spatiotemporal expression in primary human cortical tissues. These include meta-module 20, associated with FEZF2+ deep layer neurons. Half of meta-module 20 genes are putative FEZF2 targets, including TSHZ3, a transcription factor associated with neurodevelopmental disorders. Human cortical organoid experiments validated that both factors are necessary for deep layer neuron specification. Importantly, subtle manipulations of these factors drive slight changes in meta-module activity that cascade into strong differences in cell fate - demonstrating how of our meta-atlas can engender further mechanistic analyses of cortical fate specification.
]]></description>
<dc:creator>Nano, P. R.</dc:creator>
<dc:creator>Fazzari, E.</dc:creator>
<dc:creator>Azizad, D.</dc:creator>
<dc:creator>Nguyen, C. V.</dc:creator>
<dc:creator>Wang, S.</dc:creator>
<dc:creator>Kan, R. L.</dc:creator>
<dc:creator>Wick, B.</dc:creator>
<dc:creator>Haeussler, M.</dc:creator>
<dc:creator>Bhaduri, A.</dc:creator>
<dc:date>2023-09-13</dc:date>
<dc:identifier>doi:10.1101/2023.09.12.557406</dc:identifier>
<dc:title><![CDATA[A Meta-Atlas of the Developing Human Cortex Identifies Modules Driving Cell Subtype Specification]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-09-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2026.03.04.709715v1?rss=1">
<title>
<![CDATA[
Mesoscale molecular architecture of the human striatum across cell types and lifespan 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2026.03.04.709715v1?rss=1"
</link>
<description><![CDATA[
The human striatum is a central hub for a diverse array of motor, cognitive, and affective behaviors, yet it lacks obvious cytoarchitectural boundaries that define functional territories. Here, we uncover a robust and molecularly defined mesoscale architecture in the human striatum. Using Slide-tags, a scalable single-nucleus spatial transcriptomics technology, we profiled 1.1 million cells across the full span of the anterior striatum of 19 postmortem donors, spatially mapping all striatal populations. Our data uncover a natural subdivision of the striatum into six zones, each defined by molecularly distinct populations of medium spiny neurons, and featuring spatially coordinated neuron-astrocyte signaling. Relative to MSNs in ventral zones, MSNs in dorsal zones exhibit higher expression of genes for synaptic remodeling and plasticity via ephrin and TGF-beta, while the ventral zone is defined by greater expression of semaphorin, protein chaperone, and hedgehog signaling pathways. By imputing zonal identities onto a larger single-nucleus RNA-seq cohort of 131 donors, we find that the dorsal zones exhibit greater age-related transcriptional changes, and that overall, the gene-expression differences that define spatial zonation patterns are attenuated with advancing age. This atlas provides a mesoscale molecular definition of human striatal anatomy, linking cell type identity to functional specialization and aging susceptibility.
]]></description>
<dc:creator>Kraft, A. W.</dc:creator>
<dc:creator>Lee, M.</dc:creator>
<dc:creator>Rayan, N.</dc:creator>
<dc:creator>Gao, H.</dc:creator>
<dc:creator>Milidantri, J.</dc:creator>
<dc:creator>Vanderburg, C.</dc:creator>
<dc:creator>Balderrama, K.</dc:creator>
<dc:creator>Nadaf, N.</dc:creator>
<dc:creator>Kumar, V.</dc:creator>
<dc:creator>Flowers, K.</dc:creator>
<dc:creator>Finn, E.</dc:creator>
<dc:creator>Shabet, M.</dc:creator>
<dc:creator>Muratoglu, E.</dc:creator>
<dc:creator>Yoo, O.</dc:creator>
<dc:creator>Shakir, K.</dc:creator>
<dc:creator>Nemesh, J.</dc:creator>
<dc:creator>Burger, S.</dc:creator>
<dc:creator>Drouin, S.</dc:creator>
<dc:creator>Catalini, O.</dc:creator>
<dc:creator>Raj, M.</dc:creator>
<dc:creator>Mohsin, A.</dc:creator>
<dc:creator>Budnik, N.</dc:creator>
<dc:creator>Reese, L.</dc:creator>
<dc:creator>McCarroll, S. A.</dc:creator>
<dc:creator>Ichihara, K.</dc:creator>
<dc:creator>Macosko, E. Z.</dc:creator>
<dc:date>2026-03-05</dc:date>
<dc:identifier>doi:10.64898/2026.03.04.709715</dc:identifier>
<dc:title><![CDATA[Mesoscale molecular architecture of the human striatum across cell types and lifespan]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2026-03-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2026.03.06.710160v1?rss=1">
<title>
<![CDATA[
MapMyCells: High-performance mapping of unlabeled cell-by-gene data to reference brain taxonomies 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2026.03.06.710160v1?rss=1"
</link>
<description><![CDATA[
Single-cell mapping methods convert raw, heterogeneous single-cell datasets into interpretable and comparable representations of biological identity. As reference cell-type taxonomies mature, mapping new datasets to shared references has become a central strategy for enabling cross-study integration, reproducible annotation, and cumulative biological knowledge. Here we present MapMyCells, an open-source framework designed to align diverse single-cell omics datasets to hierarchical reference taxonomies with minimal preprocessing. MapMyCells provides out-of-the-box support for an expanding set of high-quality brain cell-type references generated by the Allen Institute for Brain Science, the BRAIN Initiative, and the Seattle Alzheimers Disease Brain Cell Atlas, including whole-brain mouse and human atlases, aging and Alzheimers disease cohorts, and a cross-species consensus taxonomy initially focused on the basal ganglia. MapMyCells enables efficient mapping of hundreds of thousands of cells on standard workstations without specialized hardware, providing a deterministic, scalable, and modality-agnostic approach that is robust across species and molecular assays. The framework produces interpretable confidence metrics and quantitative summaries of mapping performance, allowing users to evaluate assignment precision and accuracy. We demonstrate the mapping of unlabeled transcriptomic, epigenomic, and spatial datasets to reference taxonomies and describe a general workflow for preparing arbitrary hierarchical taxonomies for reference-based mapping. As the ecosystem of single-cell reference atlases expands, MapMyCells offers a practical and reproducible solution for community-scale cell-type annotation and cross-dataset integration, supporting the development of unified and extensible brain cell atlases.
]]></description>
<dc:creator>Daniel, S. F.</dc:creator>
<dc:creator>Lee, C.</dc:creator>
<dc:creator>Mollenkopf, T.</dc:creator>
<dc:creator>Lee, M.</dc:creator>
<dc:creator>Arbuckle, J.</dc:creator>
<dc:creator>Fiabane, E.</dc:creator>
<dc:creator>Gabitto, M. I.</dc:creator>
<dc:creator>Johansen, N.</dc:creator>
<dc:creator>Kapen, I.</dc:creator>
<dc:creator>Kraft, A. W.</dc:creator>
<dc:creator>Lai, J.</dc:creator>
<dc:creator>Li, S. Y.</dc:creator>
<dc:creator>McGinty, R.</dc:creator>
<dc:creator>Miller, J. A.</dc:creator>
<dc:creator>Welch-Moosman, S.</dc:creator>
<dc:creator>Otto, S.</dc:creator>
<dc:creator>Sawyer, L.</dc:creator>
<dc:creator>Shepard, N.</dc:creator>
<dc:creator>Thompson, C. L.</dc:creator>
<dc:creator>Tjaernberg, A.</dc:creator>
<dc:creator>Waters, J.</dc:creator>
<dc:creator>Zhen, X.</dc:creator>
<dc:creator>Macosko, E.</dc:creator>
<dc:creator>Lein, E.</dc:creator>
<dc:creator>Ng, L.</dc:creator>
<dc:creator>Zeng, H.</dc:creator>
<dc:creator>Mufti, S.</dc:creator>
<dc:creator>Yao, Z.</dc:creator>
<dc:creator>Hawrylycz, M.</dc:creator>
<dc:date>2026-03-09</dc:date>
<dc:identifier>doi:10.64898/2026.03.06.710160</dc:identifier>
<dc:title><![CDATA[MapMyCells: High-performance mapping of unlabeled cell-by-gene data to reference brain taxonomies]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2026-03-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.11.25.568393v1?rss=1">
<title>
<![CDATA[
Connecting single neuron transcriptomes to the projectome in mouse visual cortex 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.11.25.568393v1?rss=1"
</link>
<description><![CDATA[
The mammalian brain is composed of diverse neuron types that play different functional roles. Recent single-cell RNA sequencing approaches have led to a whole brain taxonomy of transcriptomically-defined cell types, yet cell type definitions that include multiple cellular properties can offer additional insights into a neurons role in brain circuits. While the Patch-seq method can investigate how transcriptomic properties relate to the local morphological and electrophysiological properties of cell types, linking transcriptomic identities to long-range projections is a major unresolved challenge. To address this, we collected coordinated Patch-seq and whole brain morphology data sets of excitatory neurons in mouse visual cortex. From the Patch-seq data, we defined 16 integrated morphoelectric-transcriptomic (MET)-types; in parallel, we reconstructed the complete morphologies of 300 neurons. We unified the two data sets with a multi-step classifier, to integrate cell type assignments and interrogate cross-modality relationships. We find that transcriptomic variations within and across MET-types correspond with morphological and electrophysiological phenotypes. In addition, this variation, along with the anatomical location of the cell, can be used to predict the projection targets of individual neurons. We also shed new light on infragranular cell types and circuits, including cell-type-specific, interhemispheric projections. With this approach, we establish a comprehensive, integrated taxonomy of excitatory neuron types in mouse visual cortex and create a system for integrated, high-dimensional cell type classification that can be extended to the whole brain and potentially across species.
]]></description>
<dc:creator>Sorensen, S. A.</dc:creator>
<dc:creator>Gouwens, N. W.</dc:creator>
<dc:creator>Wang, Y.</dc:creator>
<dc:creator>Mallory, M.</dc:creator>
<dc:creator>Budzillo, A.</dc:creator>
<dc:creator>Dalley, R.</dc:creator>
<dc:creator>Lee, B. R.</dc:creator>
<dc:creator>Gliko, O.</dc:creator>
<dc:creator>Kuo, H.</dc:creator>
<dc:creator>Kuang, X.</dc:creator>
<dc:creator>Mann, R.</dc:creator>
<dc:creator>Ahmadinia, L.</dc:creator>
<dc:creator>Alfiler, L.</dc:creator>
<dc:creator>Baftizadeh, F.</dc:creator>
<dc:creator>Baker, K. S.</dc:creator>
<dc:creator>Bannick, S.</dc:creator>
<dc:creator>Bertagnolli, D.</dc:creator>
<dc:creator>Bickley, K.</dc:creator>
<dc:creator>Bohn, P.</dc:creator>
<dc:creator>Bomben, J.</dc:creator>
<dc:creator>Boyer, G.</dc:creator>
<dc:creator>Brouner, K.</dc:creator>
<dc:creator>Cahoon, A.</dc:creator>
<dc:creator>Chen, N.</dc:creator>
<dc:creator>Chen, C.</dc:creator>
<dc:creator>Chen, K.</dc:creator>
<dc:creator>Chvilicek, M.</dc:creator>
<dc:creator>Collman, F.</dc:creator>
<dc:creator>Daigle, T. L.</dc:creator>
<dc:creator>Dawes, T.</dc:creator>
<dc:creator>de Frates, R.</dc:creator>
<dc:creator>Dee, N.</dc:creator>
<dc:creator>DePartee, M.</dc:creator>
<dc:creator>Egdorf, T.</dc:creator>
<dc:creator>El-Hifnawi, L.</dc:creator>
<dc:creator>Esposito, L.</dc:creator>
<dc:creator>Farrell, C.</dc:creator>
<dc:creator>Gala, R.</dc:creator>
<dc:creator>Gamlin, C.</dc:creator>
<dc:creator>Gary, A.</dc:creator>
<dc:creator>Goldy, J.</dc:creator>
<dc:creator>Gu, H.</dc:creator>
<dc:creator>Hadley, K.</dc:creator>
<dc:creator>Hawrylycz, M.</dc:creator>
<dc:creator>Henry, A.</dc:creator>
<dc:creator>Hill, D.</dc:creator>
<dc:creator>Hirokawa, K. E.</dc:creator>
<dc:creator>Huang, Z.</dc:creator>
<dc:creator>Jo</dc:creator>
<dc:date>2023-11-27</dc:date>
<dc:identifier>doi:10.1101/2023.11.25.568393</dc:identifier>
<dc:title><![CDATA[Connecting single neuron transcriptomes to the projectome in mouse visual cortex]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-11-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2026.03.20.713160v1?rss=1">
<title>
<![CDATA[
Inter-individual variation of cellular and gene-expression properties of the human striatum 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2026.03.20.713160v1?rss=1"
</link>
<description><![CDATA[
The human brain varies from person to person in ways that shape behaviors and vulnerabilities, yet the cellular and molecular bases for inter-individual variation are largely unknown. Here we describe an analysis of cellular and gene-expression variation in four key structures of the striatum complex - the caudate, putamen, nucleus accumbens, and internal capsule - as well as the prefrontal cortex, from single-nucleus RNA-seq analysis of 3.9 million nuclei from 178 adult brain donors. We found that people with more astrocytes in any one brain region tended to have this property in all brain regions sampled; the same was true of striatal interneurons, microglia, and oligodendrocyte precursor cells (OPCs). OPCs showed attrition with age, declining in numbers by approximately 40% between age 30 and age 80 in both gray matter and white matter regions. We identified thousands of age-associated (but few sex-associated) variations in gene expression; the vast majority of these effects of age were cell-type-specific. Aging most strongly affected gene expression in projection neurons - especially striatal medium spiny neurons (MSNs/SPNs) - and had a much smaller effect on gene expression in interneurons. Individuals ages could be predicted to within about five years based on RNA-expression patterns from any of the striatal cell types. Common genetic variants detectably affected the expression levels of some ten thousand genes; the great majority of these effects were cell-type-specific. These data will provide a foundation for exploring natural inter-individual variation, aging, and tissue-based studies of human brain vulnerabilities.
]]></description>
<dc:creator>Burger, S.</dc:creator>
<dc:creator>Yoo, O.</dc:creator>
<dc:creator>Nemesh, J.</dc:creator>
<dc:creator>Muratoglu, E.</dc:creator>
<dc:creator>Vanderburg, C.</dc:creator>
<dc:creator>Yuan, J.</dc:creator>
<dc:creator>Shakir, K.</dc:creator>
<dc:creator>Mello, C. J.</dc:creator>
<dc:creator>Rayan, N. A.</dc:creator>
<dc:creator>Milidantri, J.</dc:creator>
<dc:creator>Kim, K.</dc:creator>
<dc:creator>Drouin, S.</dc:creator>
<dc:creator>Finn, E.</dc:creator>
<dc:creator>Gao, H.</dc:creator>
<dc:creator>Budnik, N.</dc:creator>
<dc:creator>Goldman, M.</dc:creator>
<dc:creator>Fritch, H.</dc:creator>
<dc:creator>Genovese, G.</dc:creator>
<dc:creator>Hogan, M.</dc:creator>
<dc:creator>Catalini, O.</dc:creator>
<dc:creator>Kashin, S.</dc:creator>
<dc:creator>Rockweiler, N.</dc:creator>
<dc:creator>Wysoker, A.</dc:creator>
<dc:creator>Macaisa, L.</dc:creator>
<dc:creator>Reese, L.</dc:creator>
<dc:creator>Flowers, K.</dc:creator>
<dc:creator>Kraft, A. W.</dc:creator>
<dc:creator>Fleming, S. J.</dc:creator>
<dc:creator>Coe, M.</dc:creator>
<dc:creator>Gunaratne, R.</dc:creator>
<dc:creator>Spina, L.</dc:creator>
<dc:creator>Crombie, C.</dc:creator>
<dc:creator>Mohsin, A.</dc:creator>
<dc:creator>Kamitaki, N.</dc:creator>
<dc:creator>Macosko, E. Z.</dc:creator>
<dc:creator>Ichihara, K.</dc:creator>
<dc:creator>McCarroll, S. A.</dc:creator>
<dc:date>2026-03-23</dc:date>
<dc:identifier>doi:10.64898/2026.03.20.713160</dc:identifier>
<dc:title><![CDATA[Inter-individual variation of cellular and gene-expression properties of the human striatum]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2026-03-23</prism:publicationDate>
<prism:section></prism:section>
</item>
</rdf:RDF>
