	<rdf:RDF xmlns:admin="http://webns.net/mvcb/" xmlns="http://purl.org/rss/1.0/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:prism="http://purl.org/rss/1.0/modules/prism/" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:syn="http://purl.org/rss/1.0/modules/syndication/">
	<channel rdf:about="https://biorxiv.org">
	<admin:errorReportsTo rdf:resource="mailto:biorxiv@cshlpress.edu"/>
	<title>bioRxiv Channel: Harvard Program in Therapeutic Sciences</title>
	<link>https://biorxiv.org</link>
	<description>
	This feed contains articles for bioRxiv Channel "Harvard Program in Therapeutic Sciences"
	</description>

		<items>
	<rdf:Seq>
		</rdf:Seq>
	</items>
	<prism:eIssn/>
	<prism:publicationName>bioRxiv</prism:publicationName>
	<prism:issn/>

	<image rdf:resource=""/>
	</channel>
	<image rdf:about="">
	<title>bioRxiv</title>
	<url/>
	<link>https://biorxiv.org</link>
	</image>
	<item rdf:about="https://biorxiv.org/cgi/content/short/358978v1?rss=1">
<title>
<![CDATA[
Cheminformatics tools for analyzing and designing optimized small molecule libraries 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/358978v1?rss=1"
</link>
<description><![CDATA[
Libraries of highly annotated small molecules have many uses in chemical genetics, drug discovery and drug repurposing. Many such libraries have become available, but few data-driven approaches exist to compare these libraries and design new ones. In this paper, we describe such an approach that makes use of data on binding selectivity, target coverage and induced cellular phenotypes as well as chemical structure and stage of clinical development. We implement the approach as R software and a Web-accessible tool (http://www.smallmoleculesuite.org) that uses incomplete and often confounded public data in combination with user preferences to score and create libraries. Analysis of six kinase inhibitor libraries using our approach reveals dramatic differences among them, leading us to design a new LSP-OptimalKinase library that outperforms all previous collections in terms of target coverage and compact size. We also assemble a mechanism of action library that optimally covers 1852 targets of the liganded genome. Using our tools, individual research groups and companies can quickly analyze private compound collections and public libraries can be progressively improved using the latest data.
]]></description>
<dc:creator>Moret, N.</dc:creator>
<dc:creator>Clark, N.</dc:creator>
<dc:creator>Hafner, M.</dc:creator>
<dc:creator>Wang, Y.</dc:creator>
<dc:creator>Lounkine, E.</dc:creator>
<dc:creator>Medvedovic, M.</dc:creator>
<dc:creator>Wang, J.</dc:creator>
<dc:creator>Gray, N.</dc:creator>
<dc:creator>Jenkins, J.</dc:creator>
<dc:creator>Sorger, P.</dc:creator>
<dc:date>2018-06-29</dc:date>
<dc:identifier>doi:10.1101/358978</dc:identifier>
<dc:title><![CDATA[Cheminformatics tools for analyzing and designing optimized small molecule libraries]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-06-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/109199v1?rss=1">
<title>
<![CDATA[
Maintaining the provenance of microscopy metadata using OMERO.forms software 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/109199v1?rss=1"
</link>
<description><![CDATA[
The creation of datasets that are findable, accessible, interoperable and reproducible (the FAIR standard) requires that data provenance be maintained1. Provenance is particularly important for microscopy data, whose interpretation is dependent on the biological context (e.g. cell state) and detection reagent (e.g. antibody.) This paper describes a new software tool, OMERO.forms, that extends the OMERO microscopy data management system2 to simplify and enhance metadata entry and provenance tracking.
]]></description>
<dc:creator>Russell, D. P.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2017-02-16</dc:date>
<dc:identifier>doi:10.1101/109199</dc:identifier>
<dc:title><![CDATA[Maintaining the provenance of microscopy metadata using OMERO.forms software]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-02-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/119834v1?rss=1">
<title>
<![CDATA[
From word models to executable models of signaling networks using automated assembly 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/119834v1?rss=1"
</link>
<description><![CDATA[
Word models (natural language descriptions of molecular mechanisms) are a common currency in spoken and written communication in biomedicine but are of limited use in predicting the behavior of complex biological networks. We present an approach to building computational models directly from natural language using automated assembly. Molecular mechanisms described in simple English are read by natural language processing algorithms, converted into an intermediate representation and assembled into executable or network models. We have implemented this approach in the Integrated Network and Dynamical Reasoning Assembler (INDRA), which draws on existing natural language processing systems as well as pathway information in Pathway Commons and other online resources. We demonstrate the use of INDRA and natural language to model three biological processes of increasing scope: (i) p53 dynamics in response to DNA damage; (ii) adaptive drug resistance in BRAF-V600E mutant melanomas; and (iii) the RAS signaling pathway. The use of natural language for modeling makes routine tasks more efficient for modeling practitioners and increases the accessibility and transparency of models for the broader biology community.nnStandfirst textINDRA uses natural language processing systems to read descriptions of molecular mechanisms and assembles them into executable models.nnHighlightsO_LIINDRA decouples the curation of knowledge as word models from model implementationnC_LIO_LIINDRA is connected to multiple natural language processing systems and can draw on information from curated databasesnC_LIO_LIINDRA can assemble dynamical models in rule-based and reaction network formalisms, as well as Boolean networks and visualization formatsnC_LIO_LIWe used INDRA to build models of p53 dynamics, resistance to targeted inhibitors of BRAF in melanoma, and the Ras signaling pathway from natural languagenC_LI
]]></description>
<dc:creator>Gyori, B. M.</dc:creator>
<dc:creator>Bachman, J. A.</dc:creator>
<dc:creator>Subramanian, K.</dc:creator>
<dc:creator>Muhlich, J. L.</dc:creator>
<dc:creator>Galescu, L.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2017-03-23</dc:date>
<dc:identifier>doi:10.1101/119834</dc:identifier>
<dc:title><![CDATA[From word models to executable models of signaling networks using automated assembly]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-03-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/151738v1?rss=1">
<title>
<![CDATA[
A simple open-source method for highly multiplexed imaging of single cells in tissues and tumours 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/151738v1?rss=1"
</link>
<description><![CDATA[
The architecture of normal and diseased tissues strongly influences the development and progression of disease as well as responsiveness and resistance to therapy. We describe a tissue-based cyclic immunofluorescence (t-CyCIF) method for highly multiplexed immuno-fluorescence imaging of formalin-fixed, paraffin-embedded (FFPE) specimens mounted on glass slides, the most widely used specimens for histopathological diagnosis of cancer and other diseases. t-CyCIF generates up to 60-plex images using an iterative process (a cycle) in which conventional low-plex fluorescence images are repeatedly collected from the same sample and then assembled into a high dimensional representation. t-CyCIF requires no specialized instruments or reagents and is compatible with super-resolution imaging; we demonstrate its application to quantifying signal transduction cascades, tumor antigens and immune markers in diverse tissues and tumors. The simplicity and adaptability of t-CyCIF makes it an effective method for pre-clinical and clinical research and a natural complement to single-cell genomics.
]]></description>
<dc:creator>Lin, J.-R.</dc:creator>
<dc:creator>Izar, B.</dc:creator>
<dc:creator>Mei, S.</dc:creator>
<dc:creator>Wang, S.</dc:creator>
<dc:creator>Shah, P.</dc:creator>
<dc:creator>Sorger, P.</dc:creator>
<dc:date>2017-06-19</dc:date>
<dc:identifier>doi:10.1101/151738</dc:identifier>
<dc:title><![CDATA[A simple open-source method for highly multiplexed imaging of single cells in tissues and tumours]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-06-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/211680v1?rss=1">
<title>
<![CDATA[
Therapeutically advantageous secondary targets of abemaciclib identified by multi-omics profiling of CDK4/6 inhibitors 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/211680v1?rss=1"
</link>
<description><![CDATA[
FDA approval of multiple drugs differing in chemical structures but targeting the same protein raises the question whether such drugs have sufficiently similar mechanisms of action to be considered functionally equivalent. In this paper we compare three recently approved inhibitors of the cyclin-dependent kinases CDK4/6 - palbociclib, ribociclib, and abemaciclib - that are becoming important therapies for the treatment of hormone-receptor positive breast and potentially other cancers. We find that transcriptional and proteomic changes induced by the three drugs differ significantly and that abemaciclib has unique cellular activities including induction of cell death (even in pRb-deficient cells), arrest in the G2 phase of the cell cycle, and reduced drug adaptation. These activities appear to arise from inhibition of kinases other than CDK4/6 including CDK2/Cyclin A/E and CDK1/Cyclin B.nnSIGNIFICANCEThe target profiles of most drugs are established relatively early in their development and are not systematically revisited at the time of approval. Scattered reports suggest that palbociclib, ribociclib, and abemaciclib differ in pharmacokinetics, dosing, and adverse effects but the three drugs are generally regarded as similar. Our finding that the drugs differ substantially in mechanism of action - abemaciclib retains activities of the earlier-generation drug alvocidib - suggests the potential for different uses in the clinic: in particular, abemaciclib may show activity in patients progressing on palbociclib or ribociclib. More generally, our approach relying on data from five distinct phenotypic and biochemical assays strongly suggests that a multi-faceted approach is necessary to get a reliable picture the target spectrum of kinase inhibitors.
]]></description>
<dc:creator>Hafner, M.</dc:creator>
<dc:creator>Mills, C. E.</dc:creator>
<dc:creator>Subramanian, K.</dc:creator>
<dc:creator>Chen, C.</dc:creator>
<dc:creator>Chung, M.</dc:creator>
<dc:creator>Boswell, S. A.</dc:creator>
<dc:creator>Everley, R. A.</dc:creator>
<dc:creator>Walmsley, C. S.</dc:creator>
<dc:creator>Juric, D.</dc:creator>
<dc:creator>Sorger, P.</dc:creator>
<dc:date>2017-11-07</dc:date>
<dc:identifier>doi:10.1101/211680</dc:identifier>
<dc:title><![CDATA[Therapeutically advantageous secondary targets of abemaciclib identified by multi-omics profiling of CDK4/6 inhibitors]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-11-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/213553v1?rss=1">
<title>
<![CDATA[
A multi-center study on factors influencing the reproducibility of in vitro drug-response studies 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/213553v1?rss=1"
</link>
<description><![CDATA[
Evidence that some influential biomedical results cannot be repeated has increased interest in practices that generate data meeting findable, accessible, interoperable and reproducible (FAIR) standards. Multiple papers have identified examples of irreproducibility, but practical steps for increasing reproducibility have not been widely studied. Here, seven research centers in the NIH LINCS Program Consortium investigate the reproducibility of a prototypical perturbational assay: quantifying the responsiveness of cultured cells to anti-cancer drugs. Such assays are important for drug development, studying cell biology, and patient stratification. While many experimental and computational factors have an impact on intra- and inter-center reproducibility, the factors most difficult to identify and correct are those with a strong dependency on biological context. These factors often vary in magnitude with the drug being analyzed and with growth conditions. We provide ways of identifying such context-sensitive factors, thereby advancing the conceptual and practical basis for greater experimental reproducibility.
]]></description>
<dc:creator>Niepel, M.</dc:creator>
<dc:creator>Hafner, M.</dc:creator>
<dc:creator>Williams, E. H.</dc:creator>
<dc:creator>Chung, M.</dc:creator>
<dc:creator>Barrette, A. M.</dc:creator>
<dc:creator>Stern, A. D.</dc:creator>
<dc:creator>Hu, B.</dc:creator>
<dc:creator>LINCS Consortium,</dc:creator>
<dc:creator>Gray, J. W.</dc:creator>
<dc:creator>Birtwistle, M. R.</dc:creator>
<dc:creator>Heiser, L. M.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2017-11-03</dc:date>
<dc:identifier>doi:10.1101/213553</dc:identifier>
<dc:title><![CDATA[A multi-center study on factors influencing the reproducibility of in vitro drug-response studies]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-11-03</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/225698v1?rss=1">
<title>
<![CDATA[
Bioentities: a resource for entity recognition and relationship resolution in biomedical text mining 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/225698v1?rss=1"
</link>
<description><![CDATA[
BackgroundFor automated reading of scientific publications to extract useful information about molecular mechanisms it is critical that genes, proteins and other entities be correctly associated with uniform identifiers, a process known as named entity linking or "grounding." Correct grounding is essential for resolving relationships among mined information, curated interaction databases, and biological datasets. The accuracy of this process is largely dependent on the availability of machine-readable resources associating synonyms and abbreviations commonly found in biomedical literature with uniform identifiers.nnResultsIn a task involving automated reading of [~]215,000 articles using the REACH event extraction software we found that grounding was disproportionately inaccurate for multi-protein families (e.g., "AKT") and complexes with multiple subunits (e.g."NF-{kappa}B"). To address this problem we constructed FamPlex, a manually curated resource defining protein families and complexes as they are commonly encountered in biomedical text. In FamPlex the gene-level constituents of families and complexes are defined in a flexible format allowing for multi-level, hierarchical membership. To create FamPlex, text strings corresponding to entities were identified empirically from literature and linked manually to uniform identifiers; these identifiers were also mapped to equivalent entries in multiple related databases. FamPlex also includes curated prefix and suffix patterns that improve named entity recognition and event extraction. Evaluation of REACH extractions on a test corpus of [~]54,000 articles showed that FamPlex significantly increased grounding accuracy for families and complexes (from 15% to 71%). The hierarchical organization of entities in FamPlex also made it possible to integrate otherwise unconnected mechanistic information across families, subfamilies, and individual proteins. Applications of FamPlex to the TRIPS/DRUM reading system and the Biocreative VI Bioentity Normalization Task dataset demonstrated the utility of FamPlex in other settings.nnConclusionFamPlex is an effective resource for improving named entity recognition, grounding, and relationship resolution in automated reading of biomedical text. The content in FamPlex is available in both tabular and Open Biomedical Ontology formats at https://github.com/sorgerlab/famplex under the Creative Commons CC0 license and has been integrated into the TRIPS/DRUM and REACH reading systems.
]]></description>
<dc:creator>Bachman, J. A.</dc:creator>
<dc:creator>Gyori, B. M.</dc:creator>
<dc:creator>Sorger, P.</dc:creator>
<dc:date>2017-11-27</dc:date>
<dc:identifier>doi:10.1101/225698</dc:identifier>
<dc:title><![CDATA[Bioentities: a resource for entity recognition and relationship resolution in biomedical text mining]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-11-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/248328v1?rss=1">
<title>
<![CDATA[
Genome-encoded Cytoplasmic Double-Stranded RNAs, Found in C9ORF72 ALS-FTD Brain, Provoke Propagated Neuronal Death 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/248328v1?rss=1"
</link>
<description><![CDATA[
Innate immune signaling activation and DNA damage are pathological hallmarks of aging that may herald multiple adult-onset neurodegenerative diseases. Here, we report that both cell autonomous and non-autonomous neuronal death are triggered by the production of cytoplasmic double-stranded RNA (cdsRNA) from a regulated, disarticulated transgene in the setting of type I interferon (IFN-I) signaling. CdsRNA is a pathogen associated molecular pattern that induces IFN-I in many cell types. Transfection of a dsRNA mimetic into cultured human neurons also induces IFN-I signaling and cell death in a dose-dependent manner. Direct relevance to human disease is found in neurons of ALS-FTD patients carrying C9ORF72 intronic hexanucleotide expansions; cdsRNA isolated from these tissues is comprised of repeat sequences. Together, these findings implicate cdsRNA generated from genomic sequences in neurons as a trigger for sterile, viral-mimetic IFN-I induction and propagated neuronal death within in a neural circuit in the aging nervous system.
]]></description>
<dc:creator>Rodriguez, S.</dc:creator>
<dc:creator>Schrank, B. R.</dc:creator>
<dc:creator>Sahin, A.</dc:creator>
<dc:creator>Al-Lawati, H.</dc:creator>
<dc:creator>Constantino, I.</dc:creator>
<dc:creator>Benz, E.</dc:creator>
<dc:creator>Fard, D.</dc:creator>
<dc:creator>Albers, A. D.</dc:creator>
<dc:creator>Cao, L.</dc:creator>
<dc:creator>Gomez, A. C.</dc:creator>
<dc:creator>Ratti, E.</dc:creator>
<dc:creator>Cudkowicz, M.</dc:creator>
<dc:creator>Frosch, M. P.</dc:creator>
<dc:creator>Talkowski, M.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Hyman, B. T.</dc:creator>
<dc:creator>Albers, M. W.</dc:creator>
<dc:date>2018-01-19</dc:date>
<dc:identifier>doi:10.1101/248328</dc:identifier>
<dc:title><![CDATA[Genome-encoded Cytoplasmic Double-Stranded RNAs, Found in C9ORF72 ALS-FTD Brain, Provoke Propagated Neuronal Death]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-01-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/365841v1?rss=1">
<title>
<![CDATA[
Drug adaptation influences cardiotoxicity caused by tyrosine kinase inhibitors in iPSC-derived human cardiomyocytes 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/365841v1?rss=1"
</link>
<description><![CDATA[
Cardiotoxicity induced by anti-cancer drugs is of increasing concern as the durability of therapeutic responses increases. The molecular basis of cardiotoxicity remains poorly understood, particularly when due to drug classes that do not inhibit the hERG potassium channel or cause the arrhythmias associated with long QT syndrome. This paper describes systematic molecular profiling of one such class of drugs, tyrosine kinase inhibitors (TKIs), which are widely used to treat solid tumors. Human cardiomyocytes differentiated from induced pluripotent stem cells (hiPSC-CMs) were exposed to one of four TKIs (Sunitinib, Sorafenib, Lapatinib and Erlotinib) observed to cause different levels of human cardiotoxicity and profiled by RNA sequencing (RNA-Seq) and mass spectroscopy-based proteomic analysis. We find that TKIs have diverse effects on hiPSC-CMs but genes involved in cardiac metabolism are particularly sensitive. In the case of Sorafenib, many genes involved in oxidative phosphorylation are down regulated resulting in a profound defect in mitochondrial metabolism. Cells adapt to this by upregulating aerobic glycolysis. Metabolic remodeling makes cells less acutely sensitive to Sorafenib and the effect is reversible upon drug withdrawal. Thus, the response of cardiomyocytes to Sorafenib is characterized by adaptive drug resistance previously described in tumor cells.
]]></description>
<dc:creator>Wang, H.</dc:creator>
<dc:creator>Sheehan, R. P.</dc:creator>
<dc:creator>Palmer, A. C.</dc:creator>
<dc:creator>Everley, R. A.</dc:creator>
<dc:creator>Boswell, S. A.</dc:creator>
<dc:creator>Ron-Harel, N.</dc:creator>
<dc:creator>Holton, K. M.</dc:creator>
<dc:creator>Jacobson, C. A.</dc:creator>
<dc:creator>Erickson, A.</dc:creator>
<dc:creator>Maliszewski, L.</dc:creator>
<dc:creator>Haigis, M. C.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2018-07-30</dc:date>
<dc:identifier>doi:10.1101/365841</dc:identifier>
<dc:title><![CDATA[Drug adaptation influences cardiotoxicity caused by tyrosine kinase inhibitors in iPSC-derived human cardiomyocytes]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-07-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/401620v1?rss=1">
<title>
<![CDATA[
Comparing the efficacy of cancer therapies between subgroups in basket trials 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/401620v1?rss=1"
</link>
<description><![CDATA[
An increase in the number of targeted anti-cancer drugs and growing genomic stratification of patients has led to the development of basket clinical trials in which a single drug is tested simultaneously in multiple tumor subtypes under a master protocol. Basket trials typically involve few patients per type, making it difficult to rigorously compare responses across types. We describe the use of permutation testing to analyze tumor volume changes and Progression Free Survival across subtypes in basket trials for neratinib, larotrectinib, pembrolizumab, and imatinib. Permutation testing is a complement to the standard Simons two-stage binomial approach and can test for differences among subgroups using empirical null distributions while controlling for multiple hypothesis testing. This approach uncovers examples of therapeutic benefit missed by a binomial test; in the case of the SUMMIT trial, our analysis identifies an overlooked opportunity for use of neratinib in lung cancers carrying ERBB2 Exon 20 mutations.
]]></description>
<dc:creator>Palmer, A. C.</dc:creator>
<dc:creator>Plana, D.</dc:creator>
<dc:creator>Sorger, P.</dc:creator>
<dc:date>2018-08-28</dc:date>
<dc:identifier>doi:10.1101/401620</dc:identifier>
<dc:title><![CDATA[Comparing the efficacy of cancer therapies between subgroups in basket trials]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-08-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/322446v1?rss=1">
<title>
<![CDATA[
Machine characterization of rat toxin responses identifies disease states, tolerance mechanisms and organ to whole-body communication 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/322446v1?rss=1"
</link>
<description><![CDATA[
BackgroundLiving organisms are constantly exposed to toxic xenobiotics and have therefore evolved protective responses. In mammals, the liver and kidney play central roles in protecting the organism from xenobiotics, and are at high risk of xenobiotic-induced injury. Liver and kidney damage by drugs and industrial toxins have been extensively studied from both classical histopathologic and biochemical perspectives.nnMethods and FindingsWe introduce a machine learning approach for the analysis of toxicological response. Unsupervised characterization of physiological and histological changes in a large toxicogenomic dataset revealed nine discrete toxin-induced disease states. Transcriptome analysis showed that some of the machine-identified disease states correspond to known pathology, and to known effects of certain toxin classes, but others were novel. Analysis of dynamics revealed transitions between disease states at constant toxin exposure, mostly in the direction of decreased pathology, which implies induction of tolerance. Tolerance correlated with induction of known xenobiotic defense genes and novel decreased ferroptosis sensitivity biomarkers. These data reinforce emerging evidence that ferroptosis drives organ pathology, and suggest that its downreagulation may promote tolerance and recovery. Lastly, mechanism of body weight decrease, a known primary marker for toxicity, was investigated. Combined analysis of food consumption, body weight, and molecular biomarkers indicated that organ disease states promote cachexia by whole-body signaling through Gdf15 and Igf1, suggesting strategies for therapeutic intervention that may be broadly relevant to human disease.nnConclusionsApplication of machine learning to systematic data collection of physiology, histopathology, transcriptome reveals multiple disease states, tolerance mechanisms and organ to whole-body communication.
]]></description>
<dc:creator>Shimada, K.</dc:creator>
<dc:creator>Mitchison, T.</dc:creator>
<dc:date>2018-05-28</dc:date>
<dc:identifier>doi:10.1101/322446</dc:identifier>
<dc:title><![CDATA[Machine characterization of rat toxin responses identifies disease states, tolerance mechanisms and organ to whole-body communication]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-05-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/261362v1?rss=1">
<title>
<![CDATA[
Mammalian Cells Engineered to Produce Novel Steroids 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/261362v1?rss=1"
</link>
<description><![CDATA[
Steroids can be difficult to modify via traditional organic synthesis methods, but many enzymes regio- and stereo-selectively process a wide variety of steroid substrates. We tested whether steroid-modifying enzymes could make novel steroids from non-native substrates. Numerous genes encoding steroid-modifying enzymes, including some bacterial enzymes, were expressed in mammalian cells by transient transfection and found to be active. We made three unusual steroids by expression in HEK293 cells of the 7-hydroxylase CYP7B1, which was selected because of high native product yield. These cells made 7,17-dihydroxypregnenolone and 7{beta},17-dihydroxypregnenolone from 17-hydroxypregnenolone, and produced 11,16-dihydroxyprogesterone from 16-hydroxyprogesterone. The latter two products resulted from previously unobserved CYP7B1 hydroxylation sites. A Rosetta docking model of CYP7B1 suggested that these substrates D-ring hydroxylations may prevent them from binding in the same way as the native substrate, bringing different carbons near the active ferryl oxygen. This new approach could use other enzymes and substrates to produce many novel steroids for drug candidate testing.
]]></description>
<dc:creator>Spady, E. S.</dc:creator>
<dc:creator>Wyche, T. P.</dc:creator>
<dc:creator>Rollins, N. J.</dc:creator>
<dc:creator>Clardy, J.</dc:creator>
<dc:creator>Way, J. C.</dc:creator>
<dc:creator>Silver, P. A.</dc:creator>
<dc:date>2018-02-07</dc:date>
<dc:identifier>doi:10.1101/261362</dc:identifier>
<dc:title><![CDATA[Mammalian Cells Engineered to Produce Novel Steroids]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-02-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/462184v1?rss=1">
<title>
<![CDATA[
Drugs in a curative combination therapy for lymphoma exhibit low cross-resistance but not pharmacological synergy 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/462184v1?rss=1"
</link>
<description><![CDATA[
Curative cancer therapies are uncommon and nearly always involve multi-drug combinations developed by experimentation in humans; unfortunately, the mechanistic basis for the success of such combinations has rarely been investigated in detail, obscuring lessons learned. Here we use isobologram analysis to score pharmacological interaction, and clone tracing and CRISPR screening to measure cross-resistance among the five drugs comprising R-CHOP, a combination therapy that frequently cures Diffuse Large B-Cell Lymphomas. We find that drugs in R-CHOP exhibit very low cross-resistance but not synergistic interaction; together they achieve a greater fractional kill according to the null hypothesis for both the Loewe dose-additivity model and the Bliss effect-independence model. These data provide direct evidence for the 50-year old hypothesis that a curative cancer therapy can be constructed on the basis of independently effective drugs having non-overlapping mechanisms of resistance, without synergistic interaction, which has immediate significance for the design of new drug combinations.
]]></description>
<dc:creator>Palmer, A. C.</dc:creator>
<dc:creator>Chidley, C.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2018-11-05</dc:date>
<dc:identifier>doi:10.1101/462184</dc:identifier>
<dc:title><![CDATA[Drugs in a curative combination therapy for lymphoma exhibit low cross-resistance but not pharmacological synergy]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-11-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/265231v1?rss=1">
<title>
<![CDATA[
End-to-end differentiable learning of protein structure 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/265231v1?rss=1"
</link>
<description><![CDATA[
Predicting protein structure from sequence is a central challenge of biochemistry. Co-evolution methods show promise, but an explicit sequence-to-structure map remains elusive. Advances in deep learning that replace complex, human-designed pipelines with differentiable models optimized end-to-end suggest the potential benefits of similarly reformulating structure prediction. Here we report the first end-to-end differentiable model of protein structure. The model couples local and global protein structure via geometric units that optimize global geometry without violating local covalent chemistry. We test our model using two challenging tasks: predicting novel folds without co-evolutionary data and predicting known folds without structural templates. In the first task the model achieves state-of-the-art accuracy and in the second it comes within 1-2[A]; competing methods using co-evolution and experimental templates have been refined over many years and it is likely that the differentiable approach has substantial room for further improvement, with applications ranging from drug discovery to protein design.
]]></description>
<dc:creator>AlQuraishi, M.</dc:creator>
<dc:date>2018-02-14</dc:date>
<dc:identifier>doi:10.1101/265231</dc:identifier>
<dc:title><![CDATA[End-to-end differentiable learning of protein structure]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-02-14</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/385450v1?rss=1">
<title>
<![CDATA[
pNeRF: Parallelized Conversion from Internal to Cartesian Coordinates 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/385450v1?rss=1"
</link>
<description><![CDATA[
The conversion of polymer parameterization from internal coordinates (bond lengths, angles, and torsions) to Cartesian coordinates is a fundamental task in molecular modeling, often performed using the Natural Extension Reference Frame (NeRF) algorithm. NeRF can be parallelized to process multiple polymers simultaneously, but is not parallelizable along the length of a single polymer. A mathematically equivalent algorithm, pNeRF, has been derived that is parallelizable along a polymers length. Empirical analysis demonstrates an order-of-magnitude speed up using modern GPUs and CPUs. In machine learning-based workflows, in which partial derivatives are backpropagated through NeRF equations and neural network primitives, switching to pNeRF can reduce the fractional computational cost of coordinate conversion from over two-thirds to around 10%. An optimized TensorFlow-based implementation of pNeRF is available on GitHub.
]]></description>
<dc:creator>AlQuraishi, M.</dc:creator>
<dc:date>2018-08-06</dc:date>
<dc:identifier>doi:10.1101/385450</dc:identifier>
<dc:title><![CDATA[pNeRF: Parallelized Conversion from Internal to Cartesian Coordinates]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-08-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/555854v1?rss=1">
<title>
<![CDATA[
Systemic Lymphoid Architecture Response Assessment (SYLARAS): An approach to multi-organ, discovery-based immunophenotyping implicates a role for CD45R/B220+ CD8T cells in glioblastoma immunology 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/555854v1?rss=1"
</link>
<description><![CDATA[
Accurately profiling systemic immune responses to cancer initiation and progression is necessary for understanding tumor surveillance and, ultimately, improving therapy. Here, we describe the SYLARAS software tool (SYstemic Lymphoid Architecture Response ASsessment) and a data set collected with SYLARAS that describes the frequencies of immune cells in primary and secondary lymphoid organs and in the tumor microenvironment of mice engrafted with a standard syngeneic glioblastoma (GBM) model. The data resource involves profiles of 5 lymphoid tissues in 48 mice and shows that GBM causes wide-spread changes in the local and systemic immune architecture. We perform in-depth analysis of one significant tumor-induced change: depletion of a specialized subset of CD45R/B220+ CD8+ T cells from the circulation and their accumulation in the tumor mass. Immunoprofiling of tissue microarrays demonstrates the presence of similar cells in human GBM.
]]></description>
<dc:creator>Baker, G. J.</dc:creator>
<dc:creator>Palaniappan, S. K.</dc:creator>
<dc:creator>Moore, J. K.</dc:creator>
<dc:creator>Davis, S. H.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2019-02-20</dc:date>
<dc:identifier>doi:10.1101/555854</dc:identifier>
<dc:title><![CDATA[Systemic Lymphoid Architecture Response Assessment (SYLARAS): An approach to multi-organ, discovery-based immunophenotyping implicates a role for CD45R/B220+ CD8T cells in glioblastoma immunology]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-02-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/700625v1?rss=1">
<title>
<![CDATA[
Combined inhibition of mTOR and PIKKs exploits replicative and checkpoint vulnerabilities to induce death of PI3K-activated triple-negative breast cancer cells 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/700625v1?rss=1"
</link>
<description><![CDATA[
SUMMARY SUMMARY HIGHLIGHTS INTRODUCTION RESULTS DISCUSSION FUNDING AUTHOR CONTRIBUTIONS STAR METHODS Cell lines METHOD DETAILS Western blotting Evaluation of drug sensitivity Immunofluorescence microscopy Primary antibodies and dilutions Single-stranded DNA ssDNA and... Live cell microscopy Extraction of polar metabolites Metabolomics profiling by... Isobolograms and analysis of... Cell cycle model and... QUANTIFICATION AND STATISTICAL... Viable cell counts ...
]]></description>
<dc:creator>Chopra, S. S.</dc:creator>
<dc:creator>Jenney, A.</dc:creator>
<dc:creator>Palmer, A.</dc:creator>
<dc:creator>Niepel, M.</dc:creator>
<dc:creator>Chung, M.</dc:creator>
<dc:creator>Mills, C.</dc:creator>
<dc:creator>Sivakumaren, S. C.</dc:creator>
<dc:creator>Liu, Q.</dc:creator>
<dc:creator>Chen, J.-Y.</dc:creator>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Asara, J.</dc:creator>
<dc:creator>Gray, N. S.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2019-07-12</dc:date>
<dc:identifier>doi:10.1101/700625</dc:identifier>
<dc:title><![CDATA[Combined inhibition of mTOR and PIKKs exploits replicative and checkpoint vulnerabilities to induce death of PI3K-activated triple-negative breast cancer cells]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-07-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/762294v1?rss=1">
<title>
<![CDATA[
Sporadic ERK pulses drive non-genetic resistance in drug-adapted BRAFV600E melanoma cells 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/762294v1?rss=1"
</link>
<description><![CDATA[
Anti-cancer drugs commonly target signal transduction proteins activated by mutation. In patients with BRAFV600E melanoma, small molecule RAF and MEK kinase inhibitors cause dramatic but often transient tumor regression. Emerging evidence suggests that cancer cells adapting by non-genetic mechanisms constitute a reservoir for the development of drug-resistant tumors. Here, we show that few hours after exposure to RAF/MEK inhibitors, BRAFV600E melanomas undergo adaptive changes involving disruption of negative feedback and sporadic pulsatile reactivation of the MAPK pathway, so that MAPK activity is transiently high enough in some cells to drive proliferation. Quantitative proteomics and computational modeling show that pulsatile MAPK reactivation is possible due to the co-existence in cells of two MAPK cascades: one driven by BRAFV600E that is drug-sensitive and a second driven by receptors that is drug-resistant. Paradoxically, this may account both for the frequent emergence of drug resistance and for the tolerability of RAF/MEK therapy in patients.
]]></description>
<dc:creator>Gerosa, L.</dc:creator>
<dc:creator>Chidley, C.</dc:creator>
<dc:creator>Froehlich, F.</dc:creator>
<dc:creator>Sanchez, G.</dc:creator>
<dc:creator>Lim, S. K.</dc:creator>
<dc:creator>Muhlich, J.</dc:creator>
<dc:creator>Chen, J.-Y.</dc:creator>
<dc:creator>Baker, G. J.</dc:creator>
<dc:creator>Schapiro, D.</dc:creator>
<dc:creator>Shi, T.</dc:creator>
<dc:creator>Yi, L.</dc:creator>
<dc:creator>Nicora, C. D.</dc:creator>
<dc:creator>Claas, A.</dc:creator>
<dc:creator>Lauffenburger, D. A.</dc:creator>
<dc:creator>Qian, W.-J.</dc:creator>
<dc:creator>Wiley, H. S.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2019-09-08</dc:date>
<dc:identifier>doi:10.1101/762294</dc:identifier>
<dc:title><![CDATA[Sporadic ERK pulses drive non-genetic resistance in drug-adapted BRAFV600E melanoma cells]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-09-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2019.12.13.874776v1?rss=1">
<title>
<![CDATA[
A tool for browsing the Cancer Dependency Map reveals functional connections between genes and helps predict the efficacy and selectivity of candidate cancer drugs 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2019.12.13.874776v1?rss=1"
</link>
<description><![CDATA[
Individual cancers rely on distinct essential genes for their survival. The Cancer Dependency Map (DepMap) is an ongoing project to uncover gene dependency in hundreds of cancer cell lines. DepMap is a powerful drug discovery tool, but can be challenging to use without professional bioinformatics assistance. We combined CRISPR and shRNA screening data from DepMap and built a non-programmer-friendly browser (https://labsyspharm.shinyapps.io/depmap) that reports, for each gene, the growth reduction that can be expected on loss of a gene or inhibition of its action (efficacy) and the selectivity of this effect across cell lines. Cluster analysis revealed proteins that work together in pathways or complexes. This tool can be used to 1) predict the efficacy and selectivity of candidate drugs; 2) identify targets for highly selective drugs; 3) identify maximally sensitive cell lines for testing a drug; 4) target hop, i.e., navigate from an undruggable protein with the desired selectively profile, such as an activated oncogene, to more druggable targets with a similar profile; and 5) identify novel pathways needed for cancer cell growth and survival.
]]></description>
<dc:creator>Shimada, K.</dc:creator>
<dc:creator>Muchlich, J. L.</dc:creator>
<dc:creator>Mitchison, T. J.</dc:creator>
<dc:date>2019-12-18</dc:date>
<dc:identifier>doi:10.1101/2019.12.13.874776</dc:identifier>
<dc:title><![CDATA[A tool for browsing the Cancer Dependency Map reveals functional connections between genes and helps predict the efficacy and selectivity of candidate cancer drugs]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-12-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.03.24.004085v1?rss=1">
<title>
<![CDATA[
Channel Embedding for Informative Protein Identification from Highly Multiplexed Images 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.03.24.004085v1?rss=1"
</link>
<description><![CDATA[
Interest is growing rapidly in using deep learning to classify biomedical images, and interpreting these deep-learned models is necessary for life-critical decisions and scientific discovery. Effective interpretation techniques accelerate biomarker discovery and provide new insights into the etiology, diagnosis, and treatment of disease. Most interpretation techniques aim to discover spatially-salient regions within images, but few techniques consider imagery with multiple channels of information. For instance, highly multiplexed tumor and tissue images have 30-100 channels and require interpretation methods that work across many channels to provide deep molecular insights. We propose a novel channel embedding method that extracts features from each channel. We then use these features to train a classifier for prediction. Using this channel embedding, we apply an interpretation method to rank the most discriminative channels. To validate our approach, we conduct an ablation study on a synthetic dataset. Moreover, we demonstrate that our method aligns with biological findings on highly multiplexed images of breast cancer cells while outperforming baseline pipelines.
]]></description>
<dc:creator>Magid, S. A.</dc:creator>
<dc:creator>Jang, W.-D.</dc:creator>
<dc:creator>Schapiro, D.</dc:creator>
<dc:creator>Wei, D.</dc:creator>
<dc:creator>Tompkin, J.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Pfister, H.</dc:creator>
<dc:date>2020-03-25</dc:date>
<dc:identifier>doi:10.1101/2020.03.24.004085</dc:identifier>
<dc:title><![CDATA[Channel Embedding for Informative Protein Identification from Highly Multiplexed Images]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-03-25</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.03.27.001834v1?rss=1">
<title>
<![CDATA[
Interpretative guides for interacting with tissue atlas and digital pathology data using the Minerva browser 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.03.27.001834v1?rss=1"
</link>
<description><![CDATA[
The recent development of highly multiplexed tissue imaging promises to substantially accelerate research into basic biology and human disease. Concurrently, histopathology in a clinical setting is undergoing a rapid transition to digital methods. Online tissue atlases involving highly multiplexed images of research and clinical specimens will soon join genomics as a systematic source of information on the molecular basis of disease and therapeutic response. However, even with recent advances in machine learning, experience with anatomic pathology shows that there is no immediate substitute for expert visual review, annotation, and description of tissue images. In this perspective we review the ecosystem of software available for analysis of tissue images and identify a need for interactive guides or "digital docents" that allow experts to help make complex images intelligible. We illustrate this idea using Minerva software and discuss how interactive image guides are being integrated into multi-omic browsers for effective dissemination of atlas data.
]]></description>
<dc:creator>Rashid, R.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Hoffer, J.</dc:creator>
<dc:creator>Muhlich, J. L.</dc:creator>
<dc:creator>Lin, J.-R.</dc:creator>
<dc:creator>Krueger, R.</dc:creator>
<dc:creator>Pfister, H.</dc:creator>
<dc:creator>Mitchell, R.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Sorger, P.</dc:creator>
<dc:date>2020-03-30</dc:date>
<dc:identifier>doi:10.1101/2020.03.27.001834</dc:identifier>
<dc:title><![CDATA[Interpretative guides for interacting with tissue atlas and digital pathology data using the Minerva browser]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-03-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.04.02.022277v1?rss=1">
<title>
<![CDATA[
Exploring the understudied human kinome for research and therapeutic opportunities 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.04.02.022277v1?rss=1"
</link>
<description><![CDATA[
The functions of protein kinases have been widely studied and over 60 kinase inhibitors are FDA-approved drugs. Membership in the human kinome is nonetheless subject to multiple overlapping and inconsistent definitions and is unevenly studied, complicating functional genomics and chemical genetics. We describe objective criteria for refining the definition of the human kinome to comprise an extended set of 710 kinase domains and a more narrowly curated set of 557 protein kinase like (PKL) domains. An online tool (www.kinome.org) makes it possible to sort these sets on multiple structural and functional criteria. Focusing on the least studied one-third of the kinome we find that many proteins are differentially expressed, essential in multiple cell lines, and mutated in the Cancer Genome Atlas. We show that some understudied kinases are high affinity off-targets of clinical-grade compounds and approved drugs and we describe an optimized small molecule library making use of this information for selective kinome perturbation. We conclude that the understudied kinome contains physiologically important proteins, including possible targets for future drug discovery campaigns.
]]></description>
<dc:creator>The NIH Understudied Kinome Consortium,</dc:creator>
<dc:creator>Moret, N.</dc:creator>
<dc:creator>Liu, C.</dc:creator>
<dc:creator>Gyori, B. M.</dc:creator>
<dc:creator>Bachman, J. A.</dc:creator>
<dc:creator>Steppi, A.</dc:creator>
<dc:creator>Taujale, R.</dc:creator>
<dc:creator>Huang, L.-C.</dc:creator>
<dc:creator>Hug, C.</dc:creator>
<dc:creator>Berginski, M.</dc:creator>
<dc:creator>Gomez, S. M.</dc:creator>
<dc:creator>Kannan, N.</dc:creator>
<dc:creator>Sorger, P.</dc:creator>
<dc:date>2020-04-02</dc:date>
<dc:identifier>doi:10.1101/2020.04.02.022277</dc:identifier>
<dc:title><![CDATA[Exploring the understudied human kinome for research and therapeutic opportunities]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-04-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.05.15.098749v1?rss=1">
<title>
<![CDATA[
Machine Learning Identifies Novel Candidates for DrugRepurposing in Alzheimer's Disease 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.05.15.098749v1?rss=1"
</link>
<description><![CDATA[
Clinical trials of novel therapeutics for Alzheimers Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. Repurposing can yield a useful therapeutic and also accelerate proof of concept studies that ultimately lead to a new molecular entity. We present a novel machine learning framework, DRIAD (Drug Repurposing In AD), that quantifies potential associations between the pathology of AD severity (the Braak stage) and molecular mechanisms as encoded in lists of gene names. DRIAD was validated on gene lists known to be associated with AD from other studies and subsequently applied to evaluate lists of genes arising from perturbations in differentiated human neural cell cultures by 80 FDA-approved and clinically tested drugs, producing a ranked list of possible repurposing candidates. Top-scoring drugs were inspected for common trends among their nominal molecular targets and their "off-targets", revealing a high prevalence of kinases from the Janus (JAK), Unc-51-like (ULK) and NIMA-related (NEK) families. These kinase families are known to modulate pathways related to innate immune signaling, autophagy, and microtubule formation and function, suggesting possible disease-modifying mechanisms of action. We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomarker(s), could be evaluated in a clinical trial.
]]></description>
<dc:creator>Rodriguez, S.</dc:creator>
<dc:creator>Hug, C.</dc:creator>
<dc:creator>Todorov, P.</dc:creator>
<dc:creator>Moret, N.</dc:creator>
<dc:creator>Boswell, S. A.</dc:creator>
<dc:creator>Evans, K.</dc:creator>
<dc:creator>Zhou, G.</dc:creator>
<dc:creator>Johnson, N. T.</dc:creator>
<dc:creator>Hyman, B.</dc:creator>
<dc:creator>Sorger, P.</dc:creator>
<dc:creator>Albers, M. W.</dc:creator>
<dc:creator>Sokolov, A.</dc:creator>
<dc:date>2020-05-16</dc:date>
<dc:identifier>doi:10.1101/2020.05.15.098749</dc:identifier>
<dc:title><![CDATA[Machine Learning Identifies Novel Candidates for DrugRepurposing in Alzheimer's Disease]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-05-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/822668v1?rss=1">
<title>
<![CDATA[
Assembling a corpus of phosphoproteomic annotations using ProtMapper to normalize site information from databases and text mining 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/822668v1?rss=1"
</link>
<description><![CDATA[
Protein phosphorylation regulates numerous cellular processes and is highly studied in biology.However, the analysis of phosphoproteomic datasets remains challenging due to limited information on upstream regulators of phosphosites, which is fragmented across multiple curated databases and unstructured literature. When aggregating information on phosphosites from six databases and three text mining systems, we found that a substantial proportion of phosphosites were mentioned at residue positions not matching the reference sequence. These errors were often attributable to the use of residue numbers from non-canonical protein isoforms, mouse or rat proteins, or post-translationally processed proteins. Non-canonical site numbering is also prevalent in mass spectrometry datasets from large-scale efforts such as the Clinical Proteomic Tumor Analysis Consortium (CPTAC). To address these issues, we developed ProtMapper, an open-source Python tool that automatically normalizes site positions to human protein reference sequences. We used ProtMapper coupled with the INDRA knowledge assembly system to create a corpus of 37,028 regulatory annotations for 16,332 sites - to our knowledge, the most comprehensive corpus of literature-derived information about phosphosite regulation currently available. This work highlights how automated phosphosite normalization coupled to text mining and knowledge assembly allows researchers to leverage phosphosite information that exists within the scientific literature.
]]></description>
<dc:creator>Bachman, J. A.</dc:creator>
<dc:creator>Gyori, B. M.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2019-10-29</dc:date>
<dc:identifier>doi:10.1101/822668</dc:identifier>
<dc:title><![CDATA[Assembling a corpus of phosphoproteomic annotations using ProtMapper to normalize site information from databases and text mining]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-10-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.03.18.431004v1?rss=1">
<title>
<![CDATA[
Micro-region transcriptomics of fixed human tissue using Pick-Seq 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.03.18.431004v1?rss=1"
</link>
<description><![CDATA[
Spatial transcriptomics and multiplexed imaging are complementary methods for studying tissue biology and disease. Recently developed spatial transcriptomic methods use fresh-frozen specimens but most diagnostic specimens, clinical trials, and tissue archives rely on formaldehyde-fixed tissue. Here we describe the Pick-Seq method for deep spatial transcriptional profiling of fixed tissue. Pick-Seq is a form of micro-region sequencing in which small regions of tissue, containing 5-20 cells, are mechanically isolated on a microscope and then sequenced. We demonstrate the use of Pick-Seq with several different fixed and frozen human specimens. Application of Pick-Seq to a human melanoma with complex histology reveals significant differences in transcriptional programs associated with tumor invasion, proliferation, and immuno-editing. Parallel imaging confirms changes in immuno-phenotypes and cancer cell states. This work demonstrates the ability of Pick-Seq to generate deep spatial transcriptomic data from fixed and archival tissue with multiplexed imaging in parallel.
]]></description>
<dc:creator>Maliga, Z.</dc:creator>
<dc:creator>Nirmal, A. J.</dc:creator>
<dc:creator>Ericson, N. G.</dc:creator>
<dc:creator>Boswell, S. A.</dc:creator>
<dc:creator>U'Ren, L.</dc:creator>
<dc:creator>Podyminogin, R.</dc:creator>
<dc:creator>Chow, J.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Chen, A. A.</dc:creator>
<dc:creator>Weinstock, D. M.</dc:creator>
<dc:creator>Lian, C. G.</dc:creator>
<dc:creator>Murphy, G. F.</dc:creator>
<dc:creator>Kaldjian, E. P.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2021-03-19</dc:date>
<dc:identifier>doi:10.1101/2021.03.18.431004</dc:identifier>
<dc:title><![CDATA[Micro-region transcriptomics of fixed human tissue using Pick-Seq]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-03-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.03.15.435473v1?rss=1">
<title>
<![CDATA[
MCMICRO: A scalable, modular image-processing pipeline for multiplexed tissue imaging 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.03.15.435473v1?rss=1"
</link>
<description><![CDATA[
Highly multiplexed tissue imaging makes molecular analysis of single cells possible in a preserved spatial context. However, reproducible analysis of the underlying data poses a substantial computational challenge. Here we describe a modular and open-source computational pipeline (MCMICRO) for performing the sequential steps needed to transform large, multi-channel whole slide images into single-cell data. We demonstrate use of MCMICRO on images of different tissues and tumors acquired using multiple imaging platforms, thereby providing a solid foundation for the continued development of tissue imaging software.
]]></description>
<dc:creator>Schapiro, D.</dc:creator>
<dc:creator>Sokolov, A.</dc:creator>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Muhlich, J. L.</dc:creator>
<dc:creator>Hess, J.</dc:creator>
<dc:creator>Lin, J.-R.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Nariya, M. K.</dc:creator>
<dc:creator>Baker, G. J.</dc:creator>
<dc:creator>Ruokonen, J.</dc:creator>
<dc:creator>Maliga, Z.</dc:creator>
<dc:creator>Jacobson, C. A.</dc:creator>
<dc:creator>Farhi, S. L.</dc:creator>
<dc:creator>Abbondanza, D.</dc:creator>
<dc:creator>McKinley, E. T.</dc:creator>
<dc:creator>Betts, C.</dc:creator>
<dc:creator>Regev, A.</dc:creator>
<dc:creator>Coffey, R. J.</dc:creator>
<dc:creator>Coussens, L. M.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2021-03-16</dc:date>
<dc:identifier>doi:10.1101/2021.03.15.435473</dc:identifier>
<dc:title><![CDATA[MCMICRO: A scalable, modular image-processing pipeline for multiplexed tissue imaging]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-03-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.12.15.422823v1?rss=1">
<title>
<![CDATA[
Proteomic profiling of breast cancer cell lines and models 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.12.15.422823v1?rss=1"
</link>
<description><![CDATA[
We performed quantitative proteomics on 61 human-derived breast cancer cell lines to a depth of ~13,000 proteins. The resulting high-throughput datasets were assessed for quality and reproducibility. We used the datasets to identify and characterize the subtypes of breast cancer and showed that they conform to known transcriptional subtypes, revealing that molecular subtypes are preserved even in under-sampled protein feature sets. All datasets are freely available as public resources on the LINCS portal. We anticipate that these datasets, either in isolation or in combination with complimentary measurements such as genomics, transcriptomics and phosphoproteomics, can be mined for the purpose of predicting drug response, informing cell line specific context in models of signalling pathways, and identifying markers of sensitivity or resistance to therapeutics.
]]></description>
<dc:creator>Kalocsay, M.</dc:creator>
<dc:creator>Berberich, M.</dc:creator>
<dc:creator>Everley, R.</dc:creator>
<dc:creator>Nariya, M.</dc:creator>
<dc:creator>Chung, M.</dc:creator>
<dc:creator>Gaudio, B.</dc:creator>
<dc:creator>Victor, C.</dc:creator>
<dc:creator>Bradshaw, G.</dc:creator>
<dc:creator>Hafner, M.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Mills, C.</dc:creator>
<dc:creator>Subramanian, K.</dc:creator>
<dc:date>2020-12-15</dc:date>
<dc:identifier>doi:10.1101/2020.12.15.422823</dc:identifier>
<dc:title><![CDATA[Proteomic profiling of breast cancer cell lines and models]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-12-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.10.14.339952v1?rss=1">
<title>
<![CDATA[
Multiplexed proteomics and imaging of resolving and lethal SARS-CoV-2 infection in the lung 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.10.14.339952v1?rss=1"
</link>
<description><![CDATA[
Normal tissue physiology and repair depends on communication with the immune system. Understanding this communication at the molecular level in intact tissue requires new methods. The consequences of SARS-CoV-2 infection, which can result in acute respiratory distress, thrombosis and death, has been studied primarily in accessible liquid specimens such as blood, sputum and bronchoalveolar lavage, all of which are peripheral to the primary site of infection in the lung. Here, we describe the combined use of multiplexed deep proteomics with multiplexed imaging to profile infection and its sequelae directly in fixed lung tissue specimens obtained from necropsy of infected animals and autopsy of human decedents. We characterize multiple steps in disease response from cytokine accumulation and protein phosphorylation to activation of receptors, changes in signaling pathways, and crosslinking of fibrin to form clots. Our data reveal significant differences between naturally resolving SARS-CoV-2 infection in rhesus macaques and lethal COVID-19 in humans. The approach we describe is broadly applicable to other tissues and diseases.

SummaryProteomics of infected tissue reveals differences in inflammatory and thrombotic responses between resolving and lethal COVID-19.
]]></description>
<dc:creator>Kalocsay, M.</dc:creator>
<dc:creator>Maliga, Z.</dc:creator>
<dc:creator>Nirmal, A. J.</dc:creator>
<dc:creator>Eisert, R. J.</dc:creator>
<dc:creator>Bradshaw, G. A.</dc:creator>
<dc:creator>Solomon, I. H.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Pelletier, R. J.</dc:creator>
<dc:creator>Jacobson, C. A.</dc:creator>
<dc:creator>Mintseris, J.</dc:creator>
<dc:creator>Padera, R. F.</dc:creator>
<dc:creator>Martinot, A. J.</dc:creator>
<dc:creator>Barouch, D. H.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Sorger, P.</dc:creator>
<dc:date>2020-10-15</dc:date>
<dc:identifier>doi:10.1101/2020.10.14.339952</dc:identifier>
<dc:title><![CDATA[Multiplexed proteomics and imaging of resolving and lethal SARS-CoV-2 infection in the lung]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-10-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/724302v1?rss=1">
<title>
<![CDATA[
Opposing immune and genetic forces shape oncogenic programs in synovial sarcoma 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/724302v1?rss=1"
</link>
<description><![CDATA[
Synovial sarcoma is an aggressive mesenchymal neoplasm, driven by the SS18-SSX fusion, and characterized by immunogenic antigens expression and exceptionally low T cell infiltration levels. To study the cancer-immune interplay in this disease, we profiled 16,872 cells from 12 human synovial sarcoma tumors using single-cell RNA-sequencing (scRNA-Seq). Synovial sarcoma manifests antitumor immunity, high cellular plasticity and a core oncogenic program, which is predictive of low immune levels and poor clinical outcomes. Using genetic and pharmacological perturbations, we demonstrate that the program is controlled by the SS18-SSX driver and repressed by cytokines secreted by macrophages and T cells in the tumor microenvironment. Network modeling predicted that SS18-SSX promotes the program through HDAC1 and CDK6. Indeed, the combination of HDAC and CDK4/6 inhibitors represses the program, induces immunogenic cell states, and selectively targets synovial sarcoma cells. Our study demonstrates that immune evasion, cellular plasticity, and cell cycle are co-regulated and can be co-targeted in synovial sarcoma and potentially in other malignancies.
]]></description>
<dc:creator>Jerby, L.</dc:creator>
<dc:creator>Neftel, C.</dc:creator>
<dc:creator>Shore, M. E.</dc:creator>
<dc:creator>McBride, M. J.</dc:creator>
<dc:creator>Haas, B.</dc:creator>
<dc:creator>Izar, B.</dc:creator>
<dc:creator>Weissman, H. R.</dc:creator>
<dc:creator>Volorio, A.</dc:creator>
<dc:creator>Boulay, G.</dc:creator>
<dc:creator>Cironi, L.</dc:creator>
<dc:creator>Richman, A. R.</dc:creator>
<dc:creator>Broye, L. C.</dc:creator>
<dc:creator>Gurski, J. M.</dc:creator>
<dc:creator>Luo, C. C.</dc:creator>
<dc:creator>Mylvaganam, R.</dc:creator>
<dc:creator>Nguyen, L.</dc:creator>
<dc:creator>Mei, S.</dc:creator>
<dc:creator>Melms, J. c.</dc:creator>
<dc:creator>Georgescu, C.</dc:creator>
<dc:creator>Cohen, O.</dc:creator>
<dc:creator>Buendia-Buendia, J. E.</dc:creator>
<dc:creator>Cuoco, M. S.</dc:creator>
<dc:creator>Labes, D.</dc:creator>
<dc:creator>Zollinger, D. R.</dc:creator>
<dc:creator>Beechem, J. M.</dc:creator>
<dc:creator>Nielsen, P.</dc:creator>
<dc:creator>Chebib, I.</dc:creator>
<dc:creator>Cote, G.</dc:creator>
<dc:creator>Choy, E.</dc:creator>
<dc:creator>Letovanec, I.</dc:creator>
<dc:creator>Cherix, S.</dc:creator>
<dc:creator>Wagle, N.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Haynes, A. B.</dc:creator>
<dc:creator>Mullen, J. T.</dc:creator>
<dc:creator>Stamenkovic, I.</dc:creator>
<dc:creator>Rivera, M. N.</dc:creator>
<dc:creator>Kadoch, C.</dc:creator>
<dc:creator>Rozenblatt-Rosen, O.</dc:creator>
<dc:creator>Suva, M. L.</dc:creator>
<dc:creator>Riggi, N.</dc:creator>
<dc:creator>Regev, A.</dc:creator>
<dc:date>2019-08-04</dc:date>
<dc:identifier>doi:10.1101/724302</dc:identifier>
<dc:title><![CDATA[Opposing immune and genetic forces shape oncogenic programs in synovial sarcoma]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-08-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.03.31.437984v1?rss=1">
<title>
<![CDATA[
Multiplexed 3D atlas of state transitions and immune interactions in colorectal cancer 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.03.31.437984v1?rss=1"
</link>
<description><![CDATA[
Advanced solid cancers are complex assemblies of tumor, immune, and stromal cells characterized by high intratumoral variation. We use highly multiplexed tissue imaging, 3D reconstruction, spatial statistics, and machine learning to identify cell types and states underlying morphological features of known diagnostic and prognostic significance in colorectal cancer. Quantitation of these features in high-plex marker space reveals recurrent transitions from one tumor morphology to the next, some of which are coincident with long-range gradients in the expression of oncogenes and epigenetic regulators. At the tumor invasive margin, where tumor, normal, and immune cells compete, T-cell suppression involves multiple cell types and 3D imaging shows that seemingly localized 2D features such as tertiary lymphoid structures are commonly interconnected and have graded molecular properties. Thus, while cancer genetics emphasizes the importance of discrete changes in tumor state, whole-specimen imaging reveals large-scale morphological and molecular gradients analogous to those in developing tissues.
]]></description>
<dc:creator>Lin, J.-R.</dc:creator>
<dc:creator>Wang, S.</dc:creator>
<dc:creator>Coy, S.</dc:creator>
<dc:creator>Tyler, M. A.</dc:creator>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Heiser, C. N.</dc:creator>
<dc:creator>Lau, K.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2021-04-02</dc:date>
<dc:identifier>doi:10.1101/2021.03.31.437984</dc:identifier>
<dc:title><![CDATA[Multiplexed 3D atlas of state transitions and immune interactions in colorectal cancer]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-04-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.04.02.438285v1?rss=1">
<title>
<![CDATA[
UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.04.02.438285v1?rss=1"
</link>
<description><![CDATA[
Newly developed technologies have made it feasible to routinely collect highly multiplexed (20-60 channel) images at subcellular resolution from human tissues for research and diagnostic purposes. Extracting single cell data from such images requires efficient and accurate image segmentation, a challenging problem that has recently benefited from the use of deep learning. In this paper, we demonstrate two approaches to improving tissue segmentation that are applicable to multiple deep learning frameworks. The first uses "real augmentations" that comprise defocused and saturated image data collected on the same instruments as the actual data; using real augmentation improves model accuracy to a significantly greater degree than computational augmentation (Gaussian blurring). The second involves imaging the nuclear envelope to better identify nuclear outlines. The two approaches cumulatively and substantially improve segmentation on a wide range of tissue types and provide a set of improved segmentation models. We speculate that the use of real augmentations may have applications in image processing outside of microscopy.
]]></description>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Novikov, E.</dc:creator>
<dc:creator>Jang, W.-D.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Cicconet, M.</dc:creator>
<dc:creator>Maliga, Z.</dc:creator>
<dc:creator>Jacobson, C. A.</dc:creator>
<dc:creator>Wei, D.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Pfister, H.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2021-04-04</dc:date>
<dc:identifier>doi:10.1101/2021.04.02.438285</dc:identifier>
<dc:title><![CDATA[UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-04-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.02.10.942748v1?rss=1">
<title>
<![CDATA[
Recombination and convergent evolution led to the emergence of 2019 Wuhan coronavirus 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.02.10.942748v1?rss=1"
</link>
<description><![CDATA[
The emergence of SARS-CoV-2 underscores the need to better understand the evolutionary processes that drive the emergence and adaptation of zoonotic viruses in humans. In the betacoronavirus genus, which also includes SARS-CoV and MERS-CoV, recombination frequently encompasses the Receptor Binding Domain (RBD) of the Spike protein, which, in turn, is responsible for viral binding to host cell receptors. Here, we find evidence of a recombination event in the RBD involving ancestral linages to both SARS-CoV and SARS-CoV-2. Although we cannot specify the recombinant nor the parental strains, likely due to the ancestry of the event and potential undersampling, our statistical analyses in the space of phylogenetic trees support such an ancestral recombination. Consequently, SARS-CoV and SARS-CoV-2 share an RBD sequence that includes two insertions (positions 432-436 and 460-472), as well as the variants 427N and 436Y. Both 427N and 436Y belong to a helix that interacts directly with the human ACE2 (hACE2) receptor. Reconstruction of ancestral states, combined with protein-binding affinity analyses using the physics-based trRosetta algorithm, reveal that the recombination event involving ancestral strains of SARS-CoV and SARS-CoV-2 led to an increased affinity for hACE2 binding, and that alleles 427N and 436Y significantly enhanced affinity as well. Structural modeling indicates that ancestors of SARS-CoV-2 may have acquired the ability to infect humans decades ago. The binding affinity with the human receptor was subsequently boosted in SARS-CoV and SARS-CoV-2 through further mutations in RBD. In sum, we report an ancestral recombination event affecting the RBD of both SARS-CoV and SARS-CoV-2 that was associated with an increased binding affinity to hACE2.

ImportanceThis paper addresses critical questions about the origin of the SARS-CoV-2 virus: what are the evolutionary mechanisms that led to the emergence of the virus, and how can we leverage such knowledge to assess the potential of SARS-like viruses to become pandemic strains? In this work, we demonstrate common mechanisms involved in the emergence of human-infecting SARS-like viruses: first, by acquiring a common haplotype in the RBD through recombination, and further, through increased specificity to the human ACE2 receptor through lineage specific mutations. We also show that the ancestors of SARS-CoV-2 already had the potential to infect humans at least a decade ago, suggesting that SARS-like viruses currently circulating in wild animal species constitute a source of potential pandemic re-emergence.
]]></description>
<dc:creator>Patino-Galindo, J. A.</dc:creator>
<dc:creator>Filip, I.</dc:creator>
<dc:creator>AlQuraishi, M.</dc:creator>
<dc:creator>Rabadan, R.</dc:creator>
<dc:date>2020-02-18</dc:date>
<dc:identifier>doi:10.1101/2020.02.10.942748</dc:identifier>
<dc:title><![CDATA[Recombination and convergent evolution led to the emergence of 2019 Wuhan coronavirus]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-02-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.12.10.420463v1?rss=1">
<title>
<![CDATA[
Three-dimensional spatial transcriptomics uncovers cell type dynamics in the rheumatoid arthritis synovium 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.12.10.420463v1?rss=1"
</link>
<description><![CDATA[
The inflamed rheumatic joint is a highly heterogeneous and complex tissue with dynamic recruitment and expansion of multiple cell types that interact in multifaceted ways within a localized area. Rheumatoid arthritis synovium has primarily been studied either by immunostaining or by molecular profiling after tissue homogenization. Here, we use Spatial Transcriptomics to study local cellular interactions at the site of chronic synovial inflammation. We report comprehensive spatial RNA-seq data coupled to quantitative and cell type-specific chemokine-driven dynamics at and around organized structures of infiltrating leukocyte cells in the synovium.
]]></description>
<dc:creator>Vickovic, S.</dc:creator>
<dc:creator>Schapiro, D.</dc:creator>
<dc:creator>Carlberg, K.</dc:creator>
<dc:creator>Loetstedt, B.</dc:creator>
<dc:creator>Larsson, L.</dc:creator>
<dc:creator>Korotkova, M.</dc:creator>
<dc:creator>Hensvold, A. H.</dc:creator>
<dc:creator>Catrina, A. I.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Malmstroem, V.</dc:creator>
<dc:creator>Regev, A.</dc:creator>
<dc:creator>Stahl, P. L.</dc:creator>
<dc:date>2020-12-11</dc:date>
<dc:identifier>doi:10.1101/2020.12.10.420463</dc:identifier>
<dc:title><![CDATA[Three-dimensional spatial transcriptomics uncovers cell type dynamics in the rheumatoid arthritis synovium]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-12-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.01.08.898734v1?rss=1">
<title>
<![CDATA[
Biomarker-guided treatment strategies for ovarian cancer identified from a heterogeneous panel of patient-derived tumor xenografts 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.01.08.898734v1?rss=1"
</link>
<description><![CDATA[
Advanced ovarian cancers are a leading cause of cancer-related death in women. Such cancers are currently treated with surgery and chemotherapy which is often temporarily successful but exhibits a high rate of relapse after which treatment options are few. Here we assess the responses of a panel of patient-derived ovarian cancer xenografts (PDXs) to 19 mono and combination therapies, including small molecules and antibody-drug conjugates. The PDX panel aimed to mimic the heterogeneity of disease observed in patients, and exhibited a distribution of responsiveness to standard of care chemotherapy similar to human clinical data. Three monotherapies and one drug combination were found to be active in different subsets of PDXs. By analyzing gene expression data we identified gene expression biomarkers predictive of responsiveness to each of three novel targeted therapy regimens. While no single treatment had as high a response rate as chemotherapy, nearly 90% of PDXs were eligible for and responded to at least one biomarker-guided treatment, including tumors resistant to standard chemotherapy. Biomarker frequency was similar in human patients, suggesting the possibility of a new therapeutic approach to ovarian cancer and demonstrating the potential power of PDX-based trials in broadening the reach of precision cancer medicine.
]]></description>
<dc:creator>Palmer, A. C.</dc:creator>
<dc:creator>Plana, D.</dc:creator>
<dc:creator>Gao, H.</dc:creator>
<dc:creator>Korn, J. M.</dc:creator>
<dc:creator>Yang, G.</dc:creator>
<dc:creator>Green, J.</dc:creator>
<dc:creator>Zhang, X.</dc:creator>
<dc:creator>Velazquez, R.</dc:creator>
<dc:creator>McLaughlin, M. E.</dc:creator>
<dc:creator>Ruddy, D. A.</dc:creator>
<dc:creator>Kowal, C.</dc:creator>
<dc:creator>Goldovitz, J.</dc:creator>
<dc:creator>Bullock, C.</dc:creator>
<dc:creator>Rivera, S.</dc:creator>
<dc:creator>Rakiec, D.</dc:creator>
<dc:creator>Elliott, G.</dc:creator>
<dc:creator>Fordjour, P.</dc:creator>
<dc:creator>Meyer, R.</dc:creator>
<dc:creator>Loo, A.</dc:creator>
<dc:creator>Kurth, E.</dc:creator>
<dc:creator>Engelman, J. A.</dc:creator>
<dc:creator>Bitter, H.</dc:creator>
<dc:creator>Sellers, W. R.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Williams, J. A.</dc:creator>
<dc:date>2020-01-13</dc:date>
<dc:identifier>doi:10.1101/2020.01.08.898734</dc:identifier>
<dc:title><![CDATA[Biomarker-guided treatment strategies for ovarian cancer identified from a heterogeneous panel of patient-derived tumor xenografts]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-01-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/755579v1?rss=1">
<title>
<![CDATA[
GeneWalk identifies relevant gene functions for a biological context using network representation learning 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/755579v1?rss=1"
</link>
<description><![CDATA[
The primary bottleneck in high-throughput genomics experiments is identifying the most important genes and their relevant functions from a list of gene hits. Existing methods such as Gene Ontology (GO) enrichment analysis provide insight at the gene set level. For individual genes, GO annotations are static and biological context can only be added by manual literature searches. Here, we introduce GeneWalk (github.com/churchmanlab/genewalk), a method that identifies individual genes and their relevant functions under a particular experimental condition. After automatic assembly of an experiment-specific gene regulatory network, GeneWalk quantifies the similarity between vector representations of each gene and its GO annotations through representation learning, yielding annotation significance scores that reflect their functional relevance for the experimental context. We demonstrate the use of GeneWalk analysis of RNA-seq and nascent transcriptome (NET-seq) data from human cells and mouse brains, validating the methodology. By performing gene- and condition-specific functional analysis that converts a list of genes into data-driven hypotheses, GeneWalk accelerates the interpretation of high-throughput genetics experiments.
]]></description>
<dc:creator>Ietswaart, R.</dc:creator>
<dc:creator>Gyori, B. M.</dc:creator>
<dc:creator>Bachman, J. A.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Churchman, L. S.</dc:creator>
<dc:date>2019-09-05</dc:date>
<dc:identifier>doi:10.1101/755579</dc:identifier>
<dc:title><![CDATA[GeneWalk identifies relevant gene functions for a biological context using network representation learning]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-09-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/731018v1?rss=1">
<title>
<![CDATA[
Inferring reaction network structure from single-cell, multiplex data, using toric systems theory 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/731018v1?rss=1"
</link>
<description><![CDATA[
The goal of many single-cell studies on eukaryotic cells is to gain insight into the biochemical reactions that control cell fate and state. In this paper we introduce the concept of effective stoichiometric space (ESS) to guide the reconstruction of biochemical networks from multiplexed, fixed time-point, single-cell data. In contrast to methods based solely on statistical models of data, the ESS method leverages the power of the geometric theory of toric varieties to begin unraveling the structure of chemical reaction networks (CRN). This application of toric theory enables a data-driven mapping of covariance relationships in single cell measurements into stoichiometric information, one in which each cell subpopulation has its associated ESS interpreted in terms of CRN theory. In the development of ESS we reframe certain aspects of the theory of CRN to better match data analysis. As an application of our approach we process cytomery- and image-based single-cell datasets and identify differences in cells treated with kinase inhibitors. Our approach is directly applicable to data acquired using readily accessible experimental methods such as Fluorescence Activated Cell Sorting (FACS) and multiplex immunofluorescence.nnAuthor summaryWe introduce a new notion, which we call the effective stoichiometric space (ESS), that elucidates network structure from the covariances of single-cell multiplexed data. The ESS approach differs from methods that are based on purely statistical models of data: it allows a completely new and data-driven translation of the theory of toric varieties in geometry and specifically their role in chemical reaction networks (CRN). In the process, we reframe certain aspects of the theory of CRN. As illustrations of our approach, we find stoichiometry in different single-cell datasets, and pinpoint dose-dependence of network perturbations in drug-treated cells.
]]></description>
<dc:creator>Wang, S.</dc:creator>
<dc:creator>Lin, J.-r.</dc:creator>
<dc:creator>Sontag, E. D.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2019-08-09</dc:date>
<dc:identifier>doi:10.1101/731018</dc:identifier>
<dc:title><![CDATA[Inferring reaction network structure from single-cell, multiplex data, using toric systems theory]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-08-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.04.20.440625v1?rss=1">
<title>
<![CDATA[
Stitching and registering highly multiplexed whole slide images of tissues and tumors using ASHLAR software 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.04.20.440625v1?rss=1"
</link>
<description><![CDATA[
MotivationStitching microscope images into a mosaic is an essential step in the analysis and visualization of large biological specimens, particularly human and animal tissues. Recent approaches to highly-multiplexed imaging generate high-plex data from sequential rounds of lower-plex imaging. These multiplexed imaging methods promise to yield precise molecular single-cell data and information on cellular neighborhoods and tissue architecture. However, attaining mosaic images with single-cell accuracy requires robust image stitching and image registration capabilities that are not met by existing methods.

ResultsWe describe the development and testing of ASHLAR, a Python tool for coordinated stitching and registration of 103 or more individual multiplexed images to generate accurate whole-slide mosaics. ASHLAR reads image formats from most commercial microscopes and slide scanners, and we show that it performs better than existing open source and commercial software. ASHLAR outputs standard OME-TIFF images that are ready for analysis by other open-source tools and recently developed image analysis pipelines.

Availability and implementationASHLAR is written in Python and available under an MIT license at https://github.com/labsyspharm/ashlar. An informational website with user guides and test data is available at https://labsyspharm.github.io/ashlar/.
]]></description>
<dc:creator>Muhlich, J. L.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Russell, D. P. W.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2021-04-21</dc:date>
<dc:identifier>doi:10.1101/2021.04.20.440625</dc:identifier>
<dc:title><![CDATA[Stitching and registering highly multiplexed whole slide images of tissues and tumors using ASHLAR software]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-04-21</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.05.23.445310v1?rss=1">
<title>
<![CDATA[
The spatial landscape of progression and immunoediting in primary melanoma at single cell resolution 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.05.23.445310v1?rss=1"
</link>
<description><![CDATA[
Cutaneous melanoma is a highly immunogenic malignancy, surgically curable at early stages, but life- threatening when metastatic. Here we integrate high-plex imaging, 3D high-resolution microscopy, and spatially-resolved micro-region transcriptomics to study immune evasion and immunoediting in primary melanoma. We find that recurrent cellular neighborhoods involving tumor, immune, and stromal cells change significantly along a progression axis involving precursor states, melanoma in situ, and invasive tumor. Hallmarks of immunosuppression are already detectable in precursor regions. When tumors become locally invasive, a consolidated and spatially restricted suppressive environment forms along the tumor-stromal boundary. This environment is established by cytokine gradients that promote expression of MHC-II and IDO1, and by PD1-PDL1 mediated cell contacts involving macrophages, dendritic cells, and T cells. A few millimeters away, cytotoxic T cells synapse with melanoma cells in fields of tumor regression. Thus, invasion and immunoediting can co-exist within a few millimeters of each other in a single specimen.

STATEMENT OF SIGNIFICANCEThe reorganization of the tumor ecosystem in primary melanoma is an excellent setting in which to study immunoediting and immune evasion. Guided by classical histopathology, spatial profiling of proteins and mRNA reveals recurrent morphological and molecular features of tumor evolution that involve localized paracrine cytokine signaling and direct cell-cell contact.
]]></description>
<dc:creator>Nirmal, A. J.</dc:creator>
<dc:creator>Maliga, Z.</dc:creator>
<dc:creator>Vallius, T.</dc:creator>
<dc:creator>Quattrochi, B.</dc:creator>
<dc:creator>Chen, A. A.</dc:creator>
<dc:creator>Jacobson, C. A.</dc:creator>
<dc:creator>Pelletier, R. J.</dc:creator>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Arias-Camison, R.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Lian, C. G.</dc:creator>
<dc:creator>Murphy, G. F.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2021-05-23</dc:date>
<dc:identifier>doi:10.1101/2021.05.23.445310</dc:identifier>
<dc:title><![CDATA[The spatial landscape of progression and immunoediting in primary melanoma at single cell resolution]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-05-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.05.20.445065v1?rss=1">
<title>
<![CDATA[
Fides: Reliable Trust-Region Optimization for Parameter Estimation of Ordinary Differential Equation Models 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.05.20.445065v1?rss=1"
</link>
<description><![CDATA[
Ordinary differential equation (ODE) models are widely used to describe biochemical processes, since they effectively represent mass action kinetics. Optimization-based calibration of ODE models on experimental data can be challenging, even for low-dimensional problems. However, reliable model calibration is a prerequisite for uncertainty analysis, model comparison, and biological interpretation. Multiple hypotheses have been advanced to explain why optimization based calibration of biochemical models is challenging, but there are few comprehensive studies that test these hypotheses, likely because tools for performing such studies are also lacking.

We implemented an established trust-region method as a modular Python framework (fides) to enable systematic comparison of different approaches to ODE model calibration involving various Hessian approximation schemes. We evaluated fides on a set of benchmark problems for which real experimental data are available. Unexpectedly, we observed high variability in optimizer performance among different implementations of the same algorithm. Overall, fides performed most reliably and efficiently. Our investigation of possible sources of poor optimizer performance identified drawbacks in the widely used Gauss-Newton, BFGS and SR1 Hessian approximations. We address these drawbacks by proposing a novel hybrid Hessian approximation scheme that enhances optimizer performance and outperforms existing hybrid approaches. We expect fides to be broadly useful for ODE constrained optimization problems and to enable future methods development.

Availabilityfides is published under the permissive BSD-3-Clause license with source code publicly available at https://github.com/fides-dev/fides. Citeable releases are archived on Zenodo. Code to reproduce results presented in this manuscript is available at https://github.com/fides-dev/fides-benchmark.
]]></description>
<dc:creator>Froehlich, F.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2021-05-22</dc:date>
<dc:identifier>doi:10.1101/2021.05.20.445065</dc:identifier>
<dc:title><![CDATA[Fides: Reliable Trust-Region Optimization for Parameter Estimation of Ordinary Differential Equation Models]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-05-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.05.14.442837v1?rss=1">
<title>
<![CDATA[
Cancer patient survival can be accurately parameterized, revealing time-dependent therapeutic effects and doubling the precision of small trials 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.05.14.442837v1?rss=1"
</link>
<description><![CDATA[
Individual participant data (IPD) from completed oncology clinical trials are a valuable but rarely available source of information. A lack of minable survival distributions has made it difficult to identify factors determining the success and failure of clinical trials and improve trial design. We imputed survival IPD from [~]500 arms of phase III oncology trials (representing [~]220,000 events) and found that they are well fit by a two-parameter Weibull distribution. This makes it possible to use parametric statistics to substantially increase trial precision with small patient cohorts typical of phase I or II trials. For example, a 50-person trial parameterized using Weibull distributions is as precise as a 90-person trial evaluated using traditional statistics. Mining IPD also showed that frequent violations of the proportional hazards assumption, particularly in trials of immune checkpoint inhibitors (ICIs), arise from time-dependent therapeutic effects and hazard ratios. Thus, the duration of ICI trials has an underappreciated impact on the likelihood of their success.
]]></description>
<dc:creator>Plana, D.</dc:creator>
<dc:creator>Fell, G.</dc:creator>
<dc:creator>Alexander, B. M.</dc:creator>
<dc:creator>Palmer, A. C.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2021-05-17</dc:date>
<dc:identifier>doi:10.1101/2021.05.14.442837</dc:identifier>
<dc:title><![CDATA[Cancer patient survival can be accurately parameterized, revealing time-dependent therapeutic effects and doubling the precision of small trials]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-05-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.05.16.443704v1?rss=1">
<title>
<![CDATA[
Temporal and spatial topography of cell proliferation in cancer 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.05.16.443704v1?rss=1"
</link>
<description><![CDATA[
Proliferation is a fundamental trait of cancer cells but is poorly characterized in tumors by classical histologic methods. We use multiplexed tissue imaging to quantify the abundance of multiple cell cycle regulating proteins at single-cell level and develop robust multivariate proliferation metrics. Across cancers, the proliferative architecture is organized at two distinct spatial scales: large domains, and local niches enriched for specific immune lineages. A subset of tumor cells express cell cycle regulators in canonical patterns consistent with unrestrained proliferation, a phenomenon we refer to as "cell cycle coherence". By contrast, the cell cycles of other tumor cell populations are skewed toward a specific phase or characterized by non-canonical (incoherent) marker combinations. Coherence varies across space, with changes in oncogene activity, and with therapeutic intervention, and is associated with aggressive behavior. Multivariate measures capture clinically significant features of cancer proliferation, a fundamental step in enabling more precise use of anti-cancer therapies.
]]></description>
<dc:creator>Gaglia, G.</dc:creator>
<dc:creator>Kabraji, S.</dc:creator>
<dc:creator>Argyropoulou, D.</dc:creator>
<dc:creator>Dai, Y.</dc:creator>
<dc:creator>Wang, S.</dc:creator>
<dc:creator>Bergholz, J.</dc:creator>
<dc:creator>Coy, S.</dc:creator>
<dc:creator>Lin, J.-R.</dc:creator>
<dc:creator>Jeselsohn, R.</dc:creator>
<dc:creator>Metzger, O.</dc:creator>
<dc:creator>Winer, E. P.</dc:creator>
<dc:creator>Dillon, D. A.</dc:creator>
<dc:creator>Zhao, J. J.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:date>2021-05-17</dc:date>
<dc:identifier>doi:10.1101/2021.05.16.443704</dc:identifier>
<dc:title><![CDATA[Temporal and spatial topography of cell proliferation in cancer]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-05-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.08.06.455458v1?rss=1">
<title>
<![CDATA[
Integrating multi-omics data reveals function and therapeutic potential of deubiquitinating enzymes 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.08.06.455458v1?rss=1"
</link>
<description><![CDATA[
Deubiquitinating enzymes (DUBs) are proteases that remove ubiquitin conjugates from proteins, thereby regulating protein turnover. Inhibition of DUBs promises to make classically undruggable targets such as the tumor suppressor TP53 and oncogene c-Myc amenable to regulation by small molecules. However, the majority of substrates and pathways regulated by DUBs remain unknown, impeding efforts to prioritize specific enzymes for research and drug development. To assemble a knowledgebase of DUB activities, co-dependent genes, and substrates, we combined targeted experiments using CRISPR libraries and inhibitors with systematic mining of functional genomic databases. Analysis of the Dependency Map, Connectivity Map, Cancer Cell Line Encyclopedia, and protein-protein interaction databases yielded specific hypotheses about DUB function, a subset of which were confirmed in follow-on experiments. The data in this paper, which are browsable online via the DUB Portal, promise to improve understanding of DUBs as a family as well as the activities of specific DUBs such as USP14, UCHL5 and USP7, which have been targeted with investigational cancer therapeutics.
]]></description>
<dc:creator>Doherty, L. M.</dc:creator>
<dc:creator>Mills, C. E.</dc:creator>
<dc:creator>Boswell, S. A.</dc:creator>
<dc:creator>Liu, X.</dc:creator>
<dc:creator>Hoyt, C. T.</dc:creator>
<dc:creator>Gyori, B. M.</dc:creator>
<dc:creator>Buhrlage, S. J.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2021-08-09</dc:date>
<dc:identifier>doi:10.1101/2021.08.06.455458</dc:identifier>
<dc:title><![CDATA[Integrating multi-omics data reveals function and therapeutic potential of deubiquitinating enzymes]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-08-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.08.02.454840v1?rss=1">
<title>
<![CDATA[
Single-sequence protein structure prediction using language models from deep learning 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.08.02.454840v1?rss=1"
</link>
<description><![CDATA[
AlphaFold2 and related systems use deep learning to predict protein structure from co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite dramatic, recent increases in accuracy, three challenges remain: (i) prediction of orphan and rapidly evolving proteins for which an MSA cannot be generated, (ii) rapid exploration of designed structures, and (iii) understanding the rules governing spontaneous polypeptide folding in solution. Here we report development of an end-to-end differentiable recurrent geometric network (RGN) able to predict protein structure from single protein sequences without use of MSAs. This deep learning system has two novel elements: a protein language model (AminoBERT) that uses a Transformer to learn latent structural information from millions of unaligned proteins and a geometric module that compactly represents C backbone geometry. RGN2 outperforms AlphaFold2 and RoseTTAFold (as well as trRosetta) on orphan proteins and is competitive with designed sequences, while achieving up to a 106-fold reduction in compute time. These findings demonstrate the practical and theoretical strengths of protein language models relative to MSAs in structure prediction.
]]></description>
<dc:creator>Chowdhury, R.</dc:creator>
<dc:creator>Bouatta, N.</dc:creator>
<dc:creator>Biswas, S.</dc:creator>
<dc:creator>Rochereau, C.</dc:creator>
<dc:creator>Church, G. M.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>AlQuraishi, M. N.</dc:creator>
<dc:date>2021-08-04</dc:date>
<dc:identifier>doi:10.1101/2021.08.02.454840</dc:identifier>
<dc:title><![CDATA[Single-sequence protein structure prediction using language models from deep learning]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-08-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.08.06.455429v1?rss=1">
<title>
<![CDATA[
A LINCS microenvironment perturbation resource for integrative assessment of ligand-mediated molecular and phenotypic responses 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.08.06.455429v1?rss=1"
</link>
<description><![CDATA[
The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods (synapse.org/LINCS_MCF10A). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes.
]]></description>
<dc:creator>Gross, S. M.</dc:creator>
<dc:creator>Dane, M. A.</dc:creator>
<dc:creator>Smith, R. L.</dc:creator>
<dc:creator>Devlin, K.</dc:creator>
<dc:creator>Mclean, I.</dc:creator>
<dc:creator>Derrick, D.</dc:creator>
<dc:creator>Mills, C.</dc:creator>
<dc:creator>Subramanian, K.</dc:creator>
<dc:creator>London, A.</dc:creator>
<dc:creator>Torre, D.</dc:creator>
<dc:creator>Erdem, C.</dc:creator>
<dc:creator>Lyons, N.</dc:creator>
<dc:creator>Natoli, T.</dc:creator>
<dc:creator>Pessa, S.</dc:creator>
<dc:creator>Lu, X.</dc:creator>
<dc:creator>Mullahoo, J.</dc:creator>
<dc:creator>Li, J.</dc:creator>
<dc:creator>Adam, M.</dc:creator>
<dc:creator>Wassie, B.</dc:creator>
<dc:creator>Liu, M.</dc:creator>
<dc:creator>Kilburn, D. F.</dc:creator>
<dc:creator>Liby, T.</dc:creator>
<dc:creator>Bucher, E.</dc:creator>
<dc:creator>Sanchez-Aguila, C.</dc:creator>
<dc:creator>Daily, K.</dc:creator>
<dc:creator>Omberg, L.</dc:creator>
<dc:creator>Wang, Y.</dc:creator>
<dc:creator>Jacobson, C.</dc:creator>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Chung, M.</dc:creator>
<dc:creator>Vidovic, D.</dc:creator>
<dc:creator>Lu, Y.</dc:creator>
<dc:creator>Schurer, S.</dc:creator>
<dc:creator>Lee, A.</dc:creator>
<dc:creator>Pillai, A.</dc:creator>
<dc:creator>Subramanian, A.</dc:creator>
<dc:creator>Papanastasiou, M.</dc:creator>
<dc:creator>Fraenkel, E.</dc:creator>
<dc:creator>Feiler, H.</dc:creator>
<dc:creator>Mills, G. B.</dc:creator>
<dc:creator>Jaffe, J.</dc:creator>
<dc:creator>Ma'ayan, A.</dc:creator>
<dc:creator>Birtwistle, M. R.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Korkola, J. E.</dc:creator>
<dc:creator>Gray, J. W.</dc:creator>
<dc:creator>Heiser, L. M.</dc:creator>
<dc:date>2021-08-09</dc:date>
<dc:identifier>doi:10.1101/2021.08.06.455429</dc:identifier>
<dc:title><![CDATA[A LINCS microenvironment perturbation resource for integrative assessment of ligand-mediated molecular and phenotypic responses]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-08-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.08.27.457854v1?rss=1">
<title>
<![CDATA[
Multiplexed and reproducible high content screening of live and fixed cells using the Dye Drop method 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.08.27.457854v1?rss=1"
</link>
<description><![CDATA[
High-throughput measurement of cells perturbed using libraries of small molecules, gene knockouts, or different microenvironmental factors is a key step in functional genomics and pre-clinical drug discovery. However, it remains difficult to perform accurate single-cell assays in 384-well plates, limiting many studies to well-average measurements (e.g. CellTiter-Glo(R)). Here we describe a public domain "Dye Drop" method that uses sequential density displacement and microscopy to perform multi-step assays on living cells. We use Dye Drop cell viability and DNA replication assays followed by immunofluorescence imaging to collect single-cell dose-response data for 67 investigational and clinical-grade small molecules in 58 breast cancer cell lines. By separating the cytostatic and cytotoxic effects of drugs computationally, we uncover unexpected relationships between the two. Dye Drop is rapid, reproducible, customizable, and compatible with manual or automated laboratory equipment. Dye Drop improves the tradeoff between data content and cost, enabling the collection of information-rich perturbagen-response datasets.
]]></description>
<dc:creator>Mills, C. E.</dc:creator>
<dc:creator>Subramanian, K.</dc:creator>
<dc:creator>Hafner, M.</dc:creator>
<dc:creator>Niepel, M.</dc:creator>
<dc:creator>Gerosa, L.</dc:creator>
<dc:creator>Chung, M.</dc:creator>
<dc:creator>Victor, C.</dc:creator>
<dc:creator>Gaudio, B.</dc:creator>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2021-08-28</dc:date>
<dc:identifier>doi:10.1101/2021.08.27.457854</dc:identifier>
<dc:title><![CDATA[Multiplexed and reproducible high content screening of live and fixed cells using the Dye Drop method]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-08-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.01.12.475925v1?rss=1">
<title>
<![CDATA[
Single Cell Spatial Analysis Reveals the Topology of Immunomodulatory Purinergic Signaling in Glioblastoma 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.01.12.475925v1?rss=1"
</link>
<description><![CDATA[
Glioblastoma develops an immunosuppressive microenvironment that fosters tumorigenesis and resistance to current therapeutic strategies. Here we use multiplexed tissue imaging and single-cell RNA-sequencing to characterize the composition, spatial organization, and clinical significance of extracellular purinergic signaling in glioblastoma. We show that glioblastoma exhibit strong expression of CD39 and CD73 ectoenzymes, correlating with increased adenosine levels. Microglia are the predominant source of CD39, while CD73 is principally expressed by tumor cells, particularly in tumors with amplification of EGFR and astrocyte-like differentiation. Spatially-resolved single-cell analyses demonstrate strong spatial correlation between tumor CD73 and microglial CD39, and that their spatial proximity is associated with poor clinical outcomes. Together, this data reveals that tumor CD73 expression correlates with tumor genotype, lineage differentiation, and functional states, and that core purine regulatory enzymes expressed by neoplastic and tumor-associated myeloid cells interact to promote a distinctive adenosine-rich signaling niche and immunosuppressive microenvironment potentially amenable to therapeutic targeting.
]]></description>
<dc:creator>Coy, S.</dc:creator>
<dc:creator>Wang, S.</dc:creator>
<dc:creator>Stopka, S. A.</dc:creator>
<dc:creator>Lin, J.-R.</dc:creator>
<dc:creator>Rashid, R.</dc:creator>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Ritch, C. C.</dc:creator>
<dc:creator>Khadka, P.</dc:creator>
<dc:creator>Regan, M.</dc:creator>
<dc:creator>Hwang, J.</dc:creator>
<dc:creator>Wen, P. Y.</dc:creator>
<dc:creator>Bandopadhayay, P.</dc:creator>
<dc:creator>Ligon, K. L.</dc:creator>
<dc:creator>Agar, N. Y.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Touat, M.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:date>2022-01-12</dc:date>
<dc:identifier>doi:10.1101/2022.01.12.475925</dc:identifier>
<dc:title><![CDATA[Single Cell Spatial Analysis Reveals the Topology of Immunomodulatory Purinergic Signaling in Glioblastoma]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-01-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.02.17.480899v1?rss=1">
<title>
<![CDATA[
Mechanistic model of MAPK signaling reveals how allostery and rewiring contribute to drug resistance 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.02.17.480899v1?rss=1"
</link>
<description><![CDATA[
BRAFV600E is prototypical of oncogenic mutations that can be targeted therapeutically and treatment of BRAF-mutant melanomas with RAF and MEK inhibitors results in rapid tumor regression. However, drug-induced rewiring causes BRAFV600E melanoma cells to rapidly acquire a drug-adapted state. In patients this is thought to promote acquisition or selection for resistance mutations and disease recurrence. In this paper we use an energy-based implementation of ordinary differential equations in combination with proteomic, transcriptomic and imaging data from melanoma cells, to model the precise mechanisms responsible for adaptive rewiring. We demonstrate the presence of two parallel MAPK (RAF-MEK-ERK kinase) reaction channels in BRAFV600E melanoma cells that are differentially sensitive to RAF and MEK inhibitors. This arises from differences in protein oligomerization and allosteric regulation induced by oncogenic mutations and drug binding. As a result, the RAS-regulated MAPK channel can be active under conditions in which the BRAFV600E-driven channel is fully inhibited. Causal tracing demonstrates that this provides a sufficient quantitative explanation for initial and acquired responses to multiple different RAF and MEK inhibitors individually and in combination.

HighlightsO_LIA thermodynamic framework enables structure-based description of allosteric interactions in the EGFR and MAPK pathways
C_LIO_LICausal decomposition of efficacy of targeted drugs elucidates rewiring of MAPK channels
C_LIO_LIModel-based extrapolation from type I[1/2] RAF inhibitors to type II RAF inhibitors
C_LIO_LIA unified mechanistic explanation for adaptive and genetic resistance across BRAF-cancers
C_LI
]]></description>
<dc:creator>Froehlich, F.</dc:creator>
<dc:creator>Gerosa, L.</dc:creator>
<dc:creator>Muhlich, J. L.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2022-02-18</dc:date>
<dc:identifier>doi:10.1101/2022.02.17.480899</dc:identifier>
<dc:title><![CDATA[Mechanistic model of MAPK signaling reveals how allostery and rewiring contribute to drug resistance]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-02-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.05.09.490039v1?rss=1">
<title>
<![CDATA[
Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.05.09.490039v1?rss=1"
</link>
<description><![CDATA[
New highly-multiplexed imaging technologies have enabled the study of tissues in unprecedented detail. These methods are increasingly being applied to understand how cancer cells and immune response change during tumor development, progression, and metastasis, as well as following treatment. Yet, existing analysis approaches focus on investigating small tissue samples on a per-cell basis, not taking into account the spatial proximity of cells, which indicates cell-cell interaction and specific biological processes in the larger cancer microenvironment. We present Visinity, a scalable visual analytics system to analyze cell interaction patterns across cohorts of whole-slide multiplexed tissue images. Our approach is based on a fast regional neighborhood computation, leveraging unsupervised learning to quantify, compare, and group cells by their surrounding cellular neighborhood. These neighborhoods can be visually analyzed in an exploratory and confirmatory workflow. Users can explore spatial patterns present across tissues through a scalable image viewer and coordinated views highlighting the neighborhood composition and spatial arrangements of cells. To verify or refine existing hypotheses, users can query for specific patterns to determine their presence and statistical significance. Findings can be interactively annotated, ranked, and compared in the form of small multiples. In two case studies with biomedical experts, we demonstrate that Visinity can identify common biological processes within a human tonsil and uncover novel white-blood cell networks and immune-tumor interactions.
]]></description>
<dc:creator>Warchol, S.</dc:creator>
<dc:creator>Krueger, R.</dc:creator>
<dc:creator>Nirmal, A. J.</dc:creator>
<dc:creator>Gaglia, G.</dc:creator>
<dc:creator>Jessup, J.</dc:creator>
<dc:creator>Ritch, C. C.</dc:creator>
<dc:creator>Hoffer, J.</dc:creator>
<dc:creator>Muhlich, J. L.</dc:creator>
<dc:creator>Burger, M.</dc:creator>
<dc:creator>Jacks, T. L.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Pfister, H.</dc:creator>
<dc:date>2022-05-09</dc:date>
<dc:identifier>doi:10.1101/2022.05.09.490039</dc:identifier>
<dc:title><![CDATA[Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-05-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.07.22.451363v1?rss=1">
<title>
<![CDATA[
A community-based approach to image analysis of cells, tissues and tumors 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.07.22.451363v1?rss=1"
</link>
<description><![CDATA[
Emerging multiplexed imaging platforms provide an unprecedented view of an increasing number of molecular markers at subcellular resolution and the dynamic evolution of tumor cellular composition. As such, they are capable of elucidating cell-to-cell interactions within the tumor microenvironment that impact clinical outcome and therapeutic response. However, the rapid development of these platforms has far outpaced the computational methods for processing and analyzing the data they generate. While being technologically disparate, all imaging assays share many computational requirements for post-collection data processing. We convened a workshop to characterize these shared computational challenges and a follow-up hackathon to implement solutions for a selected subset of them. Here, we delineate these areas that reflect major axes of research within the field, including image registration, segmentation of cells and subcellular structures, and identification of cell types from their morphology. We further describe the logistical organization of these events, believing our lessons learned can aid others in uniting the imaging community around self-identified topics of mutual interest, in designing and implementing operational procedures to address those topics and in mitigating issues inherent in image analysis (e.g., sharing exemplar images of large datasets and disseminating baseline solutions to hackathon challenges through open-source code repositories).
]]></description>
<dc:creator>CSBC/PS-ON Image Analysis Working Group,</dc:creator>
<dc:creator>Vizcarra, J. C.</dc:creator>
<dc:creator>Burlingame, E. A.</dc:creator>
<dc:creator>Hug, C. B.</dc:creator>
<dc:creator>Goltsev, Y.</dc:creator>
<dc:creator>White, B. S.</dc:creator>
<dc:creator>Tyson, D. R.</dc:creator>
<dc:creator>Sokolov, A.</dc:creator>
<dc:date>2021-07-25</dc:date>
<dc:identifier>doi:10.1101/2021.07.22.451363</dc:identifier>
<dc:title><![CDATA[A community-based approach to image analysis of cells, tissues and tumors]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-07-25</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.12.05.471248v1?rss=1">
<title>
<![CDATA[
Design principles to assemble drug combinations for effective tuberculosis therapy using interpretable pairwise drug response measurements 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.12.05.471248v1?rss=1"
</link>
<description><![CDATA[
A challenge in designing treatment regimens for tuberculosis is the necessity to use three or more antibiotics in combination. The combination space is too large to be comprehensively assayed; therefore, only a small number of possible combinations are tested. We narrowed the prohibitively large search space of combination drug responses by breaking down high-order combinations into units of drug pairs. Using pairwise drug potency and drug interaction metrics from in vitro experiments across multiple growth environments, we trained machine learning models to predict outcomes associated with higher-order combinations in the BALB/c relapsing mouse model, an important preclinical model for drug development. We systematically predicted treatment outcomes of >500 combinations among twelve antibiotics. Our classifiers performed well on test data and predicted many novel combinations to be improved over bedaquiline + pretomanid + linezolid, an effective regimen for multidrug-resistant tuberculosis that also shortens treatment in BALB/c mice compared to the standard of care. To understand the design features of effective drug combinations, we reformulated classifiers as simple rulesets to reveal guiding principles of constructing combination therapies for both preclinical and clinical outcomes. One example ruleset is to include a drug pair that is synergistic in dormancy and another pair that is potent in a cholesterol-rich growth environment. These rulesets are predictive, intuitive, and practical, thus enabling rational construction of effective drug combinations based on in vitro pairwise drug synergies and potencies. As more preclinical and clinical drug combination data become available, we expect to improve predictions and combination design rules.
]]></description>
<dc:creator>Larkins-Ford, J.</dc:creator>
<dc:creator>Degefu, Y. N.</dc:creator>
<dc:creator>Van, N.</dc:creator>
<dc:creator>Sokolov, A.</dc:creator>
<dc:creator>Aldridge, B. B.</dc:creator>
<dc:date>2021-12-06</dc:date>
<dc:identifier>doi:10.1101/2021.12.05.471248</dc:identifier>
<dc:title><![CDATA[Design principles to assemble drug combinations for effective tuberculosis therapy using interpretable pairwise drug response measurements]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-12-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.02.03.429579v1?rss=1">
<title>
<![CDATA[
Systematic measurement of combination drug landscapes to predict in vivo treatment outcomes for tuberculosis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.02.03.429579v1?rss=1"
</link>
<description><![CDATA[
A lengthy multidrug chemotherapy is required to achieve a durable cure in tuberculosis. Variation in Mycobacterium tuberculosis drug response is created by the differing microenvironments in lesions, which generate different bacterial drug susceptibilities. To better realize the potential of combination therapy to shorten treatment duration, multidrug therapy design should deliberately explore the vast combination space. We face a significant scaling challenge in making systematic drug combination measurements because it is not practical to use animal models for comprehensive drug combination studies, nor are there well-validated high-throughput in vitro models that predict animal outcomes. We hypothesized that we could both prioritize combination therapies and quantify the predictive power of various in vitro models for drug development using a dataset of drug combination dose responses measured in multiple in vitro models. We systematically measured M. tuberculosis response to all 2- and 3-drug combinations among ten antibiotics in eight conditions that reproduce lesion microenvironments. Applying machine learning to this comprehensive dataset, we developed classifiers predictive of multidrug treatment outcome in a mouse model of disease relapse. We trained classifiers on multiple mouse models and identified ensembles of in vitro models that best describe in vivo treatment outcomes. Furthermore, we found that combination synergies are less important for predicting outcome than metrics of potency. Here, we map a path forward to rationally prioritize combinations for animal and clinical studies using systematic drug combination measurements with validated in vitro models. Our pipeline is generalizable to other difficult-to-treat diseases requiring combination therapies.

One Sentence SummarySignatures of in vitro potency and drug interaction measurements predict combination therapy outcomes in mouse models of tuberculosis.
]]></description>
<dc:creator>Larkins-Ford, J.</dc:creator>
<dc:creator>Greenstein, T.</dc:creator>
<dc:creator>Van, N.</dc:creator>
<dc:creator>Degefu, Y. N.</dc:creator>
<dc:creator>Olson, M. C.</dc:creator>
<dc:creator>Sokolov, A.</dc:creator>
<dc:creator>Aldridge, B. B.</dc:creator>
<dc:date>2021-02-04</dc:date>
<dc:identifier>doi:10.1101/2021.02.03.429579</dc:identifier>
<dc:title><![CDATA[Systematic measurement of combination drug landscapes to predict in vivo treatment outcomes for tuberculosis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-02-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/722918v1?rss=1">
<title>
<![CDATA[
Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/722918v1?rss=1"
</link>
<description><![CDATA[
Facetto is a scalable visual analytics application that is used to discover single-cell phenotypes in high-dimensional multi-channel microscopy images of human tumors and tissues. Such images represent the cutting edge of digital histology and promise to revolutionize how diseases such as cancer are studied, diagnosed, and treated. Highly multiplexed tissue images are complex, comprising 109 or more pixels, 60-plus channels, and millions of individual cells. This makes manual analysis challenging and error-prone. Existing automated approaches are also inadequate, in large part, because they are unable to effectively exploit the deep knowledge of human tissue biology available to anatomic pathologists. To overcome these challenges, Facetto enables a semi-automated analysis of cell types and states. It integrates unsupervised and supervised learning into the image and feature exploration process and offers tools for analytical provenance. Experts can cluster the data to discover new types of cancer and immune cells and use clustering results to train a convolutional neural network that classifies new cells accordingly. Likewise, the output of classifiers can be clustered to discover aggregate patterns and phenotype subsets. We also introduce a new hierarchical approach to keep track of analysis steps and data subsets created by users; this assists in the identification of cell types. Users can build phenotype trees and interact with the resulting hierarchical structures of both high-dimensional feature and image spaces. We report on use-cases in which domain scientists explore various large-scale fluorescence imaging datasets. We demonstrate how Facetto assists users in steering the clustering and classification process, inspecting analysis results, and gaining new scientific insights into cancer biology.
]]></description>
<dc:creator>Krueger, R.</dc:creator>
<dc:creator>Beyer, J.</dc:creator>
<dc:creator>Jang, W.-D.</dc:creator>
<dc:creator>Kim, N. W.</dc:creator>
<dc:creator>Sokolov, A.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Pfister, H.</dc:creator>
<dc:date>2019-08-02</dc:date>
<dc:identifier>doi:10.1101/722918</dc:identifier>
<dc:title><![CDATA[Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-08-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/704114v1?rss=1">
<title>
<![CDATA[
Highly multiplexed immunofluorescence images and single-cell data of immune markers in tonsil and lung cancer 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/704114v1?rss=1"
</link>
<description><![CDATA[
In this data descriptor, we document a dataset of multiplexed immunofluorescence images and derived single-cell measurements of immune lineage and other markers in formaldehyde-fixed and paraffin-embedded (FFPE) human tonsil and lung cancer tissue. We used tissue cyclic immunofluorescence (t-CyCIF) to generate fluorescence images which we artifact corrected using the BaSiC tool, stitched and registered using the ASHLAR algorithm, and segmented using ilastik software and MATLAB. We extracted single-cell features from these images using HistoCAT software. The resulting dataset can be visualized using image browsers and analyzed using high-dimensional, single-cell methods. This dataset is a valuable resource for biological discovery of the immune system in normal and diseased states as well as for the development of multiplexed image analysis and viewing tools.nnMETADATA SUMMARYnnO_TBL View this table:norg.highwire.dtl.DTLVardef@6edf3eorg.highwire.dtl.DTLVardef@1026e07org.highwire.dtl.DTLVardef@85a7baorg.highwire.dtl.DTLVardef@c6d7e5org.highwire.dtl.DTLVardef@87fccf_HPS_FORMAT_FIGEXP  M_TBL C_TBL
]]></description>
<dc:creator>Rashid, R.</dc:creator>
<dc:creator>Gaglia, G.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Lin, J.-R.</dc:creator>
<dc:creator>Du, Z.</dc:creator>
<dc:creator>Maliga, Z.</dc:creator>
<dc:creator>Schapiro, D.</dc:creator>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Muhlich, J.</dc:creator>
<dc:creator>Sokolov, A.</dc:creator>
<dc:creator>Sorger, P.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:date>2019-07-17</dc:date>
<dc:identifier>doi:10.1101/704114</dc:identifier>
<dc:title><![CDATA[Highly multiplexed immunofluorescence images and single-cell data of immune markers in tonsil and lung cancer]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-07-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.09.10.459803v1?rss=1">
<title>
<![CDATA[
Gilda: biomedical entity text normalization with machine-learned disambiguation as a service 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.09.10.459803v1?rss=1"
</link>
<description><![CDATA[
SummaryGilda is a software tool and web service which implements a scored string matching algorithm for names and synonyms across entries in biomedical ontologies covering genes, proteins (and their families and complexes), small molecules, biological processes and diseases. Gilda integrates machine-learned disambiguation models to choose between ambiguous strings given relevant surrounding text as context, and supports species-prioritization in case of ambiguity.

AvailabilityThe Gilda web service is available at http://grounding.indra.bio with source code, documentation and tutorials are available via https://github.com/indralab/gilda.

Contactbenjamin_gyori@hms.harvard.edu
]]></description>
<dc:creator>Gyori, B. M.</dc:creator>
<dc:creator>Hoyt, C. T.</dc:creator>
<dc:creator>Steppi, A.</dc:creator>
<dc:date>2021-09-11</dc:date>
<dc:identifier>doi:10.1101/2021.09.10.459803</dc:identifier>
<dc:title><![CDATA[Gilda: biomedical entity text normalization with machine-learned disambiguation as a service]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-09-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/862003v1?rss=1">
<title>
<![CDATA[
Robustness and parameter geography in post-translational modification systems 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/862003v1?rss=1"
</link>
<description><![CDATA[
Biological systems are acknowledged to be robust to perturbations but a rigorous understanding of this has been elusive. In a mathematical model, perturbations often exert their effect through parameters, so sizes and shapes of parametric regions offer an integrated global estimate of robustness. Here, we explore this "parameter geography" for bistability in post-translational modification (PTM) systems. We use the previously developed "linear framework" for timescale separation to describe the steady-states of a two-site PTM system as the solutions of two polynomial equations in two variables, with eight non-dimensional parameters. Importantly, this approach allows us to accommodate enzyme mechanisms of arbitrary complexity beyond the conventional Michaelis-Menten scheme, which unrealistically forbids product rebinding. We further use the numerical algebraic geometry tools Bertini, Paramotopy, and alphaCertified to statistically assess the solutions to these equations at [~]109 parameter points in total. Subject to sampling limitations, we find no bistability when substrate amount is below a threshold relative to enzyme amounts. As substrate increases, the bistable region acquires 8-dimensional volume which increases in an apparently monotonic and sigmoidal manner towards saturation. The region remains connected but not convex, albeit with a high visibility ratio. Surprisingly, the saturating bistable region occupies a much smaller proportion of the sampling domain under mechanistic assumptions more realistic than the Michaelis-Menten scheme. We find that bistability is compromised by product rebinding and that unrealistic assumptions on enzyme mechanisms have obscured its parametric rarity. The apparent monotonic increase in volume of the bistable region remains perplexing because the region itself does not grow monotonically: parameter points can move back and forth between monostability and bistability. We suggest mathematical conjectures and questions arising from these findings. Advances in theory and software now permit insights into parameter geography to be uncovered by high-dimensional, data-centric analysis.

Author SummaryBiological organisms are often said to have robust properties but it is difficult to understand how such robustness arises from molecular interactions. Here, we use a mathematical model to study how the molecular mechanism of protein modification exhibits the property of multiple internal states, which has been suggested to underlie memory and decision making. The robustness of this property is revealed by the size and shape, or "geography," of the parametric region in which the property holds. We use advances in reducing model complexity and in rapidly solving the underlying equations, to extensively sample parameter points in an 8-dimensional space. We find that under realistic molecular assumptions the size of the region is surprisingly small, suggesting that generating multiple internal states with such a mechanism is much harder than expected. While the shape of the region appears straightforward, we find surprising complexity in how the region grows with increasing amounts of the modified substrate. Our approach uses statistical analysis of data generated from a model, rather than from experiments, but leads to precise mathematical conjectures about parameter geography and biological robustness.
]]></description>
<dc:creator>Nam, K.-M.</dc:creator>
<dc:creator>Gyori, B. M.</dc:creator>
<dc:creator>Amethyst, S. V.</dc:creator>
<dc:creator>Bates, D. J.</dc:creator>
<dc:creator>Gunawardena, J.</dc:creator>
<dc:date>2019-12-02</dc:date>
<dc:identifier>doi:10.1101/862003</dc:identifier>
<dc:title><![CDATA[Robustness and parameter geography in post-translational modification systems]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-12-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.03.10.382333v1?rss=1">
<title>
<![CDATA[
Capturing scientific knowledge in computable form 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.03.10.382333v1?rss=1"
</link>
<description><![CDATA[
Technological advances in computing provide major opportunities to accelerate scientific discovery. The wide availability of structured knowledge would allow us to take full advantage of these by enabling efficient human-computer interaction. Traditionally, biological knowledge is captured in publications and knowledge bases, however, the information in articles is not directly accessible to computers, and knowledge bases are constrained by finite resources available for manual curation. To accelerate knowledge capture and communication and to keep pace with the rapid growth of scientific reports, we developed the Biofactoid (biofactoid.org) software suite, which crowdsources structured knowledge in articles from authors. Biofactoid is a web-based system that lets scientists draw a network of interactions between genes, their products, and chemical compounds and employs smart-automation to translate user input into a structured language using the expressive power of a formal ontology. The resulting data is shared via public information resources, enabling author-curated knowledge to be appreciated in the context of all existing computable knowledge. Authors of recently published papers across a range of journals have already contributed their pathway information, much of which is novel and extends existing pathway databases into new biological areas. We envision the adoption of Biofactoid for crowdsourced curation by scientists and publishers as part of an ecosystem of tools that accelerate scientific communication and discovery.

AvailabilityBiofactoid server at https://biofactoid.org
]]></description>
<dc:creator>Wong, J. V.</dc:creator>
<dc:creator>Franz, M.</dc:creator>
<dc:creator>Siper, M. C.</dc:creator>
<dc:creator>Fong, D.</dc:creator>
<dc:creator>Durupinar, F.</dc:creator>
<dc:creator>Dallago, C.</dc:creator>
<dc:creator>Luna, A.</dc:creator>
<dc:creator>Giorgi, J. M.</dc:creator>
<dc:creator>Rodchenkov, I.</dc:creator>
<dc:creator>Babur, O.</dc:creator>
<dc:creator>Gyori, B. M.</dc:creator>
<dc:creator>Bachman, J. A.</dc:creator>
<dc:creator>Demir, E.</dc:creator>
<dc:creator>Bader, G. D.</dc:creator>
<dc:creator>Sander, C.</dc:creator>
<dc:date>2021-03-11</dc:date>
<dc:identifier>doi:10.1101/2021.03.10.382333</dc:identifier>
<dc:title><![CDATA[Capturing scientific knowledge in computable form]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-03-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/536409v1?rss=1">
<title>
<![CDATA[
Re-curation and Rational Enrichment of Knowledge Graphs in Biological Expression Language 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/536409v1?rss=1"
</link>
<description><![CDATA[
The rapid accumulation of new biomedical literature not only causes curated knowledge graphs to become outdated and incomplete, but also makes manual curation an impractical and unsustainable solution. Automated or semi-automated workflows are necessary to assist in prioritizing and curating the literature to update and enrich knowledge graphs.

We have developed two workflows: one for re-curating a given knowledge graph to assure its syntactic and semantic quality and another for rationally enriching it by manually revising automatically extracted relations for nodes with low information density. We applied these workflows to the knowledge graphs encoded in Biological Expression Language from the NeuroMMSig database using content that was pre-extracted from MEDLINE abstracts and PubMed Central full text articles using text mining output integrated by INDRA. We have made this workflow freely available at https://github.com/bel-enrichment/bel-enrichment.

Database URLhttps://github.com/bel-enrichment/results
]]></description>
<dc:creator>Hoyt, C.</dc:creator>
<dc:creator>Domingo-Fernandez, D.</dc:creator>
<dc:creator>Aldisi, R.</dc:creator>
<dc:creator>Xu, L.</dc:creator>
<dc:creator>Kolpeja, K.</dc:creator>
<dc:creator>Spalek, S.</dc:creator>
<dc:creator>Wollert, E.</dc:creator>
<dc:creator>Bachman, J.</dc:creator>
<dc:creator>Gyori, B.</dc:creator>
<dc:creator>Greene, P.</dc:creator>
<dc:creator>Hofmann-Apitius, M.</dc:creator>
<dc:date>2019-01-31</dc:date>
<dc:identifier>doi:10.1101/536409</dc:identifier>
<dc:title><![CDATA[Re-curation and Rational Enrichment of Knowledge Graphs in Biological Expression Language]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-01-31</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.08.05.455309v1?rss=1">
<title>
<![CDATA[
Transitions in the proteome and phospho-proteome during early embryonic development in Xenopus 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.08.05.455309v1?rss=1"
</link>
<description><![CDATA[
Vertebrate development from an egg to a complex multi-cell organism is accompanied by multiple phases of genome-scale changes in the repertoire of proteins and their post-translational modifications. While much has been learned at the RNA level, we know less about changes at the protein level. In this paper, we present a deep analysis of changes of [~]15,000 proteins and [~]11,500 phospho-sites at 11 developmental time points in Xenopus laevis embryos ranging from the stage VI oocyte to juvenile tadpole. We find that the most dramatic changes to the proteome occur during the transition to functional organ systems, which occurs as the embryo becomes a tadpole. At that time, the absolute amount of non-yolk protein increases two-fold, and there is a shift in the balance of expression from proteins regulating gene expression to receptors, ligands, and proteins involved in cell-cell and cell-environment interactions. Between the early and late tadpole, the median increase for membrane and secreted proteins is substantially higher than that of nuclear proteins. To begin to appreciate changes at the post-translational level, we have measured quantitative phospho-proteomic data across the same developmental stages. In contrast to the significant protein changes that are concentrated at the end of the time series, the most significant phosphorylation changes are concentrated in the very early stages of development. A clear exception are phosphorylations of proteins involved in gene expression: these increase just after fertilization, with patterns that are highly correlated with the underlying protein changes. To facilitate the interpretation of this unique phospho-protoeme data set, we created a pipeline for identifying homologous human phosphorylations from the measured Xenopus phospho-proteome. Overall, we detected many profound temporal transitions, that suggest concerted changes in developmental strategies in the embryo, which are particularly pronounced once early patterning and specification are complete.
]]></description>
<dc:creator>Van Itallie, E.</dc:creator>
<dc:creator>Kalocsay, M.</dc:creator>
<dc:creator>Wuhr, M.</dc:creator>
<dc:creator>Peshkin, L.</dc:creator>
<dc:creator>Kirschner, M. W.</dc:creator>
<dc:date>2021-08-06</dc:date>
<dc:identifier>doi:10.1101/2021.08.05.455309</dc:identifier>
<dc:title><![CDATA[Transitions in the proteome and phospho-proteome during early embryonic development in Xenopus]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-08-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.07.29.225797v1?rss=1">
<title>
<![CDATA[
Time-resolved proteomic profiling of the ciliary Hedgehog response reveals that GPR161 and PKA undergo regulated co-exit from cilia 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.07.29.225797v1?rss=1"
</link>
<description><![CDATA[
The primary cilium is a signaling compartment that interprets Hedgehog signals through changes of its protein, lipid and second messenger compositions. Here, we combine proximity labeling of cilia with quantitative mass spectrometry to unbiasedly profile the time-dependent alterations of the ciliary proteome in response to Hedgehog. This approach correctly identifies the three factors known to undergo Hedgehog-regulated ciliary redistribution and reveals two such additional proteins. First, we find that a regulatory subunit of the cAMP-dependent protein kinase (PKA) rapidly exits cilia together with the G protein-coupled receptor GPR161 in response to Hedgehog; and we propose that the GPR161/PKA module senses and amplifies cAMP signals to modulate ciliary PKA activity. Second, we identify the putative phosphatase Paladin as a cell type-specific regulator of Hedgehog signaling that enters primary cilia upon pathway activation. The broad applicability of quantitative ciliary proteome profiling promises a rapid characterization of ciliopathies and their underlying signaling malfunctions.
]]></description>
<dc:creator>May, E. A.</dc:creator>
<dc:creator>Kalocsay, M.</dc:creator>
<dc:creator>Galtier D'Auriac, I.</dc:creator>
<dc:creator>Gygi, S. P.</dc:creator>
<dc:creator>Nachury, M. V.</dc:creator>
<dc:creator>Mick, D. U.</dc:creator>
<dc:date>2020-07-29</dc:date>
<dc:identifier>doi:10.1101/2020.07.29.225797</dc:identifier>
<dc:title><![CDATA[Time-resolved proteomic profiling of the ciliary Hedgehog response reveals that GPR161 and PKA undergo regulated co-exit from cilia]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-07-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.06.05.135889v1?rss=1">
<title>
<![CDATA[
Orphan nuclear receptor COUP-TFII drives the myofibroblast metabolic shift leading to fibrosis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.06.05.135889v1?rss=1"
</link>
<description><![CDATA[
Recent studies demonstrated that metabolic disturbance, such as augmented glycolysis, contributes to fibrosis. The molecular regulation of this metabolic perturbation in fibrosis, however, has been elusive. COUP-TFII (also known as NR2F2) is an important regulator of glucose and lipid metabolism. Its contribution to organ fibrosis is undefined. Here, we found increased COUP-TFII expression in myofibroblasts in kidneys of patients with chronic kidney disease, fibrotic lungs of patients with idiopathic pulmonary fibrosis, fibrotic human kidney organoids, and fibrotic mouse kidneys after injury. Genetic ablation of COUP-TFII in mice resulted in attenuation of injury-induced kidney fibrosis. A non-biased proteomic study revealed the suppression of fatty acid oxidation and the enhancement of glycolysis pathways in COUP-TFII overexpressing fibroblasts. Overexpression of COUP-TFII in fibroblasts was sufficient to enhance glycolysis and increase alpha smooth muscle actin (SMA) and collagen1 levels. Knockout of COUP-TFII decreased glycolysis and collagen1 levels in fibroblasts. Chip-qPCR assays revealed the binding of COUP-TFII on the promoter of PGC1, a critical regulator of mitochondrial genesis and oxidative metabolism. Overexpression of COUP-TFII reduced the cellular level of PGC1. In conclusion, COUP-TFII mediates fibrosis by serving as a key regulator of the shift in cellular metabolism of interstitial pericytes/fibroblasts from oxidative respiration to aerobic glycolysis. The fibrogenic response may share a common pathway in different organ injury and failure. Targeting COUP-TFII serves as a novel treatment approach for mitigating fibrosis in chronic kidney disease and potential other organ fibrosis.
]]></description>
<dc:creator>Li, L.</dc:creator>
<dc:creator>Galichon, P.</dc:creator>
<dc:creator>Xiao, X.</dc:creator>
<dc:creator>Figueroa-Ramirez, A. C.</dc:creator>
<dc:creator>Tamayo, D.</dc:creator>
<dc:creator>Lee, J. J.</dc:creator>
<dc:creator>Kalocsay, M.</dc:creator>
<dc:creator>Gonzalez-Sanchez, D.</dc:creator>
<dc:creator>Chancay, M. S.</dc:creator>
<dc:creator>McCracken, K.</dc:creator>
<dc:creator>Lemos, D.</dc:creator>
<dc:creator>Lee, N.</dc:creator>
<dc:creator>Ichimura, T.</dc:creator>
<dc:creator>Mori, Y.</dc:creator>
<dc:creator>Valerius, M. T.</dc:creator>
<dc:creator>Sun, X.</dc:creator>
<dc:creator>Edelman, E. R.</dc:creator>
<dc:creator>Bonventre, J. V.</dc:creator>
<dc:date>2020-06-05</dc:date>
<dc:identifier>doi:10.1101/2020.06.05.135889</dc:identifier>
<dc:title><![CDATA[Orphan nuclear receptor COUP-TFII drives the myofibroblast metabolic shift leading to fibrosis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-06-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2019.12.22.886523v1?rss=1">
<title>
<![CDATA[
The Kinase Chemogenomic Set (KCGS): An open science resource for kinase vulnerability identification 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2019.12.22.886523v1?rss=1"
</link>
<description><![CDATA[
We describe the assembly and annotation of a chemogenomic set of protein kinase inhibitors as an open science resource for studying kinase biology. The set only includes inhibitors that show potent kinase inhibition and a narrow spectrum of activity when screened across a large panel of kinase biochemical assays. Currently, the set contains 187 inhibitors that cover 215 human kinases. The kinase chemogenomic set (KCGS) is the most highly annotated set of selective kinase inhibitors available to researchers for use in cell-based screens.
]]></description>
<dc:creator>Wells, C. I.</dc:creator>
<dc:creator>Al-Ali, H.</dc:creator>
<dc:creator>Andrews, D. M.</dc:creator>
<dc:creator>Asquith, C. R. M.</dc:creator>
<dc:creator>Axtman, A. D.</dc:creator>
<dc:creator>Chung, M.</dc:creator>
<dc:creator>Dikic, I.</dc:creator>
<dc:creator>Ebner, D.</dc:creator>
<dc:creator>Elkins, J.</dc:creator>
<dc:creator>Ettmayer, P.</dc:creator>
<dc:creator>Fischer, C.</dc:creator>
<dc:creator>Frederiksen, M.</dc:creator>
<dc:creator>Gray, N. S.</dc:creator>
<dc:creator>Hatch, S. B.</dc:creator>
<dc:creator>Knapp, S.</dc:creator>
<dc:creator>Lee, S.</dc:creator>
<dc:creator>Lucking, U.</dc:creator>
<dc:creator>Michaelides, M.</dc:creator>
<dc:creator>Mills, C. E.</dc:creator>
<dc:creator>Muller, S.</dc:creator>
<dc:creator>Owen, D.</dc:creator>
<dc:creator>Picado, A.</dc:creator>
<dc:creator>Ramadan, K.</dc:creator>
<dc:creator>Saikatendu, K. S.</dc:creator>
<dc:creator>Schroder, M.</dc:creator>
<dc:creator>Stolz, A.</dc:creator>
<dc:creator>Tellechea, M.</dc:creator>
<dc:creator>Treiber, D. K.</dc:creator>
<dc:creator>Turunen, B. J.</dc:creator>
<dc:creator>Vilar, S.</dc:creator>
<dc:creator>Wang, J.</dc:creator>
<dc:creator>Zuercher, W.</dc:creator>
<dc:creator>Willson, T. M.</dc:creator>
<dc:creator>Drewry, D. H.</dc:creator>
<dc:date>2019-12-23</dc:date>
<dc:identifier>doi:10.1101/2019.12.22.886523</dc:identifier>
<dc:title><![CDATA[The Kinase Chemogenomic Set (KCGS): An open science resource for kinase vulnerability identification]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-12-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.09.13.276923v1?rss=1">
<title>
<![CDATA[
Age-dependent regulation of SARS-CoV-2 cell entry genes and cell death programs correlates with COVID-19 disease severity 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.09.13.276923v1?rss=1"
</link>
<description><![CDATA[
Angiotensin-converting enzyme 2 (ACE2) maintains cardiovascular and renal homeostasis but also serves as the entry receptor for the novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), the causal agent of novel coronavirus disease 2019 (COVID-19)1. COVID-19 disease severity, while highly variable, is typically lower in pediatric patients than adults (particularly the elderly), but increased rates of hospitalizations requiring intensive care are observed in infants than in older children. The reasons for these differences are unknown. To detect potential age-based correlates of disease severity, we measured ACE2 protein expression at the single cell level in human lung tissue specimens from over 100 donors from [~]4 months to 75 years of age. We found that expression of ACE2 in distal lung epithelial cells generally increases with advancing age but exhibits extreme intra- and inter-individual heterogeneity. Notably, we also detected ACE2 expression on neonatal airway epithelial cells and within the lung parenchyma. Similar patterns were found at the transcript level: ACE2 mRNA is expressed in the lung and trachea shortly after birth, downregulated during childhood, and again expressed at high levels in late adulthood in alveolar epithelial cells. Furthermore, we find that apoptosis, which is a natural host defense system against viral infection, is also dynamically regulated during lung maturation, resulting in periods of heightened apoptotic priming and dependence on pro-survival BCL-2 family proteins including MCL-1. Infection of human lung cells with SARS-CoV-2 triggers an unfolded protein stress response and upregulation of the endogenous MCL-1 inhibitor Noxa; in juveniles, MCL-1 inhibition is sufficient to trigger apoptosis in lung epithelial cells - this may limit virion production and inflammatory signaling. Overall, we identify strong and distinct correlates of COVID-19 disease severity across lifespan and advance our understanding of the regulation of ACE2 and cell death programs in the mammalian lung. Furthermore, our work provides the framework for potential translation of apoptosis modulating drugs as novel treatments for COVID-19.
]]></description>
<dc:creator>Inde, Z.</dc:creator>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Joshi, G. N.</dc:creator>
<dc:creator>Spetz, J.</dc:creator>
<dc:creator>Fraser, C.</dc:creator>
<dc:creator>Deskin, B.</dc:creator>
<dc:creator>Ghelfi, E.</dc:creator>
<dc:creator>Sodhi, C.</dc:creator>
<dc:creator>Hackam, D.</dc:creator>
<dc:creator>Kobzik, L.</dc:creator>
<dc:creator>Croker, B.</dc:creator>
<dc:creator>Brownfield, D.</dc:creator>
<dc:creator>Jia, H.</dc:creator>
<dc:creator>Sarosiek, K. A.</dc:creator>
<dc:date>2020-09-13</dc:date>
<dc:identifier>doi:10.1101/2020.09.13.276923</dc:identifier>
<dc:title><![CDATA[Age-dependent regulation of SARS-CoV-2 cell entry genes and cell death programs correlates with COVID-19 disease severity]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-09-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/507566v1?rss=1">
<title>
<![CDATA[
Highly multiplexed in situ protein imaging with signal amplification by Immuno-SABER 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/507566v1?rss=1"
</link>
<description><![CDATA[
Probing the molecular organization of tissues requires in situ analysis by microscopy. However current limitations in multiplexing, sensitivity, and throughput collectively constitute a major barrier for comprehensive single-cell profiling of proteins. Here, we report Immunostaining with Signal Amplification By Exchange Reaction (Immuno-SABER), a rapid, highly multiplexed signal amplification method that simultaneously tackles these key challenges. Immuno-SABER utilizes DNA-barcoded antibodies and provides a method for highly multiplexed signal amplification via modular orthogonal DNA concatemers generated by Primer Exchange Reaction. This approach offers the capability to preprogram and control the amplification level independently for multiple targets without in situ enzymatic reactions, and the intrinsic scalability to rapidly amplify and image a large number of protein targets. We validated our approach in diverse sample types including cultured cells, cryosections, FFPE sections, and whole mount tissues. We demonstrated independently tunable 5-180-fold amplification for multiple targets, covering the full signal range conventionally achieved by secondary antibodies to tyramide signal amplification, as well as simultaneous signal amplification for 10 different proteins using standard equipment and workflow. We further combined Immuno-SABER with Expansion Microscopy to enable rapid and highly multiplexed super-resolution tissue imaging. Overall, Immuno-SABER presents an effective and accessible platform for rapid, multiplexed imaging of proteins across scales with high sensitivity.
]]></description>
<dc:creator>Saka, S. K.</dc:creator>
<dc:creator>Wang, Y.</dc:creator>
<dc:creator>Kishi, J. Y.</dc:creator>
<dc:creator>Zhu, A.</dc:creator>
<dc:creator>Zeng, Y.</dc:creator>
<dc:creator>Xie, W.</dc:creator>
<dc:creator>Kirli, K.</dc:creator>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Cicconet, M.</dc:creator>
<dc:creator>Beliveau, B. J.</dc:creator>
<dc:creator>Lapan, S. W.</dc:creator>
<dc:creator>Yin, S.</dc:creator>
<dc:creator>Lin, M.</dc:creator>
<dc:creator>Boyden, E. S.</dc:creator>
<dc:creator>Kaeser, P. S.</dc:creator>
<dc:creator>Pihan, G.</dc:creator>
<dc:creator>Church, G. M.</dc:creator>
<dc:creator>Yin, P.</dc:creator>
<dc:date>2018-12-28</dc:date>
<dc:identifier>doi:10.1101/507566</dc:identifier>
<dc:title><![CDATA[Highly multiplexed in situ protein imaging with signal amplification by Immuno-SABER]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-12-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.08.17.456616v1?rss=1">
<title>
<![CDATA[
STonKGs: A Sophisticated Transformer Trained on Biomedical Text and Knowledge Graphs 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.08.17.456616v1?rss=1"
</link>
<description><![CDATA[
The majority of biomedical knowledge is stored in structured databases or as unstructured text in scientific publications. This vast amount of information has led to numerous machine learning-based biological applications using either text through natural language processing (NLP) or structured data through knowledge graph embedding models (KGEMs). However, representations based on a single modality are inherently limited. To generate better representations of biological knowledge, we propose STonKGs, a Sophisticated Transformer trained on biomedical text and Knowledge Graphs. This multimodal Transformer uses combined input sequences of structured information from KGs and unstructured text data from biomedical literature to learn joint representations. First, we pre-trained STonKGs on a knowledge base assembled by the Integrated Network and Dynamical Reasoning Assembler (INDRA) consisting of millions of text-triple pairs extracted from biomedical literature by multiple NLP systems. Then, we benchmarked STonKGs against two baseline models trained on either one of the modalities (i.e., text or KG) across eight different classification tasks, each corresponding to a different biological application. Our results demonstrate that STonKGs outperforms both baselines, especially on the more challenging tasks with respect to the number of classes, improving upon the F1-score of the best baseline by up to 0.083. Additionally, our pre-trained model as well as the model architecture can be adapted to various other transfer learning applications. Finally, the source code and pre-trained STonKGs models are available at https://github.com/stonkgs/stonkgs and https://huggingface.co/stonkgs/stonkgs-150k.
]]></description>
<dc:creator>Balabin, H.</dc:creator>
<dc:creator>Hoyt, C. T.</dc:creator>
<dc:creator>Birkenbihl, C.</dc:creator>
<dc:creator>Gyori, B. M.</dc:creator>
<dc:creator>Bachman, J. A.</dc:creator>
<dc:creator>Tom Kodamullil, A.</dc:creator>
<dc:creator>Ploeger, P. G.</dc:creator>
<dc:creator>Hofmann-Apitius, M.</dc:creator>
<dc:creator>Domingo-Fernandez, D.</dc:creator>
<dc:date>2021-08-18</dc:date>
<dc:identifier>doi:10.1101/2021.08.17.456616</dc:identifier>
<dc:title><![CDATA[STonKGs: A Sophisticated Transformer Trained on Biomedical Text and Knowledge Graphs]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-08-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/594085v1?rss=1">
<title>
<![CDATA[
Crosstalk and Ultrasensitivity in Protein Degradation Pathways 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/594085v1?rss=1"
</link>
<description><![CDATA[
Protein turnover is vital to protein homeostasis within the cell. Many proteins are degraded efficiently only after they have been post-translationally "tagged" with a polyubiquitin chain. Ubiquitylation is a form of Post-Translational Modification (PTM): addition of a ubiquitin to the chain is catalyzed by E3 ligases, and removal of ubiquitin is catalyzed by a De-UBiquitylating enzyme (DUB). Over three decades ago, Goldbeter and Koshland discovered that reversible PTM cycles function like on-off switches when the substrates are at saturating concentrations. Although this finding has had profound implications for the understanding of switch-like behavior in biochemical networks, the general behavior of PTM cycles subject to synthesis and degradation has not been studied. Using a mathematical modeling approach, we found that simply introducing protein turnover to a standard modification cycle has profound effects, including significantly reducing the switch-like nature of the response. Our findings suggest that many classic results on PTM cycles may not hold in vivo where protein turnover is ubiquitous. We also found that proteins sharing an E3 ligase can have closely related changes in their expression levels. These results imply that it may be difficult to interpret experimental results obtained from either overexpressing or knocking down protein levels, since changes in protein expression can be coupled via E3 ligase crosstalk. Understanding crosstalk and competition for E3 ligases will be key to ultimately developing a global picture of protein homeostasis.
]]></description>
<dc:creator>Mallela, A.</dc:creator>
<dc:creator>Nariya, M. K.</dc:creator>
<dc:creator>Deeds, E. J.</dc:creator>
<dc:date>2019-03-30</dc:date>
<dc:identifier>doi:10.1101/594085</dc:identifier>
<dc:title><![CDATA[Crosstalk and Ultrasensitivity in Protein Degradation Pathways]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-03-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.07.08.499378v1?rss=1">
<title>
<![CDATA[
The Bioregistry: Unifying the Identification of Biomedical Entities through an Integrative, Open, Community-driven Metaregistry 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.07.08.499378v1?rss=1"
</link>
<description><![CDATA[
The standardized identification of biomedical entities is a cornerstone of interoperability, reuse, and data integration in the life sciences. Several registries have been developed to catalog resources maintaining identifiers for biomedical entities such as small molecules, proteins, cell lines, and clinical trials. However, existing registries have struggled to provide sufficient coverage and metadata standards that meet the evolving needs of modern life sciences researchers. Here, we introduce the Bioregistry, an integrative, open, community-driven metaregistry that synthesizes and substantially expands upon 23 existing registries. The Bioregistry addresses the need for a sustainable registry by leveraging public infrastructure and automation, and employing a progressive governance model centered around open code and open data to foster community contribution. The Bioregistry can be used to support the standardized annotation of data, models, ontologies, and scientific literature, thereby promoting their interoperability and reuse. The Bioregistry can be accessed through https://bioregistry.io and its source code and data are available under the MIT and CC0 Licenses at https://github.com/biopragmatics/bioregistry.
]]></description>
<dc:creator>Hoyt, C. T.</dc:creator>
<dc:creator>Balk, M.</dc:creator>
<dc:creator>Callahan, T. J.</dc:creator>
<dc:creator>Domingo-Fernandez, D.</dc:creator>
<dc:creator>Haendel, M. A.</dc:creator>
<dc:creator>Hegde, H. B.</dc:creator>
<dc:creator>Himmelstein, D. S.</dc:creator>
<dc:creator>Karis, K.</dc:creator>
<dc:creator>Kunze, J.</dc:creator>
<dc:creator>Lubiana, T.</dc:creator>
<dc:creator>Matentzoglu, N.</dc:creator>
<dc:creator>McMurry, J.</dc:creator>
<dc:creator>Moxon, S.</dc:creator>
<dc:creator>Mungall, C. J.</dc:creator>
<dc:creator>Rutz, A.</dc:creator>
<dc:creator>Unni, D. R.</dc:creator>
<dc:creator>Willighagen, E.</dc:creator>
<dc:creator>Winston, D.</dc:creator>
<dc:creator>Gyori, B. M.</dc:creator>
<dc:date>2022-07-10</dc:date>
<dc:identifier>doi:10.1101/2022.07.08.499378</dc:identifier>
<dc:title><![CDATA[The Bioregistry: Unifying the Identification of Biomedical Entities through an Integrative, Open, Community-driven Metaregistry]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-07-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.08.30.505688v1?rss=1">
<title>
<![CDATA[
Automated assembly of molecular mechanisms at scale from text mining and curated databases 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.08.30.505688v1?rss=1"
</link>
<description><![CDATA[
The analysis of  omic data depends heavily on machine-readable information about protein interactions, modifications, and activities. Key resources include protein interaction networks, databases of post-translational modifications, and curated models of gene and protein function. Software systems that read primary literature can potentially extend and update such resources while reducing the burden on human curators, but machine-reading software systems have a high error rate. Here we describe an approach to precisely assemble molecular mechanisms at scale using natural language processing systems and the Integrated Network and Dynamical Reasoning Assembler (INDRA). INDRA identifies overlaps and redundancies in information extracted from published papers and pathway databases and uses probability models to reduce machine reading errors. INDRA enables the automated creation of high-quality, non-redundant corpora for use in data analysis and causal modeling. We demonstrate the use of INDRA in extending protein-protein interaction databases and explaining co-dependencies in the Cancer Dependency Map.
]]></description>
<dc:creator>Bachman, J. A.</dc:creator>
<dc:creator>Gyori, B. M.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2022-08-31</dc:date>
<dc:identifier>doi:10.1101/2022.08.30.505688</dc:identifier>
<dc:title><![CDATA[Automated assembly of molecular mechanisms at scale from text mining and curated databases]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-08-31</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.08.18.504436v1?rss=1">
<title>
<![CDATA[
A Web-based Software Resource for Interactive Analysis of Multiplex Tissue Imaging Datasets 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.08.18.504436v1?rss=1"
</link>
<description><![CDATA[
Highly multiplexed tissue imaging (MTI) are powerful spatial proteomics technologies that enable in situ single-cell characterization of tissues. However, analysis and visualization of MTI datasets remains challenging, and we developed the Galaxy-ME software hub to address this challenge.Galaxy-ME is a web-based, interactive software hub that enables end-to-end analysis and visualization of MTI datasets and is accessible to everyone. To demonstrate its utility, Galaxy-ME was used to analyze datasets obtained from multiple MTI assays and evaluate assay concordance in both normal and cancerous tissues. Galaxy-ME is a publicly available web resource.
]]></description>
<dc:creator>Creason, A. L.</dc:creator>
<dc:creator>Watson, C.</dc:creator>
<dc:creator>Gu, Q.</dc:creator>
<dc:creator>Persson, D.</dc:creator>
<dc:creator>Sargent, L. L.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Lin, J.-R.</dc:creator>
<dc:creator>Sivagnanam, S.</dc:creator>
<dc:creator>Wünnemann, F.</dc:creator>
<dc:creator>Nirmal, A. J.</dc:creator>
<dc:creator>Chin, K.</dc:creator>
<dc:creator>Feiler, H. S.</dc:creator>
<dc:creator>Coussens, L. M.</dc:creator>
<dc:creator>Schapiro, D.</dc:creator>
<dc:creator>Grüning, B. A.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Sokolov, A.</dc:creator>
<dc:creator>Goecks, J.</dc:creator>
<dc:date>2022-08-19</dc:date>
<dc:identifier>doi:10.1101/2022.08.18.504436</dc:identifier>
<dc:title><![CDATA[A Web-based Software Resource for Interactive Analysis of Multiplex Tissue Imaging Datasets]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-08-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.08.11.503237v1?rss=1">
<title>
<![CDATA[
Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.08.11.503237v1?rss=1"
</link>
<description><![CDATA[
Lymphocytes play a key role in immune surveillance of tumors, but our understanding of the spatial organization and physical interactions that facilitate lymphocyte anti-cancer functions is limited. Here, we used multiplexed imaging, quantitative spatial analysis, and machine learning to create high-definition maps of tumor-bearing lung tissues from a Kras/p53 (KP) mouse model and human resections. Networks of directly interacting lymphocytes ( lymphonets) emerge as a distinctive feature of the anti-cancer immune response. Lymphonets nucleate from small T-cell clusters and incorporate B cells with increasing size. CXCR3-mediated trafficking modulates lymphonet size and number, but neoantigen expression directs intratumoral localization. Lymphonets preferentially harbor TCF1+/PD1+ progenitor CD8 T cells involved in responses to immune checkpoint blockade (ICB). Upon treatment of mice with ICB therapy or a neoantigen-targeted vaccine, lymphonets retain progenitor and gain cytotoxic CD8 T-cell populations, likely via progenitor differentiation. These data show that lymphonets create a spatial environment supportive of CD8 T-cell anti-tumor responses.
]]></description>
<dc:creator>Gaglia, G.</dc:creator>
<dc:creator>Burger, M.</dc:creator>
<dc:creator>Ritch, C. C.</dc:creator>
<dc:creator>Rammos, D.</dc:creator>
<dc:creator>Dai, Y.</dc:creator>
<dc:creator>Crossland, G. E.</dc:creator>
<dc:creator>Tavana, S. Z.</dc:creator>
<dc:creator>Warchol, S.</dc:creator>
<dc:creator>Jaeger, A. M.</dc:creator>
<dc:creator>Naranjo, S.</dc:creator>
<dc:creator>Coy, S.</dc:creator>
<dc:creator>Johnson, A.</dc:creator>
<dc:creator>Krueger, R.</dc:creator>
<dc:creator>Lin, J.-R.</dc:creator>
<dc:creator>Pfister, H.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Jacks, T.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:date>2022-08-13</dc:date>
<dc:identifier>doi:10.1101/2022.08.11.503237</dc:identifier>
<dc:title><![CDATA[Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-08-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.09.07.507020v1?rss=1">
<title>
<![CDATA[
Paired evaluation defines performance landscapes for machine learning models 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.09.07.507020v1?rss=1"
</link>
<description><![CDATA[
The true accuracy of a machine learning model is a population-level statistic that cannot be observed directly. In practice, predictor performance is estimated against one or more test datasets, and the accuracy of this estimate strongly depends on how well the test sets represent all possible unseen datasets. Here we present paired evaluation, a simple approach for increasing the robustness of performance evaluation by systematic pairing of test samples, and use it to evaluate predictors of drug response in breast cancer cell lines and of disease severity in patients with Alzheimers Disease. Our results demonstrate that the choice of test data can cause estimates of performance to vary by as much as 30%, and that paired evaluation makes it possible to identify outliers, improve the accuracy of performance estimates in the presence of known confounders, and assign statistical significance when comparing machine learning models.
]]></description>
<dc:creator>Nariya, M. K.</dc:creator>
<dc:creator>Mills, C. E.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Sokolov, A.</dc:creator>
<dc:date>2022-09-12</dc:date>
<dc:identifier>doi:10.1101/2022.09.07.507020</dc:identifier>
<dc:title><![CDATA[Paired evaluation defines performance landscapes for machine learning models]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-09-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.09.28.509927v1?rss=1">
<title>
<![CDATA[
Multi-modal digital pathology for colorectal cancer diagnosis by high-plex immunofluorescence imaging and traditional histology of the same tissue section 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.09.28.509927v1?rss=1"
</link>
<description><![CDATA[
Precision medicine is critically dependent on better methods for diagnosing and staging disease and predicting drug response. Histopathology using Hematoxylin and Eosin (H&E) stained tissue - not genomics - remains the primary diagnostic method in cancer. Recently developed highly-multiplexed tissue imaging methods promise to enhance research studies and clinical practice with precise, spatially-resolved, single-cell data. Here we describe the "Orion" platform for collecting and analyzing H&E and high-plex immunofluorescence (IF) images from the same cells in a whole-slide format suitable for diagnosis. Using a retrospective cohort of 74 colorectal cancer resections, we show that IF and H&E images provide human experts and machine learning algorithms with complementary information that can be used to generate interpretable, multiplexed image-based models predictive of progression-free survival. Combining models of immune infiltration and tumor-intrinsic features achieves a hazard ratio of [~]0.05, demonstrating the ability of multi-modal Orion imaging to generate high-performance biomarkers.
]]></description>
<dc:creator>Lin, J.-R.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Campton, D.</dc:creator>
<dc:creator>Cooper, J.</dc:creator>
<dc:creator>Coy, S.</dc:creator>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Tefft, J.</dc:creator>
<dc:creator>McCarty, E.</dc:creator>
<dc:creator>Ligon, K.</dc:creator>
<dc:creator>Rodig, S. J.</dc:creator>
<dc:creator>Reese, S.</dc:creator>
<dc:creator>George, T.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2022-09-30</dc:date>
<dc:identifier>doi:10.1101/2022.09.28.509927</dc:identifier>
<dc:title><![CDATA[Multi-modal digital pathology for colorectal cancer diagnosis by high-plex immunofluorescence imaging and traditional histology of the same tissue section]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-09-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.10.06.511142v1?rss=1">
<title>
<![CDATA[
Multi range ERK responses shape the proliferative trajectory of single cells following oncogene induced senescence 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.10.06.511142v1?rss=1"
</link>
<description><![CDATA[
Oncogene-induced senescence (OIS) is a phenomenon in which aberrant oncogene expression causes non-transformed cells to enter a non-proliferative state. Cells undergoing OIS display phenotypic heterogeneity, with some cells senescing and others remaining proliferative. The causes of the heterogeneity remain poorly understood. We studied the sources of heterogeneity in the responses of human epithelial cells to oncogenic BRAFV600E expression. We found that a narrow expression range of BRAFV600E generated a wide range of activities of its downstream effector ERK. In population-level and single cell assays, ERK activity displayed a non-monotonic relationship to proliferation, with intermediate ERK activities leading to maximal proliferation. We profiled gene expression across a range of ERK activities over time and characterized four distinct ERK response classes, which we propose act in concert to generate the unique ERK-proliferation response. Altogether, our studies mapped the input-output relationships between ERK activity and proliferation providing important insights into how heterogeneity can be generated during OIS.
]]></description>
<dc:creator>Chen, J.-Y.</dc:creator>
<dc:creator>Hug, C.</dc:creator>
<dc:creator>Reyes, J.</dc:creator>
<dc:creator>Tian, C.</dc:creator>
<dc:creator>Gerosa, L.</dc:creator>
<dc:creator>Fröhlich, F.</dc:creator>
<dc:creator>Ponsioen, B.</dc:creator>
<dc:creator>Snippert, H. J. G.</dc:creator>
<dc:creator>Spencer, S. L.</dc:creator>
<dc:creator>Jambhekar, A.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Lahav, G.</dc:creator>
<dc:date>2022-10-07</dc:date>
<dc:identifier>doi:10.1101/2022.10.06.511142</dc:identifier>
<dc:title><![CDATA[Multi range ERK responses shape the proliferative trajectory of single cells following oncogene induced senescence]]></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/2022.08.19.504558v1?rss=1">
<title>
<![CDATA[
Resolving deleterious and near-neutral effects requires different pooled fitness assay designs 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.08.19.504558v1?rss=1"
</link>
<description><![CDATA[
Pooled sequencing-based fitness assays are a powerful and widely used approach to quantifying fitness of thousands of genetic variants in parallel. Despite the throughput of such assays, they are prone to biases in fitness estimates, and errors in measurements are typically larger for deleterious fitness effects, relative to neutral effects. In practice, designing pooled fitness assays involves tradeoffs between the number of timepoints, the sequencing depth, and other parameters to gain as much information as possible within a feasible experiment. Here, we combined theory, simulations, and reanalysis of an existing experimental dataset to explore how assay parameters impact measurements of near-neutral and deleterious fitness effects. We found that sequencing multiple timepoints at relatively modest depth improved estimates of near-neutral fitness effects, but systematically biased measurements of deleterious effects. We identified a theoretical lower bound for estimates from bulk fitness assays, and showed that increasing sequencing depth, and reducing number of timepoints improved resolution of deleterious fitness effects. Our results highlight a tradeoff between measurement of deleterious and near-neutral effect sizes for a fixed amount of data and suggest that fitness assay design should be tuned for fitness effects that are relevant to the specific biological question.
]]></description>
<dc:creator>Limdi, A.</dc:creator>
<dc:creator>Baym, M.</dc:creator>
<dc:date>2022-08-20</dc:date>
<dc:identifier>doi:10.1101/2022.08.19.504558</dc:identifier>
<dc:title><![CDATA[Resolving deleterious and near-neutral effects requires different pooled fitness assay designs]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-08-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.05.17.492023v1?rss=1">
<title>
<![CDATA[
Parallel changes in gene essentiality over 50,000 generations of evolution 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.05.17.492023v1?rss=1"
</link>
<description><![CDATA[
As evolving populations accumulate mutations, the benefits and costs of subsequent mutations change. As fitness increases, the relative benefit of new mutations typically decreases. However, the question remains whether deleterious mutations tend to have larger or smaller costs as a population adapts; theory and experiments provide support for both conflicting hypotheses. To address this question, we compared the effects of insertion mutations in every gene in Escherichia coli between ancestral and 12 independently derived strains after 50,000 generations in a uniform environment. We found both increases and decreases in the fitness costs of mutations, leaving the overall distribution of effects largely unchanged. However, at the extreme, more genes became essential over evolution than vice versa. Both changes in fitness effects and essentiality evolved in parallel across the independent populations, and most changes were not explained by structural variation or altered gene expression. Thus, the macroscopic features of the local fitness landscape remained largely unchanged, even as access to particular evolutionary trajectories changed consistently during adaptation to the experimental environment.

One Sentence SummaryLimdi et al. report parallel changes in the cost of mutations in replicate lineages of a decades-long E. coli evolution experiment.
]]></description>
<dc:creator>Limdi, A.</dc:creator>
<dc:creator>Owen, S. V.</dc:creator>
<dc:creator>Herren, C.</dc:creator>
<dc:creator>Lenski, R. E.</dc:creator>
<dc:creator>Baym, M.</dc:creator>
<dc:date>2022-05-17</dc:date>
<dc:identifier>doi:10.1101/2022.05.17.492023</dc:identifier>
<dc:title><![CDATA[Parallel changes in gene essentiality over 50,000 generations of evolution]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-05-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.04.05.438396v1?rss=1">
<title>
<![CDATA[
Decreased thermal tolerance as a trade-off of antibiotic resistance 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.04.05.438396v1?rss=1"
</link>
<description><![CDATA[
Evolutionary theory predicts that adaptations, including antibiotic resistance, should come with associated fitness costs; yet, many resistance mutations seemingly contradict this prediction by inducing no growth rate deficit. However, most growth assays comparing sensitive and resistant strains have been performed under a narrow range of environmental conditions, which do not reflect the variety of contexts that a pathogenic bacterium might encounter when causing infection. We hypothesized that reduced niche breadth, defined as diminished growth across a diversity of environments, can be a cost of antibiotic resistance. Specifically, we test whether chloramphenicol-resistant Escherichia coli incur disproportionate growth deficits in novel thermal conditions. Here we show that chloramphenicol-resistant bacteria have greater fitness costs at novel temperatures than their antibiotic-sensitive ancestors. In several cases, we observed no resistance cost in growth rate at the historic temperature but saw diminished growth at warmer and colder temperatures. These results were consistent across various genetic mechanisms of resistance. Thus, we propose that decreased thermal niche breadth is an under-documented fitness cost of antibiotic resistance. Furthermore, these results demonstrate that the cost of antibiotic resistance shifts rapidly as the environment changes; these context-dependent resistance costs should select for the rapid gain and loss of resistance as an evolutionary strategy.
]]></description>
<dc:creator>Herren, C. M.</dc:creator>
<dc:creator>Baym, M.</dc:creator>
<dc:date>2021-04-05</dc:date>
<dc:identifier>doi:10.1101/2021.04.05.438396</dc:identifier>
<dc:title><![CDATA[Decreased thermal tolerance as a trade-off of antibiotic resistance]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-04-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.07.08.499367v1?rss=1">
<title>
<![CDATA[
Swapped genetic code blocks viral infections and gene transfer 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.07.08.499367v1?rss=1"
</link>
<description><![CDATA[
Removing cellular transfer RNAs (tRNAs), making their cognate codons unreadable, creates a genetic firewall that prevents viral replication and horizontal gene transfer. However, numerous viruses and mobile genetic elements encode parts of the translational apparatus, including tRNAs, potentially rendering a genetic-code-based firewall ineffective. In this paper, we show that such horizontally transferred tRNA genes can enable viral replication in Escherichia coli cells despite the genome-wide lack of three codons and the previously essential cognate tRNAs and release factor 1. By repurposing viral tRNAs, we then develop recoded cells bearing an amino-acid-swapped genetic code that reassigns two of the six serine codons to leucine during translation. This amino-acid-swapped genetic code renders cells completely resistant to viral infections by mistranslating viral proteomes and prevents the escape of synthetic genetic information by engineered reliance on serine codons to produce leucine-requiring proteins. Finally, we also repurpose the third free codon to biocontain this virus-resistant host via dependence on an amino acid not found in nature.
]]></description>
<dc:creator>Nyerges, A.</dc:creator>
<dc:creator>Vinke, S.</dc:creator>
<dc:creator>Flynn, R.</dc:creator>
<dc:creator>Owen, S. V.</dc:creator>
<dc:creator>Rand, E. A.</dc:creator>
<dc:creator>Budnik, B.</dc:creator>
<dc:creator>Keen, E.</dc:creator>
<dc:creator>Narasimhan, K.</dc:creator>
<dc:creator>Marchand, J. A.</dc:creator>
<dc:creator>Baas-Thomas, M.</dc:creator>
<dc:creator>Liu, M.</dc:creator>
<dc:creator>Chen, K.</dc:creator>
<dc:creator>Chiappino-Pepe, A.</dc:creator>
<dc:creator>Hu, F.</dc:creator>
<dc:creator>Baym, M.</dc:creator>
<dc:creator>Church, G. M.</dc:creator>
<dc:date>2022-07-10</dc:date>
<dc:identifier>doi:10.1101/2022.07.08.499367</dc:identifier>
<dc:title><![CDATA[Swapped genetic code blocks viral infections and gene transfer]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-07-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.01.12.903443v1?rss=1">
<title>
<![CDATA[
Simplitigs as an efficient and scalable representation of de Bruijn graphs 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.01.12.903443v1?rss=1"
</link>
<description><![CDATA[
De Bruijn graphs play an essential role in computational biology. However, despite their widespread use, they lack a universal scalable representation suitable for different types of genomic data sets. Here, we introduce simplitigs as a compact, efficient and scalable representation and present a fast algorithm for their computation. On examples of several model organisms and two bacterial pan-genomes, we show that, compared to the best existing representation, simplitigs provide a substantial improvement in the cumulative sequence length and their number, especially for graphs with many branching nodes. We demonstrate that this improvement is amplified with more data available. Combined with the commonly used Burrows-Wheeler Transform index of genomic sequences, simplitigs substantially reduce both memory and index loading and query times, as illustrated with large-scale examples of GenBank bacterial pan-genomes.
]]></description>
<dc:creator>Brinda, K.</dc:creator>
<dc:creator>Baym, M.</dc:creator>
<dc:creator>Kucherov, G.</dc:creator>
<dc:date>2020-01-12</dc:date>
<dc:identifier>doi:10.1101/2020.01.12.903443</dc:identifier>
<dc:title><![CDATA[Simplitigs as an efficient and scalable representation of de Bruijn graphs]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-01-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.05.24.445430v1?rss=1">
<title>
<![CDATA[
The gut bacterial natural product colibactin triggers induction of latent viruses in diverse bacteria 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.05.24.445430v1?rss=1"
</link>
<description><![CDATA[
Colibactin is a chemically unstable small molecule genotoxin produced by multiple different bacteria, including members of the human gut microbiome.1,2 While the biological activity of colibactin has been extensively investigated in mammalian systems,3 little is known about its effects on other microorganisms. Here, we discover that colibactin targets bacteria carrying prophages, inducing lytic development via the bacterial SOS response. DNA, added exogenously, protects bacteria from colibactin, as does expressing a colibactin resistance protein (ClbS) in non-colibactin-producing cells. The prophage-inducing effects we observe apply broadly across taxonomically diverse phage-bacteria systems. Finally, we identify bacteria that possess colibactin resistance genes but lack colibactin biosynthetic genes. Many of these bacteria are infected with predicted prophages, and we show that the expression of their ClbS homologs provides immunity from colibactin-triggered induction. Our study reveals a mechanism by which colibactin production could impact microbiomes and highlights an underappreciated role for microbial natural products in influencing population-level events such as phage outbreaks.
]]></description>
<dc:creator>Silpe, J. E.</dc:creator>
<dc:creator>Wong, J. W. H.</dc:creator>
<dc:creator>Owen, S. V.</dc:creator>
<dc:creator>Baym, M.</dc:creator>
<dc:creator>Balskus, E. P.</dc:creator>
<dc:date>2021-05-24</dc:date>
<dc:identifier>doi:10.1101/2021.05.24.445430</dc:identifier>
<dc:title><![CDATA[The gut bacterial natural product colibactin triggers induction of latent viruses in diverse bacteria]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-05-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/261750v1?rss=1">
<title>
<![CDATA[
Stronger connectivity of the resident gut microbiome lends resistance to invading bacteria 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/261750v1?rss=1"
</link>
<description><![CDATA[
Bacterial infection in the gut is often due to successful invasion of the host microbiome by an introduced pathogen. Ecological theory indicates that resident community members and their interactions should be strong determinants of whether an invading taxon can persist in a community. In the context of the gut microbiome, this suggests colonization resistance against newly introduced bacteria should depend on the instantaneous bacterial community composition within the gut and interactions between these constituent members. Here we develop a mathematical model of how metabolite-dependent biotic interactions between resident bacteria mediate invasion, and find that stronger biotic connectivity from metabolite cross-feeding and competition increases colonization resistance. We then introduce a statistical method for identifying invasive taxa in the human gut, and show empirically that greater connectivity of the resident gut microbiome is related to increased resistance to invading bacteria. Finally, we examined patient outcomes after fecal microbiota transplant (FMT) for recurring Clostridium difficile infection. Patients with lower connectivity of the gut microbiome after treatment were more likely to relapse, experiencing a later infection. Thus, simulation models and data from human subjects support the hypothesis that stronger interactions between bacteria in the gut repel invaders. These results demonstrate how ecological invasion theory can be applied to the gut microbiome, which might inform targeted microbiome manipulations and interventions. More broadly, this study provides evidence that low connectivity in gut microbial communities is a hallmark of community instability and susceptibility to invasion.
]]></description>
<dc:creator>Herren, C.</dc:creator>
<dc:creator>Baym, M.</dc:creator>
<dc:date>2018-02-08</dc:date>
<dc:identifier>doi:10.1101/261750</dc:identifier>
<dc:title><![CDATA[Stronger connectivity of the resident gut microbiome lends resistance to invading bacteria]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-02-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.07.13.199331v1?rss=1">
<title>
<![CDATA[
Prophage-encoded phage defence proteins with cognate self-immunity 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.07.13.199331v1?rss=1"
</link>
<description><![CDATA[
Temperate phages are pervasive in bacterial genomes, existing as vertically-inherited islands called prophages. Prophages are vulnerable to the predation of their host bacterium by exogenous phages. Here we identify BstA, a novel family of prophage-encoded phage defense proteins found in diverse Gram-negative bacteria. BstA drives potent suppression of phage epidemics through abortive infection. During lytic replication, the bstA-encoding prophage is not itself inhibited by BstA due to a self-immunity mechanism conferred by the anti-BstA (aba) element, a short stretch of DNA within the bstA locus. Inhibition of phage replication by distinct BstA proteins from Salmonella, Klebsiella and Escherichia prophages is functionally interchangeable, but each possesses a cognate aba element. The specificity of the aba element ensures that immunity is exclusive to the replicating prophage, and cannot be exploited by heterologous BstA-encoding phages. BstA allows prophages to defend host cells against exogenous phage attack, without sacrificing their own lytic autonomy.
]]></description>
<dc:creator>Owen, S. V.</dc:creator>
<dc:creator>Wenner, N.</dc:creator>
<dc:creator>Dulberger, C. L.</dc:creator>
<dc:creator>Rodwell, E. V.</dc:creator>
<dc:creator>Bowers-Barnard, A.</dc:creator>
<dc:creator>Quinones-Olvera, N.</dc:creator>
<dc:creator>Rigden, D. J.</dc:creator>
<dc:creator>Rubin, E. J.</dc:creator>
<dc:creator>Garner, E. C.</dc:creator>
<dc:creator>Baym, M.</dc:creator>
<dc:creator>Hinton, J. C. D.</dc:creator>
<dc:date>2020-07-13</dc:date>
<dc:identifier>doi:10.1101/2020.07.13.199331</dc:identifier>
<dc:title><![CDATA[Prophage-encoded phage defence proteins with cognate self-immunity]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-07-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/403204v1?rss=1">
<title>
<![CDATA[
Lineage calling can identify antibiotic resistant clones within minutes 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/403204v1?rss=1"
</link>
<description><![CDATA[
Surveillance of drug-resistant bacteria is essential for healthcare providers to deliver effective empiric antibiotic therapy. However, traditional molecular epidemiology does not typically occur on a timescale that could impact patient treatment and outcomes. Here we present a method called  genomic neighbor typing for inferring the phenotype of a bacterial sample by identifying its closest relatives in a database of genomes with metadata. We show that this technique can infer antibiotic susceptibility and resistance for both S. pneumoniae and N. gonorrhoeae. We implemented this with rapid k-mer matching, which, when used on Oxford Nanopore MinION data, can run in real time. This resulted in determination of resistance within ten minutes (sens/spec 91%/100% for S. pneumoniae and 81%/100% N. gonorrhoeae from isolates with a representative database) of sequencing starting, and for clinical metagenomic sputum samples (75%/100% for S. pneumoniae), within four hours of sample collection. This flexible approach has wide application to pathogen surveillance and may be used to greatly accelerate appropriate empirical antibiotic treatment.
]]></description>
<dc:creator>Brinda, K.</dc:creator>
<dc:creator>Callendrello, A.</dc:creator>
<dc:creator>Cowley, L.</dc:creator>
<dc:creator>Charalampous, T.</dc:creator>
<dc:creator>Lee, R. S.</dc:creator>
<dc:creator>MacFadden, D. R.</dc:creator>
<dc:creator>Kucherov, G.</dc:creator>
<dc:creator>O'Grady, J.</dc:creator>
<dc:creator>Baym, M.</dc:creator>
<dc:creator>Hanage, W. P.</dc:creator>
<dc:date>2018-08-29</dc:date>
<dc:identifier>doi:10.1101/403204</dc:identifier>
<dc:title><![CDATA[Lineage calling can identify antibiotic resistant clones within minutes]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-08-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.08.08.503176v1?rss=1">
<title>
<![CDATA[
Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.08.08.503176v1?rss=1"
</link>
<description><![CDATA[
Dynamical models in the form of systems of ordinary differential equations have become a standard tool in systems biology. Many parameters of such models are usually unknown and have to be inferred from experimental data. Gradient-based optimization has proven to be effective for parameter estimation. However, computing gradients becomes increasingly costly for larger models, which are required for capturing the complex interactions of multiple biochemical pathways. Adjoint sensitivity analysis has been pivotal for working with such large models, but methods tailored for steady-state data are currently not available. We propose a new adjoint method for computing gradients, which is applicable if the experimental data include steady-state measurements. The method is based on a reformulation of the backward integration problem to a system of linear algebraic equations. The evaluation of the proposed method using real-world problems shows a speedup of total simulation time by a factor of up to 4.4. Our results demonstrate that the proposed approach can achieve a substantial improvement in computation time, in particular for large-scale models, where computational efficiency is critical.

Author summaryLarge-scale dynamical models are nowadays widely used for the analysis of complex processes and the integration of large-scale data sets. However, computational cost is often a bottleneck. Here, we propose a new gradient computation method that facilitates the parameterization of large-scale models based on steady-state measurements. The method can be combined with existing gradient computation methods for time-course measurements. Accordingly, it is an essential contribution to the environment of computationally efficient approaches for the study of large-scale screening and omics data, but not tailored to biological applications, and, therefore, also useful beyond the field of computational biology.
]]></description>
<dc:creator>Lakrisenko, P.</dc:creator>
<dc:creator>Stapor, P.</dc:creator>
<dc:creator>Grein, S.</dc:creator>
<dc:creator>Paszkowski, Łukasz</dc:creator>
<dc:creator>Pathirana, D.</dc:creator>
<dc:creator>Fröhlich, F.</dc:creator>
<dc:creator>Lines, G. T.</dc:creator>
<dc:creator>Weindl, D.</dc:creator>
<dc:creator>Hasenauer, J.</dc:creator>
<dc:date>2022-08-11</dc:date>
<dc:identifier>doi:10.1101/2022.08.08.503176</dc:identifier>
<dc:title><![CDATA[Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-08-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.05.02.490362v1?rss=1">
<title>
<![CDATA[
KINOMO: A non-negative matrix factorization framework for recovering intra- and inter-tumoral heterogeneity from single-cell RNA-seq data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.05.02.490362v1?rss=1"
</link>
<description><![CDATA[
Single-cell RNA-sequencing (scRNA-seq) is a powerful technology to uncover cellular heterogeneity in tumor ecosystems. Due to differences in underlying gene load, direct comparison between patient samples is challenging, and this is further complicated by the sparsity of data matrices in scRNA-seq. Here, we present a factorization method called KINOMO (Kernel dIfferentiability correlation-based NOn-negative Matrix factorization algorithm using Kullback-Leibler divergence loss Optimization). This tool uses quadratic approximation approach for error correction and an iterative multiplicative approach, which improves the quality assessment of NMF-identified factorization, while mitigating biases introduced by inter-patient genomic variability. We benchmarked this new approach against nine different methods across 15 scRNA-seq experiments and find that KINOMO outperforms prior methods when evaluated with an adjusted Rand index (ARI), ranging 0.82-0.91 compared to 0.68-0.77. Thus, KINOMO provides an improved approach for determining coherent transcriptional programs (and meta-programs) from scRNA-seq data of cancer tissues, enabling comparison of patients with variable genomic backgrounds.

ClassificationPhysical Sciences (Applied Mathematics; Biophysics and Computational Biology), Biological Sciences (Applied Biological Sciences; Biophysics and Computational Biology; Medical Sciences; Systems Biology.).

Significance StatementIdentification of shared or distinct cell programs in single-cell RNA-seq data of patient cancer cells is challenging due to underlying variability of gene load which determines transcriptional output. We developed an analytical approach to define transcriptional variability more accurately across patients and therefore enable comparison of program expression despite inherent genetic heterogeneity. Thus, this method overcomes challenges not adequately addressed by other methods broadly used for the analysis of single-cell genomics data.
]]></description>
<dc:creator>Tagore, S.</dc:creator>
<dc:creator>Wang, Y.</dc:creator>
<dc:creator>Biermann, J.</dc:creator>
<dc:creator>Rabadan, R.</dc:creator>
<dc:creator>Azizi, E.</dc:creator>
<dc:creator>Izar, B.</dc:creator>
<dc:date>2022-05-04</dc:date>
<dc:identifier>doi:10.1101/2022.05.02.490362</dc:identifier>
<dc:title><![CDATA[KINOMO: A non-negative matrix factorization framework for recovering intra- and inter-tumoral heterogeneity from single-cell RNA-seq data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-05-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.10.24.513552v1?rss=1">
<title>
<![CDATA[
NDEx IQuery: a multi-method network gene set analysis leveraging the Network Data Exchange 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.10.24.513552v1?rss=1"
</link>
<description><![CDATA[
MotivationThe investigation of sets of genes using biological pathways is a common task for researchers and is supported by a wide variety of software tools. This type of analysis generates hypotheses about the biological processes active or modulated in a specific experimental context.

ResultsThe NDEx Integrated Query (IQuery) is a new tool for network and pathway-based gene set interpretation that complements or extends existing resources. It combines novel sources of pathways, integration with Cytoscape, and the ability to store and share analysis results. The IQuery web application performs multiple gene set analyses based on diverse pathways and networks stored in NDEx. These include curated pathways from WikiPathways and SIGNOR, published pathway figures from the last 27 years, machine-assembled networks using the INDRA system, and the new NCI-PID v2.0, an updated version of the popular NCI Pathway Interaction Database. IQuerys integration with MSigDB and cBioPortal now provides pathway analysis in the context of these two resources.

Availability and ImplementationIQuery is available at https://www.ndexbio.org/iquery and is implemented in Javascript and Java.

ContactDexter Pratt (depratt@health.ucsd.edu)
]]></description>
<dc:creator>Pillich, R. T.</dc:creator>
<dc:creator>Chen, J.</dc:creator>
<dc:creator>Churas, C.</dc:creator>
<dc:creator>Fong, D.</dc:creator>
<dc:creator>Gyori, B. M.</dc:creator>
<dc:creator>Ideker, T.</dc:creator>
<dc:creator>Karis, K.</dc:creator>
<dc:creator>Liu, S. N.</dc:creator>
<dc:creator>Ono, K.</dc:creator>
<dc:creator>Pico, A.</dc:creator>
<dc:creator>Pratt, D.</dc:creator>
<dc:date>2022-10-25</dc:date>
<dc:identifier>doi:10.1101/2022.10.24.513552</dc:identifier>
<dc:title><![CDATA[NDEx IQuery: a multi-method network gene set analysis leveraging the Network Data Exchange]]></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/2022.11.20.517210v1?rss=1">
<title>
<![CDATA[
OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.11.20.517210v1?rss=1"
</link>
<description><![CDATA[
AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. These are necessary to (i) tackle new tasks, like protein-ligand complex structure prediction, (ii) investigate the process by which the model learns, which remains poorly understood, and (iii) assess the models generalization capacity to unseen regions of fold space. Here we report OpenFold, a fast, memory-efficient, and trainable implementation of AlphaFold2. We train OpenFold from scratch, fully matching the accuracy of AlphaFold2. Having established parity, we assess OpenFolds capacity to generalize across fold space by retraining it using carefully designed datasets. We find that OpenFold is remarkably robust at generalizing despite extreme reductions in training set size and diversity, including near-complete elisions of classes of secondary structure elements. By analyzing intermediate structures produced by OpenFold during training, we also gain surprising insights into the manner in which the model learns to fold proteins, discovering that spatial dimensions are learned sequentially. Taken together, our studies demonstrate the power and utility of OpenFold, which we believe will prove to be a crucial new resource for the protein modeling community.
]]></description>
<dc:creator>Ahdritz, G.</dc:creator>
<dc:creator>Bouatta, N.</dc:creator>
<dc:creator>Kadyan, S.</dc:creator>
<dc:creator>Xia, Q.</dc:creator>
<dc:creator>Gerecke, W.</dc:creator>
<dc:creator>O'Donnell, T. J.</dc:creator>
<dc:creator>Berenberg, D.</dc:creator>
<dc:creator>Fisk, I.</dc:creator>
<dc:creator>Zanichelli, N.</dc:creator>
<dc:creator>Zhang, B.</dc:creator>
<dc:creator>Nowaczynski, A.</dc:creator>
<dc:creator>Wang, B.</dc:creator>
<dc:creator>Stepniewska-Dziubinska, M. M.</dc:creator>
<dc:creator>Zhang, S.</dc:creator>
<dc:creator>Ojewole, A.</dc:creator>
<dc:creator>Guney, M. E.</dc:creator>
<dc:creator>Biderman, S.</dc:creator>
<dc:creator>Watkins, A. M.</dc:creator>
<dc:creator>Ra, S.</dc:creator>
<dc:creator>Lorenzo, P. R.</dc:creator>
<dc:creator>Nivon, L.</dc:creator>
<dc:creator>Weitzner, B.</dc:creator>
<dc:creator>Ban, Y.-E. A.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Mostaque, E.</dc:creator>
<dc:creator>Zhang, Z.</dc:creator>
<dc:creator>Bonneau, R.</dc:creator>
<dc:creator>AlQuraishi, M.</dc:creator>
<dc:date>2022-11-22</dc:date>
<dc:identifier>doi:10.1101/2022.11.20.517210</dc:identifier>
<dc:title><![CDATA[OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-11-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.12.13.472421v1?rss=1">
<title>
<![CDATA[
Characterization of metabolic compartmentalization in the liver using spatially resolved metabolomics 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.12.13.472421v1?rss=1"
</link>
<description><![CDATA[
Cells adapt their metabolism to physiological stimuli, and metabolic heterogeneity exists between cell types, within tissues, and subcellular compartments. The liver plays an essential role in maintaining whole-body metabolic homeostasis and is structurally defined by metabolic zones. These zones are well-understood on the transcriptomic level, but have not been comprehensively characterized on the metabolomic level. Mass spectrometry imaging (MSI) can be used to map hundreds of metabolites directly from a tissue section, offering an important advance to investigate metabolic heterogeneity in tissues compared to extraction-based metabolomics methods that analyze tissue metabolite profiles in bulk. We established a workflow for the preparation of tissue specimens for matrix-assisted laser desorption/ionization (MALDI) MSI and achieved broad coverage of central carbon, nucleotide, and lipid metabolism pathways. We used this approach to visualize the effect of nutrient stress and excess on liver metabolism. Our data revealed a highly organized metabolic compartmentalization in livers, which becomes disrupted under nutrient stress conditions. Fasting caused changes in glucose metabolism and increased the levels of fatty acids in the circulation. In contrast, a prolonged high-fat diet (HFD) caused lipid accumulation within liver tissues with clear zonal patterns. Fatty livers had higher levels of purine and pentose phosphate related metabolites, which generates reducing equivalents to counteract oxidative stress. This MALDI MSI approach allowed the visualization of liver metabolic compartmentalization at high resolution and can be applied more broadly to yield new insights into metabolic heterogeneity in vivo.
]]></description>
<dc:creator>van der Reest, J.</dc:creator>
<dc:creator>Stopka, S. A.</dc:creator>
<dc:creator>Abdelmoula, W. M.</dc:creator>
<dc:creator>Ruiz, D. F.</dc:creator>
<dc:creator>Joshi, S.</dc:creator>
<dc:creator>Ringel, A.</dc:creator>
<dc:creator>Haigis, M. C.</dc:creator>
<dc:creator>Agar, N. Y. R.</dc:creator>
<dc:date>2021-12-13</dc:date>
<dc:identifier>doi:10.1101/2021.12.13.472421</dc:identifier>
<dc:title><![CDATA[Characterization of metabolic compartmentalization in the liver using spatially resolved metabolomics]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-12-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.01.26.525375v1?rss=1">
<title>
<![CDATA[
A CRISPRi/a screening platform to study cellular nutrient transport in diverse microenvironments 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.01.26.525375v1?rss=1"
</link>
<description><![CDATA[
Blocking the import of nutrients essential for cancer cell proliferation represents a therapeutic opportunity, but it is unclear which transporters to target. Here, we report a CRISPRi/a screening platform to systematically interrogate the contribution of specific nutrient transporters to support cancer cell proliferation in environments ranging from standard culture media to tumor models. We applied this platform to identify the transporters of amino acids in leukemia cells and found that amino acid transport is characterized by high bidirectional flux that is dependent on the composition of the microenvironment. While investigating the role of transporters in cystine starved cells, we uncovered a novel role for serotonin uptake in preventing ferroptosis. Finally, we identified transporters essential for cell proliferation in subcutaneous tumors and found that levels of glucose and amino acids can restrain proliferation in that environment. This study provides a framework for the systematic identification of critical cellular nutrient transporters, characterizing the function of such transporters, and studying how the tumor microenvironment impacts cancer metabolism.
]]></description>
<dc:creator>Chidley, C.</dc:creator>
<dc:creator>Darnell, A. M.</dc:creator>
<dc:creator>Gaudio, B. L.</dc:creator>
<dc:creator>Lien, E. C.</dc:creator>
<dc:creator>Barbeau, A. M.</dc:creator>
<dc:creator>Vander Heiden, M. G.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2023-01-26</dc:date>
<dc:identifier>doi:10.1101/2023.01.26.525375</dc:identifier>
<dc:title><![CDATA[A CRISPRi/a screening platform to study cellular nutrient transport in diverse microenvironments]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-01-26</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.12.09.519807v1?rss=1">
<title>
<![CDATA[
3D multiplexed tissue imaging reconstruction and optimized region-of-interest (ROI) selection through deep learning model of channels embedding 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.12.09.519807v1?rss=1"
</link>
<description><![CDATA[
Tissue-based sampling and diagnosis are defined as the extraction of information from certain limited spaces and its diagnostic significance of a certain object. Pathologists deal with issues related to tumor heterogeneity since analyzing a single sample does not necessarily capture a representative depiction of cancer, and a tissue biopsy usually only presents a small fraction of the tumor. Many multiplex tissue imaging platforms (MTIs) make the assumption that tissue microarrays (TMAs) containing small core samples of 2-dimensional (2D) tissue sections are a good approximation of bulk tumors although tumors are not 2D. However, emerging whole slide imaging (WSI) or 3D tumor atlases that employ MTIs like cyclic immunofluorescence (CyCIF) strongly challenge this assumption. In spite of the additional insight gathered by measuring the tumor microenvironment in WSI or 3D, it can be prohibitively expensive and time-consuming to process tens or hundreds of tissue sections with CyCIF. Even when resources are not limited, the criteria for region-of-interest (ROI) selection in tissues for downstream analysis remain largely qualitative and subjective as stratified sampling requires the knowledge of objects and evaluates their features. Despite the fact TMAs fail to adequately approximate whole tissue features, a theoretical subsampling of tissue exists that can best represent the tumor in the whole slide image. To address these challenges, we propose deep learning approaches to learn multi-modal image translation tasks from two aspects: 1) generative modeling approach to reconstruct 3D CyCIF representation and 2) co-embedding CyCIF image and Hematoxylin and Eosin (H&E) section to learn multi-modal mappings by a cross-domain translation for minimum representative ROI selection. We demonstrate that generative modeling enables a 3D virtual CyCIF reconstruction of a colorectal cancer specimen given a small subset of the imaging data at training time. By co-embedding histology and MTI features, we propose a simple convex optimization for objective ROI selection. We demonstrate the potential application of ROI selection and the efficiency of its performance with respect to cellular heterogeneity.
]]></description>
<dc:creator>Burlingame, E.</dc:creator>
<dc:creator>Ternes, L.</dc:creator>
<dc:creator>Lin, J.-R.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Kim, E. N.</dc:creator>
<dc:creator>Gray, J. W.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Chang, Y. H.</dc:creator>
<dc:date>2022-12-12</dc:date>
<dc:identifier>doi:10.1101/2022.12.09.519807</dc:identifier>
<dc:title><![CDATA[3D multiplexed tissue imaging reconstruction and optimized region-of-interest (ROI) selection through deep learning model of channels embedding]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-12-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.11.29.518386v1?rss=1">
<title>
<![CDATA[
Prediction and Curation of Missing Biomedical Identifier Mappings with Biomappings 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.11.29.518386v1?rss=1"
</link>
<description><![CDATA[
MotivationBiomedical identifier resources (ontologies, taxonomies, controlled vocabularies) commonly overlap in scope and contain equivalent entries under different identifiers. Maintaining mappings for these relationships is crucial for interoperability and the integration of data and knowledge. However, there are substantial gaps in available mappings motivating their semi-automated curation.

ResultsBiomappings implements a curation cycle workflow for missing mappings which combines automated prediction with human-in-the-loop curation. It supports multiple prediction approaches and provides a web-based user interface for reviewing predicted mappings for correctness, combined with automated consistency checking. Predicted and curated mappings are made available in public, version-controlled resource files on GitHub. Biomappings currently makes available 8,560 curated mappings and 41,178 predicted ones, providing previously missing mappings between widely used resources covering small molecules, cell lines, diseases and other concepts. We demonstrate the value of Biomappings on case studies involving predicting and curating missing mappings among cancer cell lines as well as small molecules tested in clinical trials. We also present how previously missing mappings curated using Biomappings were contributed back to multiple widely used community ontologies.

AvailabilityThe data and code are available under the CC0 and MIT licenses at https://github.com/biopragmatics/biomappings.

Contactbenjamin_gyori@hms.harvard.edu
]]></description>
<dc:creator>Hoyt, C. T.</dc:creator>
<dc:creator>Hoyt, A. L.</dc:creator>
<dc:creator>Gyori, B. M.</dc:creator>
<dc:date>2022-12-02</dc:date>
<dc:identifier>doi:10.1101/2022.11.29.518386</dc:identifier>
<dc:title><![CDATA[Prediction and Curation of Missing Biomedical Identifier Mappings with Biomappings]]></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/2023.02.01.526526v1?rss=1">
<title>
<![CDATA[
Nociceptor neuroimmune interactomes reveal cell type- and injury-specific inflammatory pain pathways 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.02.01.526526v1?rss=1"
</link>
<description><![CDATA[
Inflammatory pain associated with tissue injury and infections, results from the heightened sensitivity of the peripheral terminals of nociceptor sensory neurons in response to exposure to inflammatory mediators. Targeting immune-derived inflammatory ligands, like prostaglandin E2, has been effective in alleviating inflammatory pain. However, the diversity of immune cells and the vast array of ligands they produce make it challenging to systematically map all neuroimmune pathways that contribute to inflammatory pain. Here, we constructed a comprehensive and updatable database of receptor-ligand pairs and complemented it with single-cell transcriptomics of immune cells and sensory neurons in three distinct inflammatory pain conditions, to generate injury-specific neuroimmune interactomes. We identified cell-type-specific neuroimmune axes that are common, as well as unique, to different injury types. This approach successfully predicts neuroimmune pathways with established roles in inflammatory pain as well as ones not previously described. We found that thrombospondin-1 produced by myeloid cells in all three conditions, is a negative regulator of nociceptor sensitization, revealing a non-canonical role of immune ligands as an endogenous reducer of peripheral sensitization. This computational platform lays the groundwork to identify novel mechanisms of immune-mediated peripheral sensitization and the specific disease contexts in which they act.
]]></description>
<dc:creator>Jain, A.</dc:creator>
<dc:creator>Gyori, B.</dc:creator>
<dc:creator>Hakim, S.</dc:creator>
<dc:creator>Bunga, S.</dc:creator>
<dc:creator>Taub, D. G.</dc:creator>
<dc:creator>Ruiz-Cantero, M. C.</dc:creator>
<dc:creator>Tong-Li, C.</dc:creator>
<dc:creator>Andrews, N.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Woolf, C. J.</dc:creator>
<dc:date>2023-02-03</dc:date>
<dc:identifier>doi:10.1101/2023.02.01.526526</dc:identifier>
<dc:title><![CDATA[Nociceptor neuroimmune interactomes reveal cell type- and injury-specific inflammatory pain pathways]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-02-03</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.03.06.531314v1?rss=1">
<title>
<![CDATA[
Matrix and analysis metadata standards (MAMS) to facilitate harmonization and reproducibility of single-cell data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.03.06.531314v1?rss=1"
</link>
<description><![CDATA[
A large number of genomic and imaging datasets are being produced by consortia that seek to characterize healthy and disease tissues at single-cell resolution. While much effort has been devoted to capturing information related to biospecimen information and experimental procedures, the metadata standards that describe data matrices and the analysis workflows that produced them are relatively lacking. Detailed metadata schema related to data analysis are needed to facilitate sharing and interoperability across groups and to promote data provenance for reproducibility. To address this need, we developed the Matrix and Analysis Metadata Standards (MAMS) to serve as a resource for data coordinating centers and tool developers. We first curated several simple and complex "use cases" to characterize the types of featureobservation matrices (FOMs), annotations, and analysis metadata produced in different workflows. Based on these use cases, metadata fields were defined to describe the data contained within each matrix including those related to processing, modality, and subsets. Suggested terms were created for the majority of fields to aid in harmonization of metadata terms across groups. Additional provenance metadata fields were also defined to describe the software and workflows that produced each FOM. Finally, we developed a simple listlike schema that can be used to store MAMS information and implemented in multiple formats. Overall, MAMS can be used as a guide to harmonize analysis-related metadata which will ultimately facilitate integration of datasets across tools and consortia. MAMS specifications, use cases, and examples can be found at https://github.com/single-cell-mams/mams/.
]]></description>
<dc:creator>Wang, Y.</dc:creator>
<dc:creator>Sarfraz, I.</dc:creator>
<dc:creator>Teh, W. K.</dc:creator>
<dc:creator>Sokolov, A.</dc:creator>
<dc:creator>Herb, B. R.</dc:creator>
<dc:creator>Creasy, H. H.</dc:creator>
<dc:creator>Virshup, I.</dc:creator>
<dc:creator>Dries, R.</dc:creator>
<dc:creator>Degatano, K.</dc:creator>
<dc:creator>Mahurkar, A.</dc:creator>
<dc:creator>Schnell, D. J.</dc:creator>
<dc:creator>Madrigal, P.</dc:creator>
<dc:creator>Hilton, J.</dc:creator>
<dc:creator>Gehlenborg, N.</dc:creator>
<dc:creator>Tickle, T.</dc:creator>
<dc:creator>Campbell, J. D.</dc:creator>
<dc:date>2023-03-07</dc:date>
<dc:identifier>doi:10.1101/2023.03.06.531314</dc:identifier>
<dc:title><![CDATA[Matrix and analysis metadata standards (MAMS) to facilitate harmonization and reproducibility of single-cell data]]></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/2023.03.19.532758v1?rss=1">
<title>
<![CDATA[
Diverse and Abundant Viruses Exploit Conjugative Plasmids 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.03.19.532758v1?rss=1"
</link>
<description><![CDATA[
Phages exert profound evolutionary pressure on bacteria by interacting with receptors on the cell surface to initiate infection. While the majority of phages use chromosomally-encoded cell surface structures as receptors, plasmid-dependent phages exploit plasmid-encoded conjugation proteins, making their host range dependent on horizontal transfer of the plasmid. Despite their unique biology and biotechnological significance, only a small number of plasmid-dependent phages have been characterized. Here we systematically search for new plasmid-dependent phages targeting IncP and IncF plasmids using a targeted discovery platform, and find that they are common and abundant in wastewater, and largely unexplored in terms of their genetic diversity. Plasmid-dependent phages are enriched in non-canonical types of phages, and all but one of the 64 phages we isolated were non-tailed, and members of the lipid-containing tectiviruses, ssDNA filamentous phages or ssRNA phages. We show that plasmid-dependent tectiviruses exhibit profound differences in their host range which is associated with variation in the phage holin protein. Despite their relatively high abundance in wastewater, plasmid-dependent tectiviruses are missed by metaviromic analyses, underscoring the continued importance of culture-based phage discovery. Finally, we identify a tailed phage dependent on the IncF plasmid, and find related structural genes in phages that use the orthogonal type 4 pilus as a receptor, highlighting the promiscuous use of these distinct contractile structures by multiple groups of phages. Taken together, these results indicate plasmid-dependent phages play an under-appreciated evolutionary role in constraining horizontal gene transfer via conjugative plasmids.
]]></description>
<dc:creator>Quinones-Olvera, N.</dc:creator>
<dc:creator>Owen, S. V.</dc:creator>
<dc:creator>McCully, L. M.</dc:creator>
<dc:creator>Marin, M. G.</dc:creator>
<dc:creator>Rand, E. A.</dc:creator>
<dc:creator>Fan, A. C.</dc:creator>
<dc:creator>Dosumu, O. J. M.</dc:creator>
<dc:creator>Paul, K.</dc:creator>
<dc:creator>Castano, C. E. S.</dc:creator>
<dc:creator>Paull, J. S.</dc:creator>
<dc:creator>Petherbridge, R.</dc:creator>
<dc:creator>Baym, M.</dc:creator>
<dc:date>2023-03-19</dc:date>
<dc:identifier>doi:10.1101/2023.03.19.532758</dc:identifier>
<dc:title><![CDATA[Diverse and Abundant Viruses Exploit Conjugative Plasmids]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-03-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.04.03.535435v1?rss=1">
<title>
<![CDATA[
Immune Profiling of Dermatologic Adverse Events from Checkpoint Blockade using Tissue Cyclic Immunofluorescence 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.04.03.535435v1?rss=1"
</link>
<description><![CDATA[
In this study, we demonstrate the utility of whole-slide CyCIF (tissue-based cyclic immunofluorescence) imaging for characterizing immune cell infiltrates in immune checkpoint inhibitor (ICI)-induced dermatologic adverse events (dAEs). We analyzed six cases of ICI-induced dAEs, including lichenoid, bullous pemphigoid, psoriasis, and eczematous eruptions, comparing immune profiling results obtained using both standard immunohistochemistry (IHC) and CyCIF. Our findings indicate that CyCIF provides more detailed and precise single-cell characterization of immune cell infiltrates than IHC, which relies on semi-quantitative scoring by pathologists. This pilot study highlights the potential of CyCIF to advance our understanding of the immune environment in dAEs by revealing tissue-level spatial patterns of immune cell infiltrates, allowing for more precise phenotypic distinctions and deeper exploration of disease mechanisms. By demonstrating that CyCIF can be performed on friable tissues, such as bullous pemphigoid, we provide a foundation for future studies to examine the drivers of specific dAEs using larger cohorts of phenotyped toxicity and suggest a broader role for highly multiplexed tissue imaging in phenotyping the immune mediated disease that they resemble.
]]></description>
<dc:creator>Maliga, Z.</dc:creator>
<dc:creator>Kim, D. Y.</dc:creator>
<dc:creator>Bui, A.-T. N.</dc:creator>
<dc:creator>Lin, J.-R.</dc:creator>
<dc:creator>Dewan, A. K.</dc:creator>
<dc:creator>Murphy, G. F.</dc:creator>
<dc:creator>Nirmal, A. J.</dc:creator>
<dc:creator>Lian, C. G.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>LeBoeuf, N. R.</dc:creator>
<dc:date>2023-04-05</dc:date>
<dc:identifier>doi:10.1101/2023.04.03.535435</dc:identifier>
<dc:title><![CDATA[Immune Profiling of Dermatologic Adverse Events from Checkpoint Blockade using Tissue Cyclic Immunofluorescence]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-04-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.04.15.536996v1?rss=1">
<title>
<![CDATA[
Efficient and Robust Search of Microbial Genomes via Phylogenetic Compression 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.04.15.536996v1?rss=1"
</link>
<description><![CDATA[
Comprehensive collections approaching millions of sequenced genomes have become central information sources in the life sciences. However, the rapid growth of these collections has made it effectively impossible to search these data using tools such as BLAST and its successors. Here, we present a technique called phylogenetic compression, which uses evolutionary history to guide compression and efficiently search large collections of microbial genomes using existing algorithms and data structures. We show that, when applied to modern diverse collections approaching millions of genomes, lossless phylogenetic compression improves the compression ratios of assemblies, de Bruijn graphs, and k-mer indexes by one to two orders of magnitude. Additionally, we develop a pipeline for a BLAST-like search over these phylogeny-compressed reference data, and demonstrate it can align genes, plasmids, or entire sequencing experiments against all sequenced bacteria until 2019 on ordinary desktop computers within a few hours. Phylogenetic compression has broad applications in computational biology and may provide a fundamental design principle for future genomics infrastructure.
]]></description>
<dc:creator>Brinda, K.</dc:creator>
<dc:creator>Lima, L.</dc:creator>
<dc:creator>Pignotti, S.</dc:creator>
<dc:creator>Quinones-Olvera, N.</dc:creator>
<dc:creator>Salikhov, K.</dc:creator>
<dc:creator>Chikhi, R.</dc:creator>
<dc:creator>Kucherov, G.</dc:creator>
<dc:creator>Iqbal, Z.</dc:creator>
<dc:creator>Baym, M.</dc:creator>
<dc:date>2023-04-16</dc:date>
<dc:identifier>doi:10.1101/2023.04.15.536996</dc:identifier>
<dc:title><![CDATA[Efficient and Robust Search of Microbial Genomes via Phylogenetic Compression]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-04-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.07.21.548450v1?rss=1">
<title>
<![CDATA[
Addressing persistent challenges in digital image analysis of cancerous tissues 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.07.21.548450v1?rss=1"
</link>
<description><![CDATA[
The National Cancer Institute (NCI) supports many research programs and consortia, many of which use imaging as a major modality for characterizing cancerous tissue. A trans-consortia Image Analysis Working Group (IAWG) was established in 2019 with a mission to disseminate imaging-related work and foster collaborations. In 2022, the IAWG held a virtual hackathon focused on addressing challenges of analyzing high dimensional datasets from fixed cancerous tissues. Standard image processing techniques have automated feature extraction, but the next generation of imaging data requires more advanced methods to fully utilize the available information. In this perspective, we discuss current limitations of the automated analysis of multiplexed tissue images, the first steps toward deeper understanding of these limitations, what possible solutions have been developed, any new or refined approaches that were developed during the Image Analysis Hackathon 2022, and where further effort is required. The outstanding problems addressed in the hackathon fell into three main themes: 1) challenges to cell type classification and assessment, 2) translation and visual representation of spatial aspects of high dimensional data, and 3) scaling digital image analyses to large (multi-TB) datasets. We describe the rationale for each specific challenge and the progress made toward addressing it during the hackathon. We also suggest areas that would benefit from more focus and offer insight into broader challenges that the community will need to address as new technologies are developed and integrated into the broad range of image-based modalities and analytical resources already in use within the cancer research community.
]]></description>
<dc:creator>Prabhakaran, S.</dc:creator>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Baker, G. J.</dc:creator>
<dc:creator>Beyer, J.</dc:creator>
<dc:creator>Chang, Y. H.</dc:creator>
<dc:creator>Creason, A. L.</dc:creator>
<dc:creator>Krueger, R.</dc:creator>
<dc:creator>Muhlich, J.</dc:creator>
<dc:creator>Patterson, N. H.</dc:creator>
<dc:creator>Sidak, K.</dc:creator>
<dc:creator>Sudar, D.</dc:creator>
<dc:creator>Taylor, A. J.</dc:creator>
<dc:creator>Ternes, L.</dc:creator>
<dc:creator>Troidl, J.</dc:creator>
<dc:creator>Yubin, X.</dc:creator>
<dc:creator>Sokolov, A.</dc:creator>
<dc:creator>Tyson, D. R.</dc:creator>
<dc:creator>Participants of the Cell Imaging Hackathon 2022,</dc:creator>
<dc:date>2023-07-24</dc:date>
<dc:identifier>doi:10.1101/2023.07.21.548450</dc:identifier>
<dc:title><![CDATA[Addressing persistent challenges in digital image analysis of cancerous tissues]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-07-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.11.07.566063v1?rss=1">
<title>
<![CDATA[
2D and 3D multiplexed subcellular profiling of nuclear instability in human cancer 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.11.07.566063v1?rss=1"
</link>
<description><![CDATA[
Nuclear atypia, including altered nuclear size, contour, and chromatin organization, is ubiquitous in cancer cells. Atypical primary nuclei and micronuclei can rupture during interphase; however, the frequency, causes, and consequences of nuclear rupture are unknown in most cancers. We demonstrate that nuclear envelope rupture is surprisingly common in many human cancers, particularly glioblastoma. Using highly-multiplexed 2D and super-resolution 3D-imaging of glioblastoma tissues and patient-derived xenografts and cells, we link primary nuclear rupture with reduced lamin A/C and micronuclear rupture with reduced lamin B1. Moreover, ruptured glioblastoma cells activate cGAS-STING-signaling involved in innate immunity. We observe that local patterning of cell states influences tumor spatial organization and is linked to both lamin expression and rupture frequency, with neural-progenitor-cell-like states exhibiting the lowest lamin A/C levels and greatest susceptibility to primary nuclear rupture. Our study reveals that nuclear instability is a core feature of cancer, and links nuclear integrity, cell state, and immune signaling.
]]></description>
<dc:creator>Coy, S.</dc:creator>
<dc:creator>Cheng, B.</dc:creator>
<dc:creator>Lee, J. S.</dc:creator>
<dc:creator>Rashid, R.</dc:creator>
<dc:creator>Browning, L.</dc:creator>
<dc:creator>Xu, Y.</dc:creator>
<dc:creator>Chakrabarty, S. S.</dc:creator>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Chan, S.</dc:creator>
<dc:creator>Tefft, J. B.</dc:creator>
<dc:creator>Spektor, A.</dc:creator>
<dc:creator>Ligon, K. L.</dc:creator>
<dc:creator>Baker, G. J.</dc:creator>
<dc:creator>Pellman, D.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:date>2023-11-11</dc:date>
<dc:identifier>doi:10.1101/2023.11.07.566063</dc:identifier>
<dc:title><![CDATA[2D and 3D multiplexed subcellular profiling of nuclear instability in human cancer]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-11-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.11.01.565120v1?rss=1">
<title>
<![CDATA[
Quality Control for Single Cell Analysis of High-plex Tissue Profiles using CyLinter 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.11.01.565120v1?rss=1"
</link>
<description><![CDATA[
Tumors are complex assemblies of cellular and acellular structures patterned on spatial scales from microns to centimeters. Study of these assemblies has advanced dramatically with the introduction of high-plex spatial profiling. Image-based profiling methods reveal the intensities and spatial distributions of 20-100 proteins at subcellular resolution in 103-107 cells per specimen. Despite extensive work on methods for extracting single-cell data from these images, all tissue images contain artefacts such as folds, debris, antibody aggregates, optical aberrations and image processing errors that arise from imperfections in specimen preparation, data acquisition, image assembly, and feature extraction. We show that these artefacts dramatically impact single-cell data analysis, obscuring meaningful biological interpretation. We describe an interactive quality control software tool, CyLinter, that identifies and removes data associated with imaging artefacts. CyLinter greatly improves single-cell analysis, especially for archival specimens sectioned many years prior to data collection, such as those from clinical trials.
]]></description>
<dc:creator>Baker, G. J.</dc:creator>
<dc:creator>Novikov, E.</dc:creator>
<dc:creator>Zhao, Z.</dc:creator>
<dc:creator>Vallius, T.</dc:creator>
<dc:creator>Davis, J. A.</dc:creator>
<dc:creator>Lin, J.-R.</dc:creator>
<dc:creator>Muhlich, J. L.</dc:creator>
<dc:creator>Mittendorf, E. A.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Guerriero, J. L.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2023-11-01</dc:date>
<dc:identifier>doi:10.1101/2023.11.01.565120</dc:identifier>
<dc:title><![CDATA[Quality Control for Single Cell Analysis of High-plex Tissue Profiles using CyLinter]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-11-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.11.15.567196v1?rss=1">
<title>
<![CDATA[
Cell Spotter (CSPOT): A machine-learning approach to automated cell spotting and quantification of highly multiplexed tissue images 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.11.15.567196v1?rss=1"
</link>
<description><![CDATA[
Highly multiplexed tissue imaging and in situ spatial profiling aim to extract single-cell data from specimens containing closely packed cells of diverse morphology. This is challenging due to the difficulty of accurately assigning boundaries between cells (segmentation) and then generating per-cell staining intensities. Existing methods use gating to convert per-cell intensity data to positive and negative scores; this is a common approach in flow cytometry, but one that is problematic in imaging. In contrast, human experts identify cells in crowded environments using morphological, neighborhood, and intensity information. Here we describe a computational approach (Cell Spotter or CSPOT) that uses supervised machine learning in combination with classical segmentation to perform automated cell type calling. CSPOT is robust to artifacts that commonly afflict tissue imaging and can replace conventional gating. The end-to-end Python implementation of CSPOT can be integrated into cloud-based image processing pipelines to substantially improve the speed, accuracy, and reproducibility of single-cell spatial data.
]]></description>
<dc:creator>Nirmal, A. J.</dc:creator>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Sorger, P.</dc:creator>
<dc:date>2023-11-17</dc:date>
<dc:identifier>doi:10.1101/2023.11.15.567196</dc:identifier>
<dc:title><![CDATA[Cell Spotter (CSPOT): A machine-learning approach to automated cell spotting and quantification of highly multiplexed tissue images]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-11-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.11.10.566670v1?rss=1">
<title>
<![CDATA[
Multiplexed 3D Analysis of Cell Plasticity and Immune Niches in Melanoma 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.11.10.566670v1?rss=1"
</link>
<description><![CDATA[
Diseases like cancer involve alterations in in cell proportions, states, and local interactions as well as complex changes in 3D tissue architecture. However, disease diagnosis and most multiplexed spatial profiling studies rely on inspecting thin (4-5 micron) tissue specimens. Here, we use confocal microscopy and cyclic immunofluorescence (3D CyCIF) to show that few if any cells are intact in these thin sections; this reduces the accuracy of cell phenotyping and interaction analysis. In contrast, high-plex 3D CyCIF imaging of intact cells in thick tissue sections enables accurate quantification of marker proteins and detailed analysis of intracellular structures and organelles. Precise imaging of cell membranes also makes it possible to detect juxtacrine signalling among interacting tumour and immune cells and reveals the formation of spatially-restricted cytokine niches. Thus, 3D CyCIF provides insights into cell states and morphologies in preserved human tissues at a level of detail previously limited to cultured cells.
]]></description>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Nirmal, A. J.</dc:creator>
<dc:creator>Zhou, F. Y.</dc:creator>
<dc:creator>Maliga, Z.</dc:creator>
<dc:creator>Montero Llopis, P.</dc:creator>
<dc:creator>Murphy, G. F.</dc:creator>
<dc:creator>Lian, C.</dc:creator>
<dc:creator>Danuser, G.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2023-11-15</dc:date>
<dc:identifier>doi:10.1101/2023.11.10.566670</dc:identifier>
<dc:title><![CDATA[Multiplexed 3D Analysis of Cell Plasticity and Immune Niches in Melanoma]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-11-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.11.10.566378v1?rss=1">
<title>
<![CDATA[
SpatialCells: Automated Profiling of Tumor Microenvironments with Spatially Resolved Multiplexed Single-Cell Data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.11.10.566378v1?rss=1"
</link>
<description><![CDATA[
BackgroundCancer is a complex cellular ecosystem where malignant cells coexist and interact with immune, stromal, and other cells within the tumor microenvironment. Recent technological advancements in spatially resolved multiplexed imaging at single-cell resolution have led to the generation of large-scale and high-dimensional datasets from biological specimens. This underscores the necessity for automated methodologies that can effectively characterize the molecular, cellular, and spatial properties of tumor microenvironments for various malignancies.

ResultsThis study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of tumor microenvironments using multiplexed single-cell data.

ConclusionsSpatialCells efficiently streamlines the automated extraction of features from multiplexed single-cell data and can process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions, making it an essential tool in advancing our understanding of tumor growth, invasion, and metastasis.

Availability of code and materialshttps://github.com/SemenovLab/SpatialCells.
]]></description>
<dc:creator>Wan, G.</dc:creator>
<dc:creator>Maliga, Z.</dc:creator>
<dc:creator>Yan, B.</dc:creator>
<dc:creator>Vallius, T.</dc:creator>
<dc:creator>Shi, Y.</dc:creator>
<dc:creator>Khattab, S.</dc:creator>
<dc:creator>Chang, C.</dc:creator>
<dc:creator>Nirmal, A. J.</dc:creator>
<dc:creator>Yu, K.-H.</dc:creator>
<dc:creator>Liu, D.</dc:creator>
<dc:creator>Lian, C. G.</dc:creator>
<dc:creator>DeSimone, M. S.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Semenov, Y. R.</dc:creator>
<dc:date>2023-11-14</dc:date>
<dc:identifier>doi:10.1101/2023.11.10.566378</dc:identifier>
<dc:title><![CDATA[SpatialCells: Automated Profiling of Tumor Microenvironments with Spatially Resolved Multiplexed Single-Cell Data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-11-14</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.10.09.561530v1?rss=1">
<title>
<![CDATA[
ASCT2 is the primary serine transporter in cancer cells 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.10.09.561530v1?rss=1"
</link>
<description><![CDATA[
The non-essential amino acid serine is a critical nutrient for cancer cells due to its diverse biosynthetic functions. While some tumors can synthesize serine de novo, others are auxotrophic for serine and therefore reliant on the uptake of exogenous serine. Importantly, however, the transporter(s) that mediate serine uptake in cancer cells are not known. Here, we characterize the amino acid transporter ASCT2 (coded for by the gene SLC1A5) as the primary serine transporter in cancer cells. ASCT2 is well-known as a glutamine transporter in cancer, and our work demonstrates that serine and glutamine compete for uptake through ASCT2. We further show that ASCT2-mediated serine uptake is essential for purine nucleotide biosynthesis and that ER promotes serine uptake by directly activating SLC1A5 transcription. Together, our work defines an additional important role for ASCT2 as a serine transporter in cancer and evaluates ASCT2 as a potential therapeutic target in serine metabolism.
]]></description>
<dc:creator>Conger, K. O.</dc:creator>
<dc:creator>Chidley, C.</dc:creator>
<dc:creator>Ozgurses, M. E.</dc:creator>
<dc:creator>Zhao, H.</dc:creator>
<dc:creator>Kim, Y.</dc:creator>
<dc:creator>Semina, S. E.</dc:creator>
<dc:creator>Burns, P. A.</dc:creator>
<dc:creator>Rawat, V.</dc:creator>
<dc:creator>Sheldon, R.</dc:creator>
<dc:creator>Ben-Sahra, I.</dc:creator>
<dc:creator>Frasor, J.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>DeNicola, G. M.</dc:creator>
<dc:creator>Coloff, J. L.</dc:creator>
<dc:date>2023-10-11</dc:date>
<dc:identifier>doi:10.1101/2023.10.09.561530</dc:identifier>
<dc:title><![CDATA[ASCT2 is the primary serine transporter in cancer cells]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-10-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.09.29.560006v1?rss=1">
<title>
<![CDATA[
Resident memory T cell development is associated with AP-1 transcription factor upregulation across anatomical niches. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.09.29.560006v1?rss=1"
</link>
<description><![CDATA[
Tissue-resident memory T (TRM) cells play a central role in immune responses to pathogens across all barrier tissues after infection. However, the underlying mechanisms that drive TRM differentiation and priming for their recall effector function remains unclear. In this study, we leveraged both newly generated and publicly available single-cell RNA-sequencing (scRNAseq) data generated across 10 developmental time points to define features of CD8 TRM across both skin and small-intestine intraepithelial lymphocytes (siIEL). We employed linear modeling to capture temporally-associated gene programs that increase their expression levels in T cell subsets transitioning from an effector to a memory T cell state. In addition to capturing tissue-specific gene programs, we defined a consensus TRM signature of 60 genes across skin and siIEL that can effectively distinguish TRM from circulating T cell populations, providing a more specific TRM signature than what was previously generated by comparing bulk TRM to naive or non-tissue resident memory populations. This updated TRM signature included the AP-1 transcription factor family members Fos, Fosb and Fosl2. Moreover, ATACseq analysis detected an enrichment of AP-1-specific motifs at open chromatin sites in mature TRM. CyCIF tissue imaging detected nuclear co-localization of AP-1 members Fosb and Junb in resting CD8 TRM >100 days post-infection. Taken together, these results reveal a critical role of AP-1 transcription factor members in TRM biology and suggests a novel mechanism for rapid reactivation of resting TRM in tissue upon antigen encounter.
]]></description>
<dc:creator>Smith, N. P.</dc:creator>
<dc:creator>Yan, Y.</dc:creator>
<dc:creator>Pan, Y.</dc:creator>
<dc:creator>Williams, J. B.</dc:creator>
<dc:creator>Manakongtreecheep, K.</dc:creator>
<dc:creator>Pant, S.</dc:creator>
<dc:creator>Zhao, J.</dc:creator>
<dc:creator>Tian, T.</dc:creator>
<dc:creator>Pan, T.</dc:creator>
<dc:creator>Stingley, C.</dc:creator>
<dc:creator>Wu, K.</dc:creator>
<dc:creator>Zhang, J.</dc:creator>
<dc:creator>Kley, A. L.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Villani, A.-C.</dc:creator>
<dc:creator>Kupper, T. S.</dc:creator>
<dc:date>2023-10-02</dc:date>
<dc:identifier>doi:10.1101/2023.09.29.560006</dc:identifier>
<dc:title><![CDATA[Resident memory T cell development is associated with AP-1 transcription factor upregulation across anatomical niches.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-10-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.09.27.557464v1?rss=1">
<title>
<![CDATA[
Longitudinal and multimodal auditing of tumor adaptation to CDK4/6 inhibitors in HR+ metastatic breast cancers 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.09.27.557464v1?rss=1"
</link>
<description><![CDATA[
CDK4/6 inhibitors (CDK4/6i) have transformed the treatment of hormone receptor-positive (HR+), HER2-negative (HR+) breast cancers as they are effective across all clinicopathological, age, and ethnicity subgroups for metastatic HR+ breast cancer. In metastatic ER+ breast cancer, CDK4/6i lead to strong and consistent improvement in survival across different lines of therapy. To improve understanding of how metastatic HR+ breast cancers become refractory to CDK4/6i, we have created a multimodal and longitudinal tumor atlas to investigate therapeutic adaptations in malignant cells and in the tumor immune microenvironment. This atlas is part of the NCI Cancer Moonshot Human Tumor Atlas Network and includes seven pairs of pre- and on-progression biopsies from five metastatic HR+ breast cancer patients treated with CDK4/6i. Biopsies were profiled with bulk genomics, transcriptomics, and proteomics as well as single-cell ATAC-seq and multiplex tissue imaging for spatial, single-cell resolution. These molecular datasets were then linked with detailed clinical metadata to create an atlas for understanding tumor adaptations during therapy. Analysis of our atlas datasets suggests a diverse set of tumor adaptations to CDK4/6i therapy. Malignant cells may adapt to therapy via mTORC1 activation, cell cycle bypass, and increased replication stress. The tumor immune microenvironment displayed evidence of both immune activation and immune suppression during therapy. Together, our metastatic ER+ breast cancer atlas represents a rich multimodal resource to better understand HR+ breast cancer tumor therapeutic adaptations to CDK4/6i therapy.
]]></description>
<dc:creator>Creason, A. L.</dc:creator>
<dc:creator>Egger, J.</dc:creator>
<dc:creator>Watson, C.</dc:creator>
<dc:creator>Sivagnanam, S.</dc:creator>
<dc:creator>Chin, K.</dc:creator>
<dc:creator>MacPherson, K.</dc:creator>
<dc:creator>Lin, J.-R.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Johnson, B. E.</dc:creator>
<dc:creator>Feiler, H. S.</dc:creator>
<dc:creator>Galipeau, D.</dc:creator>
<dc:creator>Navin, N. E.</dc:creator>
<dc:creator>Demir, E.</dc:creator>
<dc:creator>Chang, Y. H.</dc:creator>
<dc:creator>Corless, C. L.</dc:creator>
<dc:creator>Mitri, Z. I.</dc:creator>
<dc:creator>Thomas, G.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Adey, A. C.</dc:creator>
<dc:creator>Coussens, L. M.</dc:creator>
<dc:creator>Gray, J. W.</dc:creator>
<dc:creator>Mills, G. B.</dc:creator>
<dc:creator>Goecks, J.</dc:creator>
<dc:date>2023-09-29</dc:date>
<dc:identifier>doi:10.1101/2023.09.27.557464</dc:identifier>
<dc:title><![CDATA[Longitudinal and multimodal auditing of tumor adaptation to CDK4/6 inhibitors in HR+ metastatic breast cancers]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-09-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.06.04.595773v1?rss=1">
<title>
<![CDATA[
TYK2 as a novel therapeutic target in Alzheimer Disease 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.06.04.595773v1?rss=1"
</link>
<description><![CDATA[
Neuroinflammation is a pathological feature of many neurodegenerative diseases, including Alzheimers disease (AD)1,2 and amyotrophic lateral sclerosis (ALS)3, raising the possibility of common therapeutic targets. We previously established that cytoplasmic double-stranded RNA (cdsRNA) is spatially coincident with cytoplasmic pTDP-43 inclusions in neurons of patients with C9ORF72-mediated ALS4. CdsRNA triggers a type-I interferon (IFN-I)-based innate immune response in human neural cells, resulting in their death 4. Here, we report that cdsRNA is also spatially coincident with pTDP-43 cytoplasmic inclusions in brain cells of patients with AD pathology and that type-I interferon response genes are significantly upregulated in brain regions affected by AD. We updated our machine-learning pipeline DRIAD-SP (Drug Repurposing In Alzheimers Disease with Systems Pharmacology) to incorporate cryptic exon (CE) detection as a proxy of pTDP-43 inclusions and demonstrated that the FDA-approved JAK inhibitors baricitinib and ruxolitinib that block interferon signaling show a protective signal only in cortical brain regions expressing multiple CEs. Furthermore, the JAK family member TYK2 was a top hit in a CRISPR screen of cdsRNA-mediated death in differentiated human neural cells. The selective TYK2 inhibitor deucravacitinib, an FDA-approved drug for psoriasis, rescued toxicity elicited by cdsRNA. Finally, we identified CCL2, CXCL10, and IL-6 as candidate predictive biomarkers for cdsRNA-related neurodegenerative diseases. Together, we find parallel neuroinflammatory mechanisms between TDP-43 associated-AD and ALS and nominate TYK2 as a possible disease-modifying target of these incurable neurodegenerative diseases.
]]></description>
<dc:creator>Konig, L.</dc:creator>
<dc:creator>Rodriguez, S.</dc:creator>
<dc:creator>Hug, C.</dc:creator>
<dc:creator>Daneshvari, S.</dc:creator>
<dc:creator>Chung, A.</dc:creator>
<dc:creator>Bradshaw, G.</dc:creator>
<dc:creator>Sahin, A.</dc:creator>
<dc:creator>Zhou, G.</dc:creator>
<dc:creator>Eisert, R.</dc:creator>
<dc:creator>Piccioni, F.</dc:creator>
<dc:creator>Das, S.</dc:creator>
<dc:creator>Kalocsay, M.</dc:creator>
<dc:creator>Sokolov, A.</dc:creator>
<dc:creator>Sorger, P.</dc:creator>
<dc:creator>Root, D.</dc:creator>
<dc:creator>Albers, M. W.</dc:creator>
<dc:date>2024-06-06</dc:date>
<dc:identifier>doi:10.1101/2024.06.04.595773</dc:identifier>
<dc:title><![CDATA[TYK2 as a novel therapeutic target in Alzheimer Disease]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-06-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.06.02.596989v1?rss=1">
<title>
<![CDATA[
Persistent Neurological Deficits in Mouse PASC Reveal Antiviral Drug Limitations 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.06.02.596989v1?rss=1"
</link>
<description><![CDATA[
Post-Acute Sequelae of COVID-19 (PASC) encompasses persistent neurological symptoms, including olfactory and autonomic dysfunction. Here, we report chronic neurological dysfunction in mice infected with a virulent mouse-adapted SARS-CoV-2 that does not infect the brain. Long after recovery from nasal infection, we observed loss of tyrosine hydroxylase (TH) expression in olfactory bulb glomeruli and neurotransmitter levels in the substantia nigra (SN) persisted. Vulnerability of dopaminergic neurons in these brain areas was accompanied by increased levels of proinflammatory cytokines and neurobehavioral changes. RNAseq analysis unveiled persistent microglia activation, as found in human neurodegenerative diseases. Early treatment with antivirals (nirmatrelvir and molnupiravir) reduced virus titers and lung inflammation but failed to prevent neurological abnormalities, as observed in patients. Together these results show that chronic deficiencies in neuronal function in SARS-CoV-2-infected mice are not directly linked to ongoing olfactory epithelium dysfunction. Rather, they bear similarity with neurodegenerative disease, the vulnerability of which is exacerbated by chronic inflammation.
]]></description>
<dc:creator>Verma, A. K.</dc:creator>
<dc:creator>Lowery, S.</dc:creator>
<dc:creator>Lin, L.-C.</dc:creator>
<dc:creator>Duraisami, E.</dc:creator>
<dc:creator>Abrahante, J. E. K.</dc:creator>
<dc:creator>Qiu, Q.</dc:creator>
<dc:creator>Hefti, M. M.</dc:creator>
<dc:creator>Yu, R. C.</dc:creator>
<dc:creator>Albers, M. W.</dc:creator>
<dc:creator>Perlman, S.</dc:creator>
<dc:date>2024-06-03</dc:date>
<dc:identifier>doi:10.1101/2024.06.02.596989</dc:identifier>
<dc:title><![CDATA[Persistent Neurological Deficits in Mouse PASC Reveal Antiviral Drug Limitations]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-06-03</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.05.22.594980v1?rss=1">
<title>
<![CDATA[
Spatial Profiling of Metals through Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.05.22.594980v1?rss=1"
</link>
<description><![CDATA[
The spatial-omic analysis of biomolecules such as nucleic acids, lipids, metabolites, and proteins is advancing the study of biological systems and processes in a physio-pathological context. Here, we describe an innovative matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) method to detect metals within biological tissues using instrumentation that is widely available in research and clinical laboratories. We characterize the spatial distribution of metals in diverse settings including mouse embryogenesis, genetic disorders leading to abnormal metal accumulation, and preclinical testing for improved platinum-based chemotherapy delivery through focused ultrasound across the blood-brain barrier. Spatial metal profiling will advance research studies and the clinical analysis of metal-related diseases, enabling more precise use of metal-based therapies and advances in diverse scientific fields beyond biomedicine.

One-Sentence SummarySpatial metallomic profiling maps native metals or those coordinated to xenobiotics, antibodies, and biomolecules in tissues.
]]></description>
<dc:creator>Stopka, S.</dc:creator>
<dc:creator>Bodineau, C.</dc:creator>
<dc:creator>Baquer, G.</dc:creator>
<dc:creator>Lin, J.-R.</dc:creator>
<dc:creator>Hossain, M. A.</dc:creator>
<dc:creator>Regan, M. S.</dc:creator>
<dc:creator>Ruiz, D. F.</dc:creator>
<dc:creator>Fletcher, S.-M.</dc:creator>
<dc:creator>Pourquie, O.</dc:creator>
<dc:creator>Lutsenko, S.</dc:creator>
<dc:creator>Payne, C.</dc:creator>
<dc:creator>Agar, J. N.</dc:creator>
<dc:creator>Mazitschek, R.</dc:creator>
<dc:creator>McDannold, N.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Agar, N.</dc:creator>
<dc:date>2024-05-23</dc:date>
<dc:identifier>doi:10.1101/2024.05.22.594980</dc:identifier>
<dc:title><![CDATA[Spatial Profiling of Metals through Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-05-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.04.11.589087v1?rss=1">
<title>
<![CDATA[
psudo: Exploring Multi-Channel Biomedical Image Data with Spatially and Perceptually Optimized Pseudocoloring 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.04.11.589087v1?rss=1"
</link>
<description><![CDATA[
Over the past century, multichannel fluorescence imaging has been pivotal in myriad scientific breakthroughs by enabling the spatial visualization of proteins within a biological sample. With the shift to digital methods and visualization software, experts can now flexibly pseudocolor and combine image channels, each corresponding to a different protein, to explore their spatial relationships. We thus propose psudo, an interactive system that allows users to create optimal color palettes for multichannel spatial data. In psudo, a novel optimization method generates palettes that maximize the perceptual differences between channels while mitigating confusing color blending in overlapping channels. We integrate this method into a system that allows users to explore multi-channel image data and compare and evaluate color palettes for their data. An interactive lensing approach provides on-demand feedback on channel overlap and a color confusion metric while giving context to the underlying channel values. Color palettes can be applied globally or, using the lens, to local regions of interest. We evaluate our palette optimization approach using three graphical perception tasks in a crowdsourced user study with 150 participants, showing that users are more accurate at discerning and comparing the underlying data using our approach. Additionally, we showcase psudo in a case study exploring the complex immune responses in cancer tissue data with a biologist.

CCS ConceptsO_LIHuman-centered computing [-&gt;] Visualization systems and tools;
C_LI
]]></description>
<dc:creator>Warchol, S.</dc:creator>
<dc:creator>Troidl, J.</dc:creator>
<dc:creator>Muhlich, J. L.</dc:creator>
<dc:creator>Krueger, R.</dc:creator>
<dc:creator>Hoffer, J.</dc:creator>
<dc:creator>Lin, T.</dc:creator>
<dc:creator>Beyer, J.</dc:creator>
<dc:creator>Glassman, E.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Pfister, H.</dc:creator>
<dc:date>2024-04-15</dc:date>
<dc:identifier>doi:10.1101/2024.04.11.589087</dc:identifier>
<dc:title><![CDATA[psudo: Exploring Multi-Channel Biomedical Image Data with Spatially and Perceptually Optimized Pseudocoloring]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-04-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.05.03.592249v1?rss=1">
<title>
<![CDATA[
A general algorithm for consensus 3D cell segmentation from 2D segmented stacks 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.05.03.592249v1?rss=1"
</link>
<description><![CDATA[
Cell segmentation is the foundation of a wide range of microscopy-based biological studies. Deep learning has revolutionized 2D cell segmentation, enabling generalized solutions across cell types and imaging modalities. This has been driven by the ease of scaling up image acquisition, annotation, and computation. However, 3D cell segmentation, requiring dense annotation of 2D slices still poses significant challenges. Manual labeling of 3D cells to train broadly applicable segmentation models is prohibitive. Even in high- contrast images annotation is ambiguous and time-consuming. Here we develop a theory and toolbox, u- Segment3D, for 2D-to-3D segmentation, compatible with any 2D method generating pixel-based instance cell masks. u-Segment3D translates and enhances 2D instance segmentations to a 3D consensus instance segmentation without training data, as demonstrated on 11 real-life datasets, >70,000 cells, spanning single cells, cell aggregates, and tissue. Moreover, u-Segment3D is competitive with native 3D segmentation, even exceeding when cells are crowded and have complex morphologies.
]]></description>
<dc:creator>Zhou, F. Y.</dc:creator>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Shang, Z.</dc:creator>
<dc:creator>Daetwyler, S.</dc:creator>
<dc:creator>Marin, Z.</dc:creator>
<dc:creator>Islam, M. T.</dc:creator>
<dc:creator>Nanes, B. A.</dc:creator>
<dc:creator>Jenkins, E.</dc:creator>
<dc:creator>Gihana, G. M.</dc:creator>
<dc:creator>Chang, B.-J.</dc:creator>
<dc:creator>Weems, A.</dc:creator>
<dc:creator>Dustin, M.</dc:creator>
<dc:creator>Morrison, S. J.</dc:creator>
<dc:creator>Fiolka, R. P.</dc:creator>
<dc:creator>Dean, K. M.</dc:creator>
<dc:creator>Jamieson, A.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Danuser, G. K.</dc:creator>
<dc:date>2024-05-06</dc:date>
<dc:identifier>doi:10.1101/2024.05.03.592249</dc:identifier>
<dc:title><![CDATA[A general algorithm for consensus 3D cell segmentation from 2D segmented stacks]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-05-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.03.07.583804v1?rss=1">
<title>
<![CDATA[
Sendai virus persistence questions the transient naive reprogramming method for iPSC generation 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.03.07.583804v1?rss=1"
</link>
<description><![CDATA[
Since the revolutionary discovery of induced pluripotent stem cells (iPSCs) by Shinya Yamanaka, the comparison between iPSCs and embryonic stem cells (ESCs) has revealed significant differences in their epigenetic states and developmental potential. A recent compelling study published in Nature by Buckberry et al.1 demonstrated that a transient-naive-treatment (TNT) could facilitate epigenetic reprogramming and improve the developmental potential of human iPSCs (hiPSCs). However, the study characterized bulk hiPSCs instead of isolating clonal lines and overlooked the persistent expression of Sendai virus carrying exogenous Yamanaka factors. Our analyses revealed that Sendai genes were expressed in most control PSC samples, including hESCs, which were not intentionally infected. The highest levels of Sendai expression were detected in samples continuously treated with naive media, where it led to overexpression of exogenous MYC, SOX2, and KLF4, altering both the expression levels and ratios of reprogramming factors. Our findings call for further research to verify the effectiveness of the TNT method in the context of delivery methods that ensure prompt elimination of exogenous factors, leading to the generation of bona fide transgene-independent iPSCs.
]]></description>
<dc:creator>De Los Angeles, A.</dc:creator>
<dc:creator>Hug, C. B.</dc:creator>
<dc:creator>Gladyshev, V. N.</dc:creator>
<dc:creator>Church, G. M.</dc:creator>
<dc:creator>Velychko, S.</dc:creator>
<dc:date>2024-03-12</dc:date>
<dc:identifier>doi:10.1101/2024.03.07.583804</dc:identifier>
<dc:title><![CDATA[Sendai virus persistence questions the transient naive reprogramming method for iPSC generation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-03-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.07.26.605293v1?rss=1">
<title>
<![CDATA[
PRAME expression in melanoma is negatively regulated by TET2-mediated DNA hydroxymethylation 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.07.26.605293v1?rss=1"
</link>
<description><![CDATA[
Preferentially Expressed Antigen in Melanoma (PRAME) and Ten-Eleven Translocation (TET) dioxygenase-mediated 5-hydroxymethylcytosine (5hmC) are emerging melanoma biomarkers. We observed an inverse correlation between PRAME expression and 5hmC levels in benign nevi, melanoma in situ, primary invasive melanoma, and metastatic melanomas via immunohistochemistry and multiplex immunofluorescence: nevi exhibited high 5hmC and low PRAME, whereas melanomas showed the opposite pattern. Single-cell multiplex imaging of melanoma precursors revealed that diminished 5hmC coincides with PRAME upregulation in premalignant cells. Analysis of TCGA and GTEx databases confirmed a negative relationship between TET2 and PRAME mRNA expression in melanoma. Additionally, 5hmC levels were reduced at the PRAME 5 promoter in melanoma compared to nevi, suggesting a role for 5hmC in PRAME transcription. Restoring 5hmC levels via TET2 overexpression notably reduced PRAME expression in melanoma cell lines. These findings establish a function of TET2-mediated DNA hydroxymethylation in regulating PRAME expression and demonstrate epigenetic reprogramming as pivotal in melanoma tumorigenesis.

TeaserMelanoma biomarker PRAME expression is negatively regulated epigenetically by TET2-mediated DNA hydroxymethylation
]]></description>
<dc:creator>Fang, R.</dc:creator>
<dc:creator>Vallius, T.</dc:creator>
<dc:creator>Zhang, A.</dc:creator>
<dc:creator>Van Cura, D.</dc:creator>
<dc:creator>Alicandri, F.</dc:creator>
<dc:creator>Fischer, G.</dc:creator>
<dc:creator>Draper, E.</dc:creator>
<dc:creator>Xu, S.</dc:creator>
<dc:creator>Pelletier, R.</dc:creator>
<dc:creator>Katsyv, I.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Murphy, G. F.</dc:creator>
<dc:creator>Lian, C. G.</dc:creator>
<dc:date>2024-07-26</dc:date>
<dc:identifier>doi:10.1101/2024.07.26.605293</dc:identifier>
<dc:title><![CDATA[PRAME expression in melanoma is negatively regulated by TET2-mediated DNA hydroxymethylation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-07-26</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.06.25.598921v1?rss=1">
<title>
<![CDATA[
Sharing Data from the Human Tumor Atlas Network through Standards, Infrastructure, and Community Engagement 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.06.25.598921v1?rss=1"
</link>
<description><![CDATA[
The Data Coordinating Center (DCC) of the Human Tumor Atlas Network (HTAN) has played a crucial role in enabling the broad sharing and effective utilization of HTAN data within the scienti[fi]c community. Data from the [fi]rst phase of HTAN are now available publicly. We describe the diverse datasets and modalities shared, multiple access routes to HTAN assay data and metadata, data standards, technical infrastructure and governance approaches, as well as our approach to sustained community engagement. HTAN data can be accessed via the HTAN Portal, explored in visualization tools--including CellxGene, Minerva, and cBioPortal--and analyzed in the cloud through the NCI Cancer Research Data Commons nodes. We have developed a streamlined infrastructure to ingest and disseminate data by leveraging the Synapse platform. Taken together, the HTAN DCCs approach demonstrates a successful model for coordinating, standardizing, and disseminating complex cancer research data via multiple resources in the cancer data ecosystem, offering valuable insights for similar consortia, and researchers looking to leverage HTAN data.
]]></description>
<dc:creator>de Bruijn, I.</dc:creator>
<dc:creator>Nikolov, M.</dc:creator>
<dc:creator>Lau, C.</dc:creator>
<dc:creator>Clayton, A.</dc:creator>
<dc:creator>Gibbs, D. L.</dc:creator>
<dc:creator>Mitraka, E.</dc:creator>
<dc:creator>Pozhideyeva, D.</dc:creator>
<dc:creator>Lash, A.</dc:creator>
<dc:creator>Sumer, S. O.</dc:creator>
<dc:creator>Altreuter, J.</dc:creator>
<dc:creator>Anton, K.</dc:creator>
<dc:creator>DeFelice, M.</dc:creator>
<dc:creator>Li, X.</dc:creator>
<dc:creator>Lisman, A.</dc:creator>
<dc:creator>Longabaugh, W. J. R.</dc:creator>
<dc:creator>Muhlich, J.</dc:creator>
<dc:creator>Sandro, S.</dc:creator>
<dc:creator>Nandakumar, S.</dc:creator>
<dc:creator>Peter, S. K.</dc:creator>
<dc:creator>Suver, C.</dc:creator>
<dc:creator>Schultz, N.</dc:creator>
<dc:creator>Taylor, A. J.</dc:creator>
<dc:creator>Thorsson, V.</dc:creator>
<dc:creator>Cerami, E.</dc:creator>
<dc:creator>Eddy, J. A.</dc:creator>
<dc:date>2024-06-30</dc:date>
<dc:identifier>doi:10.1101/2024.06.25.598921</dc:identifier>
<dc:title><![CDATA[Sharing Data from the Human Tumor Atlas Network through Standards, Infrastructure, and Community Engagement]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-06-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.08.16.503984v1?rss=1">
<title>
<![CDATA[
Nucleotide depletion promotes cell fate transitions by inducing DNA replication stress 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.08.16.503984v1?rss=1"
</link>
<description><![CDATA[
Control of cellular identity requires coordination of developmental programs with environmental factors such as nutrient availability, suggesting that modulating aspects of metabolism could alter cell state along differentiation trajectories. Here we find that nucleotide depletion and DNA replication stress are common drivers of cell state progression across a variety of normal and transformed hematopoietic systems. DNA replication stress-induced cell state transitions begin during S phase and are independent of ATR/ATM checkpoint signaling, double-stranded DNA break formation, and changes in cell cycle length. In systems where differentiation is blocked by oncogenic transcription factor expression, replication stress leads to increased activity at primed regulatory loci and expression of lineage-appropriate maturation genes while progenitor TF activity is still present. Altering the baseline cell state by manipulating the cohort of transcription factors expressed redirects the effect of replication stress towards induction of a different set of lineage-specific genes. The ability of replication stress to selectively activate primed maturation programs across different cellular contexts suggests a general mechanism by which metabolism can promote lineage-appropriate and potentially therapeutically relevant cell state transitions.
]]></description>
<dc:creator>Hsu, P. P.</dc:creator>
<dc:creator>Do, B. T.</dc:creator>
<dc:creator>Vermeulen, S. Y.</dc:creator>
<dc:creator>Wang, Z.</dc:creator>
<dc:creator>Hirz, T.</dc:creator>
<dc:creator>Aziz, N.</dc:creator>
<dc:creator>Replogle, J. M.</dc:creator>
<dc:creator>Abbott, K. L.</dc:creator>
<dc:creator>Block, S.</dc:creator>
<dc:creator>Darnell, A. M.</dc:creator>
<dc:creator>Ferreira, R.</dc:creator>
<dc:creator>Milosevic, J. T.</dc:creator>
<dc:creator>Schmidt, D. R.</dc:creator>
<dc:creator>Chidley, C.</dc:creator>
<dc:creator>Su, X. A.</dc:creator>
<dc:creator>Harris, I. S.</dc:creator>
<dc:creator>Weissman, J. S.</dc:creator>
<dc:creator>Cheloufi, S.</dc:creator>
<dc:creator>Sykes, D. B.</dc:creator>
<dc:creator>Vander Heiden, M. G.</dc:creator>
<dc:date>2022-08-16</dc:date>
<dc:identifier>doi:10.1101/2022.08.16.503984</dc:identifier>
<dc:title><![CDATA[Nucleotide depletion promotes cell fate transitions by inducing DNA replication stress]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-08-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.09.25.615007v1?rss=1">
<title>
<![CDATA[
Multimodal Spatial Profiling Reveals Immune Suppression and Microenvironment Remodeling in Fallopian Tube Precursors to High-Grade Serous Ovarian Carcinoma 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.09.25.615007v1?rss=1"
</link>
<description><![CDATA[
High-Grade Serous Ovarian Cancer (HGSOC) originates from fallopian tube (FT) precursors. However, the molecular changes that occur as precancerous lesions progress to HGSOC are not well understood. To address this, we integrated high-plex imaging and spatial transcriptomics to analyze human tissue samples at different stages of HGSOC development, including p53 signatures, serous tubal intraepithelial carcinomas (STIC), and invasive HGSOC. Our findings reveal immune modulating mechanisms within precursor epithelium, characterized by chromosomal instability, persistent interferon (IFN) signaling, and dysregulated innate and adaptive immunity. FT precursors display elevated expression of MHC-class I, including HLA-E, and IFN-stimulated genes, typically linked to later-stage tumorigenesis. These molecular alterations coincide with progressive shifts in the tumor microenvironment, transitioning from immune surveillance in early STICs to immune suppression in advanced STICs and cancer. These insights identify potential biomarkers and therapeutic targets for HGSOC interception and clarify the molecular transitions from precancer to cancer.

STATEMENT OF SIGNIFICANCEThis study maps the immune response in fallopian tube precursors of high-grade serous ovarian cancer, highlighting localized interferon signaling, CIN, and competing immune surveillance and suppression along the progression axis. It provides an explorable public spatial profiling atlas for investigating precancer mechanisms, biomarkers, and early detection and interception strategies.
]]></description>
<dc:creator>Kader, T.</dc:creator>
<dc:creator>Lin, J.-R.</dc:creator>
<dc:creator>Hug, C.</dc:creator>
<dc:creator>Coy, S.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>de Bruijn, I.</dc:creator>
<dc:creator>Shih, N.</dc:creator>
<dc:creator>Jung, E.</dc:creator>
<dc:creator>Pelletier, R. J.</dc:creator>
<dc:creator>Leon, M. L.</dc:creator>
<dc:creator>Mingo, G.</dc:creator>
<dc:creator>Omran, D. K.</dc:creator>
<dc:creator>Lee, J. S.</dc:creator>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Satravada, B. A.</dc:creator>
<dc:creator>Kundra, R.</dc:creator>
<dc:creator>Xu, Y.</dc:creator>
<dc:creator>Chan, S.</dc:creator>
<dc:creator>Tefft, J.</dc:creator>
<dc:creator>Muhlich, J. L.</dc:creator>
<dc:creator>Kim, S.</dc:creator>
<dc:creator>Gysler, S. M.</dc:creator>
<dc:creator>Agudo, J.</dc:creator>
<dc:creator>Heath, J. R.</dc:creator>
<dc:creator>Schultz, N.</dc:creator>
<dc:creator>Drescher, C.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Drapkin, R.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:date>2024-09-27</dc:date>
<dc:identifier>doi:10.1101/2024.09.25.615007</dc:identifier>
<dc:title><![CDATA[Multimodal Spatial Profiling Reveals Immune Suppression and Microenvironment Remodeling in Fallopian Tube Precursors to High-Grade Serous Ovarian Carcinoma]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-09-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.09.24.614701v1?rss=1">
<title>
<![CDATA[
Integrating spatial profiles and cancer genomics to identify immune-infiltrated mismatch repair proficient colorectal cancers 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.09.24.614701v1?rss=1"
</link>
<description><![CDATA[
Predicting the progression of solid cancers based solely on genetics is challenging due to the influence of the tumor microenvironment (TME). For colorectal cancer (CRC), tumors deficient in mismatch repair (dMMR) are more immune infiltrated than mismatch repair proficient (pMMR) tumors and have better prognosis following resection. Here we quantify features of the CRC TME by combining spatial profiling with genetic analysis and release our findings via a spatially enhanced version of cBioPortal that facilitates multi-modal data exploration and analysis. We find that [~]20% of pMMR tumors exhibit similar levels of T cell infiltration as dMMR tumors and that this is associated with better survival but not any specific somatic mutation. These T cell-infiltrated pMMR (tipMMR) tumors contain abundant cells expressing PD1 and PDL1 as well as T regulatory cells, consistent with a suppressed immune response. Thus, like dMMR CRC, tipMMR CRC may benefit from immune checkpoint inhibitor therapy.

SIGNIFICANCEpMMR tumors with high T cell infiltration and active immunosuppression are identifiable with a mid-plex imaging assay whose clinical deployment might double the number of treatment-naive CRCs eligible for ICIs. Moreover, the low tumor mutational burden in tipMMR CRC shows that MMR status is not the only factor promoting immune infiltration.
]]></description>
<dc:creator>Wala, J.</dc:creator>
<dc:creator>de Bruijn, I.</dc:creator>
<dc:creator>Coy, S.</dc:creator>
<dc:creator>Gagne, A.</dc:creator>
<dc:creator>Chan, S.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Hoffer, J.</dc:creator>
<dc:creator>Muhlich, J.</dc:creator>
<dc:creator>Schultz, N.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Sorger, P.</dc:creator>
<dc:date>2024-09-26</dc:date>
<dc:identifier>doi:10.1101/2024.09.24.614701</dc:identifier>
<dc:title><![CDATA[Integrating spatial profiles and cancer genomics to identify immune-infiltrated mismatch repair proficient colorectal cancers]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-09-26</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.07.19.665696v1?rss=1">
<title>
<![CDATA[
SEAL: Spatially-resolved Embedding Analysis with Linked Imaging Data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.07.19.665696v1?rss=1"
</link>
<description><![CDATA[
Dimensionality reduction techniques help analysts make sense of complex, high-dimensional spatial datasets, such as multiplexed tissue imaging, satellite imagery, and astronomical observations, by projecting data attributes into a two-dimensional space. However, these techniques typically abstract away crucial spatial, positional, and morphological contexts, complicating interpretation and limiting insights. To address these limitations, we present SEAL, an interactive visual analytics system designed to bridge the gap between abstract 2D embeddings and their rich spatial imaging context. SEAL introduces a novel hybrid-embedding visualization that preserves image and morphological information while integrating critical high-dimensional feature data. By adapting set visualization methods, SEAL allows analysts to identify, visualize, and compare selections--defined manually or algorithmically--in both the embedding and original spatial views, facilitating a deeper understanding of the spatial arrangement and morphological characteristics of entities of interest. To elucidate differences between selected sets of items, SEAL employs a scalable surrogate model to calculate feature importance scores, identifying the most influential features governing the position of objects within embeddings. These importance scores are visually summarized across selections, with mathematical set operations enabling detailed comparative analyses. We demonstrate SEALs effectiveness and versatility through three case studies: colorectal cancer tissue analysis with a pharmacologist, melanoma investigation with a cell biologist, and exploration of sky survey data with an astronomer. These studies underscore the importance of integrating image context into embedding spaces when interpreting complex imaging datasets. Implemented as a standalone tool while also integrating seamlessly with computational notebooks, SEAL provides an interactive platform for spatially informed exploration of high-dimensional datasets, significantly enhancing interpretability and insight generation.
]]></description>
<dc:creator>Warchol, S.</dc:creator>
<dc:creator>Guo, G.</dc:creator>
<dc:creator>Knittel, J.</dc:creator>
<dc:creator>Freeman, D.</dc:creator>
<dc:creator>Bhalla, U.</dc:creator>
<dc:creator>Muhlich, J.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Pfister, H.</dc:creator>
<dc:date>2025-07-22</dc:date>
<dc:identifier>doi:10.1101/2025.07.19.665696</dc:identifier>
<dc:title><![CDATA[SEAL: Spatially-resolved Embedding Analysis with Linked Imaging Data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-07-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.02.20.639240v1?rss=1">
<title>
<![CDATA[
Dynamic Modeling of Cell Cycle Arrest Through Integrated Single-Cell and Mathematical Modelling Approaches 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.02.20.639240v1?rss=1"
</link>
<description><![CDATA[
Highly multiplexed imaging assays allow simultaneous quantification of multiple protein and phosphorylation markers, providing a static snapshots of cell types and states. Pseudo-time techniques can transform these static snapshots of unsynchronized cells into dynamic trajectories, enabling the study of dynamic processes such as development trajectories and the cell cycle. Such ordering also enables training of mathematical models on these data, but technical challenges have hitherto made it difficult to integrate multiple experimental conditions, limiting the predictive power and insights these models can generate. In this work, we propose data processing and model training approaches for integrating multiplexed, multi-condition immunofluorescence data with mathematical modelling. We devise training strategies that are applicable to datasets where cells exhibit oscillatory as well as arrested dynamics and use them to train a cell cycle model on a dataset of MCF-10A mammary epithelial exposed to cell-cycle arresting small molecules. We validate the model by investigating predicted growth factor sensitivities and responses to inhibitors of cells at different initial conditions. We anticipate that our framework will generalise to other highly multiplexed measurement techniques such as mass-cytometry, rendering larger bodies of data accessible to dynamic modelling and paving the way to deeper biological insights.

Author SummaryAdvanced imaging techniques allow us to see detailed pictures of different proteins and cell changes. By using computational algorithms, we turn these static pictures into dynamic sequences to understand processes like the cell cycle better. However, combining data from different experiments is difficult and limits how well our models can predict outcomes. This study introduces new ways to process data and train models to handle complex data from various conditions.

The approach is tested by using data from untreated and treated cells to create a model of the cell cycle. This model was then checked for accuracy by seeing how well it could predict how cells respond to growth factors and drugs from different starting points. In the future, this method could be used with other data types, allowing for more detailed and accurate models of cellular behavior.
]]></description>
<dc:creator>Cortes-Rios, J.</dc:creator>
<dc:creator>Rodriguez-Fernandez, M.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Fröhlich, F.</dc:creator>
<dc:date>2025-02-26</dc:date>
<dc:identifier>doi:10.1101/2025.02.20.639240</dc:identifier>
<dc:title><![CDATA[Dynamic Modeling of Cell Cycle Arrest Through Integrated Single-Cell and Mathematical Modelling Approaches]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-02-26</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.06.23.661064v1?rss=1">
<title>
<![CDATA[
Morphology-Aware Profiling of Highly Multiplexed Tissue Images using Variational Autoencoders 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.06.23.661064v1?rss=1"
</link>
<description><![CDATA[
Spatial proteomics (highly multiplexed tissue imaging) provides unprecedented insight into the types, states, and spatial organization of cells within preserved tissue environments. To enable single-cell analysis, high-plex images are typically segmented using algorithms that assign marker signals to individual cells. However, conventional segmentation is often imprecise and susceptible to signal spillover between adjacent cells, interfering with accurate cell type identification. Segmentation-based methods also fail to capture the morphological detail that histopathologists rely on for disease diagnosis and staging. Here, we present a method that combines unsupervised, pixel-level machine learning using autoencoders with traditional segmentation to generate single-cell data that captures information on protein abundance, morphology, and local neighborhood in a manner analogous to human experts while overcoming signal spillover. We demonstrate the generality of this approach by applying it to CyCIF, Lunaphore COMET, and CODEX data, and show that it can learn histological features across multiple spatial scales.
]]></description>
<dc:creator>Baker, G. J.</dc:creator>
<dc:creator>Novikov, E.</dc:creator>
<dc:creator>Coy, S.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Hug, C. B.</dc:creator>
<dc:creator>Ahmed, Z.</dc:creator>
<dc:creator>Ordonez, S. A. C.</dc:creator>
<dc:creator>Huang, S.</dc:creator>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Sokolov, A.</dc:creator>
<dc:creator>Pfister, H.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2025-06-27</dc:date>
<dc:identifier>doi:10.1101/2025.06.23.661064</dc:identifier>
<dc:title><![CDATA[Morphology-Aware Profiling of Highly Multiplexed Tissue Images using Variational Autoencoders]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-06-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.06.21.660851v1?rss=1">
<title>
<![CDATA[
Spatial determinants of tumor cell dedifferentiation and plasticity in primary cutaneous melanoma 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.06.21.660851v1?rss=1"
</link>
<description><![CDATA[
Localized cutaneous melanoma can be cured by excision but success critically depends on early detection and risk assessment of primary lesions. However, their initiation, progression, and immunology remain poorly understood, partly due to high intra- and inter-tumor heterogeneity. We studied this heterogeneity using spatial profiling in over 300 histological domains, each representing a single progression stage, and found that 200-600 cell neighborhoods from a single melanoma can be as different in RNA and protein expression as neighborhoods from different tumors. These differences are not stochastic, however, and disease progression can be mapped at the neighborhood level onto a cell state landscape defined by the activity of the melanocyte master regulator MITF and genes associated with a dedifferentiated neural crest phenotype. Position in this landscape is influenced by proximity to immune cells, perivascular environments, and other tissue features, but no single association is absolute, giving rise to complex spatial patterns.
]]></description>
<dc:creator>Vallius, T.</dc:creator>
<dc:creator>Shi, Y.</dc:creator>
<dc:creator>Novikov, E.</dc:creator>
<dc:creator>Pant, S. M.</dc:creator>
<dc:creator>Pelletier, R.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Tefft, J. B.</dc:creator>
<dc:creator>Nirmal, A. J.</dc:creator>
<dc:creator>Maliga, Z.</dc:creator>
<dc:creator>Wan, G.</dc:creator>
<dc:creator>Murphy, G.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Semenov, Y.</dc:creator>
<dc:creator>Liu, D.</dc:creator>
<dc:creator>Lian, C. G.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2025-06-24</dc:date>
<dc:identifier>doi:10.1101/2025.06.21.660851</dc:identifier>
<dc:title><![CDATA[Spatial determinants of tumor cell dedifferentiation and plasticity in primary cutaneous melanoma]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-06-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.03.10.642376v1?rss=1">
<title>
<![CDATA[
Multiomic analysis identifies suppressive myeloid cell populations in human TB granulomas. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.03.10.642376v1?rss=1"
</link>
<description><![CDATA[
Tuberculosis (TB) remains a major global health challenge, particularly in the context of multidrug-resistant (MDR) Mycobacterium tuberculosis (Mtb). Host-directed therapies (HDTs) have been proposed as adjunctive therapy to enhance immune control of infection. Recently, one such HDT, pharmacologic modulation of myeloid-derived suppressor cells (MDSCs), has been proposed to treat MDR-TB. While MDSCs have been well characterized in cancer, their role in TB pathogenesis remains unclear. To investigate whether MDSCs or other myeloid suppressor populations contribute to TB granuloma microenvironments (GME), we performed spatial transcriptional profiling and single-cell immunophenotyping on eighty-four granulomas in lung specimens from three individuals with active disease. Granulomas were histologically classified based on H&E staining, and transcriptional signatures were compared across regions of interest (ROIs) at different states of granuloma maturation. Our analysis revealed that immune suppression within granuloma was not primarily driven by classical MDSCs but rather by multiple myeloid cell subsets, including dendritic cells expressing indoleamine 2,3 dioxygenase-1 expressing (IDO1+ DCs). IDO1+ DCs were the most frequently observed suppressive myeloid cells, particularly in cellular regions, and their spatial proximity to activated T cells suggested localized immunosuppression. Importantly, granulomas at different stages contained distinct proportions of suppressor myeloid cells, with necrotic and cellular regions showing different myeloid phenotypes that may influence granuloma progression. Gene set enrichment analysis (GSEA) further indicated that elevated IDO1 expression was associated with a complex immune response that balanced suppressive signaling, immune activation, and cellular metabolism. These findings suggest that classical MDSCs, as defined in tumor microenvironments, likely play a minor role in TB, whereas IDO1+ DCs may be key regulators of immune suppression in granulomas influencing local Mtb control in infected lung. A deeper understanding of the role of IDO1+ suppressive myeloid cells in TB granulomas is essential to assessing their potential as therapeutic targets in TB treatment.
]]></description>
<dc:creator>Jain, N.</dc:creator>
<dc:creator>Ogbanna, E. C.</dc:creator>
<dc:creator>Maliga, Z.</dc:creator>
<dc:creator>Jacobson, C. A.</dc:creator>
<dc:creator>Zhang, L.</dc:creator>
<dc:creator>Shih, A. R.</dc:creator>
<dc:creator>Rosenberg, J. M.</dc:creator>
<dc:creator>Kalam, H.</dc:creator>
<dc:creator>Gagne, A.</dc:creator>
<dc:creator>Solomon, I. H.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Aldridge, B. B.</dc:creator>
<dc:creator>Martinot, A. J.</dc:creator>
<dc:date>2025-03-13</dc:date>
<dc:identifier>doi:10.1101/2025.03.10.642376</dc:identifier>
<dc:title><![CDATA[Multiomic analysis identifies suppressive myeloid cell populations in human TB granulomas.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-03-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2026.01.14.699605v1?rss=1">
<title>
<![CDATA[
Ribosome-associated quality control of aberrant protein production during amino acid limitation 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2026.01.14.699605v1?rss=1"
</link>
<description><![CDATA[
Amino acids can become limiting for protein synthesis through depletion of charged tRNAs, leading to ribosome stalling and disruption of translation elongation at specific codons. To assess whether this is a mechanism by which amino acid availability can directly influence gene expression, we designed a reporter library to measure translation disruption across all sense codons in the context of amino acid limitations. We found that arginine limitation consistently impairs translation at the arginine codon AGA, resulting in synthesis of proteins from endogenous transcripts. In contrast, GCN2 pathway activation suppresses translation disruption following depletion of most other amino acids. Genome-wide screens revealed that the ribosome quality control trigger (RQC-T) and RQC pathways, which resolve ribosome collisions on defective mRNAs, catalyze ribosome splitting and premature fall-off in response to arginine depletion. Additionally, the E3 ubiquitin ligase RNF14, recently shown to clear ribosome A-site obstructions, promotes translation disruption through both ribosome fall-off and frameshifting during arginine limitation. Together, these data show that the RQC machinery is engaged by tRNA-limited ribosomes and identify a new role for RNF14 as a regulator of translation upon arginine limitation.
]]></description>
<dc:creator>Darnell, A. M.</dc:creator>
<dc:creator>Chidley, C.</dc:creator>
<dc:creator>Paradise, V.</dc:creator>
<dc:creator>Cui, D. S.</dc:creator>
<dc:creator>Davidsen, K.</dc:creator>
<dc:creator>Lincoln, S. C.</dc:creator>
<dc:creator>Abbott, K. L.</dc:creator>
<dc:creator>Elbashir, R.</dc:creator>
<dc:creator>Vander Heiden, C. P.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Sullivan, L. B.</dc:creator>
<dc:creator>Davis, J. H.</dc:creator>
<dc:creator>Vander Heiden, M. G.</dc:creator>
<dc:date>2026-01-15</dc:date>
<dc:identifier>doi:10.64898/2026.01.14.699605</dc:identifier>
<dc:title><![CDATA[Ribosome-associated quality control of aberrant protein production during amino acid limitation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2026-01-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2026.01.05.697601v1?rss=1">
<title>
<![CDATA[
In vivo evidence for bleb-induced survival signaling in metastatic melanoma 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2026.01.05.697601v1?rss=1"
</link>
<description><![CDATA[
Bleb signaling is a cellular process in which pressure-driven plasma membrane protrusions generate localized micron-scale membrane curvature that recruits cytosolic septin complexes, assembling signaling hubs that promote cell survival1. Our prior work showed that this morphology-encoded signaling pathway is necessary to sustain anchorage-independent survival of BRAF and NRAS mutant melanoma cells in vitro. However, whether bleb-induced signaling occurs in vivo and contributes to cancer progression is unclear. Here, we develop complementary in vivo and ex vivo assays spanning human patient samples and mouse xenograft models to mechanistically interrogate bleb signaling directly in physiologic contexts. We observe that bleb-associated septin hubs are exclusively formed in poorly adherent amoeboid tumor cells at the invasive margin, within malignant effusions, and at distant metastatic sites, while well-adhered cells in the tumor interior show no signs of septin hub formation. Accordingly, pharmacological pathway disruption specifically kills disseminated tumor cells within these low-adhesion microenvironments while having no appreciable effect on adhered cells in the tumor interior, resulting in reduced metastatic burden and delayed disease recurrence in vivo. This work confirms septin-mediated bleb signaling as a previously unrecognized vulnerability of disseminated cancer cells and demonstrates that this morphology-encoded pathway operates in vivo to support disease progression, preserving cancer cell viability under conditions in which cell survival signals from the environment are muted. These findings suggest novel opportunities to target survival signaling in micrometastatic disseminated cancer cells.



O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/697601v1_ufig1.gif" ALT="Figure 1">
View larger version (61K):
org.highwire.dtl.DTLVardef@1770384org.highwire.dtl.DTLVardef@87e567org.highwire.dtl.DTLVardef@1e5d6b2org.highwire.dtl.DTLVardef@724c5f_HPS_FORMAT_FIGEXP  M_FIG C_FIG
]]></description>
<dc:creator>Weems, A. D.</dc:creator>
<dc:creator>Islam, M. T.</dc:creator>
<dc:creator>Borges, H.</dc:creator>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Perez-Castro, L.</dc:creator>
<dc:creator>Nogueira, P.</dc:creator>
<dc:creator>Lin, J.</dc:creator>
<dc:creator>Maliga, Z.</dc:creator>
<dc:creator>Lewis, A. J.</dc:creator>
<dc:creator>Kutter, C.</dc:creator>
<dc:creator>Mannino, M.</dc:creator>
<dc:creator>Vallius, T.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Conacci-Sorrell, M.</dc:creator>
<dc:creator>Dean, K. M.</dc:creator>
<dc:creator>Danuser, G. M.</dc:creator>
<dc:date>2026-01-05</dc:date>
<dc:identifier>doi:10.64898/2026.01.05.697601</dc:identifier>
<dc:title><![CDATA[In vivo evidence for bleb-induced survival signaling in metastatic melanoma]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2026-01-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.12.11.692633v1?rss=1">
<title>
<![CDATA[
Mechanisms of tumor persistence in metastatic melanoma following successful immunotherapy 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.12.11.692633v1?rss=1"
</link>
<description><![CDATA[
Persistent stable lesions represent a common but ambiguous outcome in melanoma patients receiving immune checkpoint inhibitors (ICIs). However, these lesions are infrequently removed and poorly characterized. Here, we perform in-depth multi-omics spatial profiling on persistent stable lesions from six ICI-treated patients. In one, the proportion of viable and proliferating tumor cells was similar to that of site-matched tumors from patients progressing during ICI. Extensive infiltration with cytotoxic T cells and a high level of programmed cell death were also observed. Some quiescent cancer cells were present, but this was not the dominant tumor state. A second stable lesion, while pathologically negative, also contained proliferative tumor nests with proximate immune cells. These findings provide evidence in patients for extended tumor mass dormancy in which cell death balances ongoing proliferation and further demonstrate that persistent stable lesions can be reservoirs of viable tumor cells with implications for clinical monitoring and management.

SIGNIFICANCEThis study demonstrates that, following immunotherapy, persistent stable melanomas can arise through tumor mass dormancy rather than quiescence. Mass dormancy is characterized by active tumor cell proliferation and compensatory programmed cell death. Thus, viable tumor cells in some stable lesions have the potential to reactivate disease should ongoing immunosurveillance fail.
]]></description>
<dc:creator>Shi, Y.</dc:creator>
<dc:creator>Maliga, Z.</dc:creator>
<dc:creator>Vallius, T.</dc:creator>
<dc:creator>Pant, S. M.</dc:creator>
<dc:creator>Pelletier, R.</dc:creator>
<dc:creator>Kobs, B.</dc:creator>
<dc:creator>Solanky, P.</dc:creator>
<dc:creator>He, Y.</dc:creator>
<dc:creator>Van Allen, E.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Ott, P.</dc:creator>
<dc:creator>Lian, C. G.</dc:creator>
<dc:creator>Buchbinder, E.</dc:creator>
<dc:creator>Liu, D.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2025-12-15</dc:date>
<dc:identifier>doi:10.64898/2025.12.11.692633</dc:identifier>
<dc:title><![CDATA[Mechanisms of tumor persistence in metastatic melanoma following successful immunotherapy]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-12-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.11.06.686771v1?rss=1">
<title>
<![CDATA[
Integrative single-cell and spatial mapping of oxidative stress response uncovers GCLC+ mesenchymal tumor cell state linked with favorable outcomes in triple negative breast cancer 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.11.06.686771v1?rss=1"
</link>
<description><![CDATA[
Inhibiting oxidative stress response (OSR) proteins has been suggested as a therapeutic strategy in triple negative breast cancer (TNBC). However, the cell type specificity and spatial distribution of OSR genes and proteins, such as GCLC and NQO1, is unknown. Using single cell and spatial transcriptomics datasets we found that OSR genes were highly expressed in TNBC tumor cells, which localized in spatial clusters. Multiplex immunofluorescence imaging of 345 TNBC samples from 186 patients demonstrated that OSR proteins GCLC and NQO1 exhibit distinct expression profiles across tumor, immune, and stromal cell populations and are elevated in inflamed histological regions. Tumor cell OSR protein expression was associated with the composition of the adjacent cellular neighborhood. Furthermore, we identified GCLC and vimentin positive (GCLC+VIM+) mesenchymal-like tumor cells, residing near immune cells and exhibiting increased proliferation and decreased anastasis signatures, suggesting sensitivity to chemotherapy. Across a panel of thirteen TNBC cell lines, GCLC expression was positively correlated with sensitivity to cisplatin. Cox regression analysis revealed that patients with higher proportions of GCLC+VIM+ tumor cells had a longer overall survival. Collectively, our results demonstrate that individual OSR proteins are expressed in distinct microenvironments and tumor cell states, potentially contributing to patient outcomes.
]]></description>
<dc:creator>Girnius, N.</dc:creator>
<dc:creator>Vallius, T.</dc:creator>
<dc:creator>Chen, W.</dc:creator>
<dc:creator>Launonen, I.-M.</dc:creator>
<dc:creator>Palomino-Echeverria, S.</dc:creator>
<dc:creator>Lin, J.-R.</dc:creator>
<dc:creator>Mills, C. E.</dc:creator>
<dc:creator>Kauppila, S.</dc:creator>
<dc:creator>Kronqvist, P.</dc:creator>
<dc:creator>Ellonen, A.</dc:creator>
<dc:creator>Perala, M.</dc:creator>
<dc:creator>Withnell, E.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Secrier, M.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Farkkila, A.</dc:creator>
<dc:date>2025-11-07</dc:date>
<dc:identifier>doi:10.1101/2025.11.06.686771</dc:identifier>
<dc:title><![CDATA[Integrative single-cell and spatial mapping of oxidative stress response uncovers GCLC+ mesenchymal tumor cell state linked with favorable outcomes in triple negative breast cancer]]></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/2025.09.21.677465v1?rss=1">
<title>
<![CDATA[
Spatially organized lymphocytic microenvironments in high grade primary prostate tumors 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.09.21.677465v1?rss=1"
</link>
<description><![CDATA[
The spatial organization and composition of the tumor-immune microenvironment (TME) play a critical role in shaping the progression of many solid cancers, but the organization of the TME in primary prostate cancer (PCa) remains poorly characterized. We therefore profiled the abundance and spatial distributions of major cell types involved in adaptive immunity in 29 radical prostatectomy specimens stratified into high (HGG; n=14) and low Gleason-grade (LGG; n=15). Compared to LGG, HGG PCa exhibited significantly greater B and T cell infiltration with many immune cells organized into clusters, some of which resembled tertiary lymphoid structures (TLSs). In HGG tumors, these clusters were dense, symmetric, rich in PD-1+ T cells, and frequently proximate to the tumor compartment. LGG clusters were less well organized, and T cell depleted. Thus, a subset of high-grade PCa harbor organized immune clusters that may play a role in tumor control and contain therapeutically targetable T and B cells.
]]></description>
<dc:creator>Amiryousefi, A.</dc:creator>
<dc:creator>Wala, J.</dc:creator>
<dc:creator>Lin, J.-R.</dc:creator>
<dc:creator>Labadie, B. W.</dc:creator>
<dc:creator>Atmakuri, A.</dc:creator>
<dc:creator>Maliga, Z.</dc:creator>
<dc:creator>Toye, E.</dc:creator>
<dc:creator>Chaudagar, K.</dc:creator>
<dc:creator>Torcasso, M. S.</dc:creator>
<dc:creator>Coy, S.</dc:creator>
<dc:creator>Fanelli, G. N.</dc:creator>
<dc:creator>Kobs, B.</dc:creator>
<dc:creator>Socciarelli, F.</dc:creator>
<dc:creator>Gagne, A.</dc:creator>
<dc:creator>Van Allen, E.</dc:creator>
<dc:creator>Patnaik, A.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2025-09-21</dc:date>
<dc:identifier>doi:10.1101/2025.09.21.677465</dc:identifier>
<dc:title><![CDATA[Spatially organized lymphocytic microenvironments in high grade primary prostate tumors]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-09-21</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.10.01.679282v1?rss=1">
<title>
<![CDATA[
Multiomic profiling of a unique in-transit melanoma cohort identifies melanoma differentiation as predictor of tumor progression and therapy response 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.10.01.679282v1?rss=1"
</link>
<description><![CDATA[
Melanoma patients with in-transit metastasis (ITM), a stage of disease where melanoma has metastasized to sites in between the primary lesion and draining lymph node, vary significantly in their clinical outcomes, but the biology driving differential outcomes in ITM is poorly understood. To elucidate the mechanisms of differential outcomes, we utilized multimodal molecular profiling (WES, RNA-seq, highly multiplexed immunofluorescence, spatial transcriptomics) in 1) evolutionary analysis of longitudinal tumor samples and 2) identifying prognostic tumor intrinsic and microenvironmental features in a unique cohort of patients with unresectable ITM. Among other findings, we observed a persistent dedifferentiated AXL/NGFR clonal lineage pre-existing and following immune checkpoint blockade in in-transit and distant metastases. Concordantly, we found that low pigmentation and high T cell exhaustion signatures were independently associated with distant progression. Our findings highlight tumor cell state and immune dysfunction as key predictors and potential biomarkers of metastatic risk in ITM.

STATEMENT OF SIGNIFICANCEWhat drives distant progression in melanoma is unclear. Analyzing tumor and immune features in a rare in-transit melanoma patient cohort, we identify biological signals highlighting how immune and tumor states observable in pre-distant metastasis melanomas shape long-term outcomes, and nominate potential prognostic biomarkers.
]]></description>
<dc:creator>Tarantino, G.</dc:creator>
<dc:creator>Zaremba, A.</dc:creator>
<dc:creator>Vallius, T.</dc:creator>
<dc:creator>Woodnorth, M.</dc:creator>
<dc:creator>Hua, Y.</dc:creator>
<dc:creator>Pelletier, R.</dc:creator>
<dc:creator>Lopez Leon, M.</dc:creator>
<dc:creator>Shi, Y.</dc:creator>
<dc:creator>Maliga, Z.</dc:creator>
<dc:creator>Makhzami, S.</dc:creator>
<dc:creator>Aprati, T. J.</dc:creator>
<dc:creator>Karlas, B.</dc:creator>
<dc:creator>Glutsch, V.</dc:creator>
<dc:creator>Schilling, B.</dc:creator>
<dc:creator>Hassel, J.</dc:creator>
<dc:creator>Berking, C.</dc:creator>
<dc:creator>Utikal, J.</dc:creator>
<dc:creator>Meier, F.</dc:creator>
<dc:creator>Meiss, F.</dc:creator>
<dc:creator>Heinzerling, L.</dc:creator>
<dc:creator>Kahler, K.</dc:creator>
<dc:creator>Chen, J.</dc:creator>
<dc:creator>Zimmer, L.</dc:creator>
<dc:creator>Sucker, A.</dc:creator>
<dc:creator>Livingstone, E.</dc:creator>
<dc:creator>Hadaschik, E.</dc:creator>
<dc:creator>Lian, C.</dc:creator>
<dc:creator>Murphy, G.</dc:creator>
<dc:creator>Semenov, Y. R.</dc:creator>
<dc:creator>Boland, G. M.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Rambow, F.</dc:creator>
<dc:creator>Liu, D.</dc:creator>
<dc:creator>Schadendorf, D.</dc:creator>
<dc:date>2025-10-02</dc:date>
<dc:identifier>doi:10.1101/2025.10.01.679282</dc:identifier>
<dc:title><![CDATA[Multiomic profiling of a unique in-transit melanoma cohort identifies melanoma differentiation as predictor of tumor progression and therapy response]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-10-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.07.23.666465v1?rss=1">
<title>
<![CDATA[
Antigen reactivity defines tissue-resident memory and exhausted T cells in tumours 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.07.23.666465v1?rss=1"
</link>
<description><![CDATA[
CD8+ T cells are a key weapon in the therapeutic armamentarium against cancer. While CD8+CD103+ T cells with a tissue-resident memory T (TRM) cell phenotype have been favourably correlated with patient prognoses1-6, the tumour microenvironment also contains dysfunctional exhausted T (TEX) cells that exhibit a myriad of TRM-like features, leading to conflation of these two populations. Here, we deconvolute TRM and TEX cells within the intratumoural CD8+CD103+ T cell pool across human cancers, ascribing markers and gene signatures that distinguish these CD8+ populations and enable their functional distinction. We found that while TRM cells exhibit superior functionality and are associated with long-term survival post-tumour resection, they are not associated with responsiveness to immune checkpoint blockade. Deconvolution of the two populations showed that tumour-associated TEX and TRM cells are clonally distinct, with the latter comprising both tumour-independent bystanders and tumour-specific cells segregated from their cognate antigen. Intratumoural TRM cells can be forced towards an exhausted fate when chronic antigen stimulation occurs, arguing that the presence or absence of continuous antigen exposure within the microenvironment is the key distinction between respective tumour-associated TEX and TRM populations. These results suggest unique roles for TRM and TEX cells in tumour control, underscoring the need for distinct strategies to harness these T cell populations in novel cancer therapies.
]]></description>
<dc:creator>Burn, T. N.</dc:creator>
<dc:creator>Schroeder, J.</dc:creator>
<dc:creator>Gandolfo, L. C.</dc:creator>
<dc:creator>Osman, M.</dc:creator>
<dc:creator>Wainwright, E. N.</dc:creator>
<dc:creator>Lam, E. Y. N.</dc:creator>
<dc:creator>McDonald, K. M.</dc:creator>
<dc:creator>Evans, R. B.</dc:creator>
<dc:creator>Lee, S.</dc:creator>
<dc:creator>Rawlinson, D.</dc:creator>
<dc:creator>Dryburgh, L.</dc:creator>
<dc:creator>Zaid, A.</dc:creator>
<dc:creator>Maliga, Z.</dc:creator>
<dc:creator>Schienstock, D.</dc:creator>
<dc:creator>Meiser, P.</dc:creator>
<dc:creator>Lee, H. J.</dc:creator>
<dc:creator>Lai, H.</dc:creator>
<dc:creator>Moreira, M. L.</dc:creator>
<dc:creator>Zareie, P.</dc:creator>
<dc:creator>Lee, H.-Y.</dc:creator>
<dc:creator>Huq, L.</dc:creator>
<dc:creator>Christo, S. N.</dc:creator>
<dc:creator>Seow, J. J. W.</dc:creator>
<dc:creator>Ching, K. A.</dc:creator>
<dc:creator>Guillaume, S. M.</dc:creator>
<dc:creator>Knezevic, K.</dc:creator>
<dc:creator>Park, S. L.</dc:creator>
<dc:creator>Evrard, M.</dc:creator>
<dc:creator>Waithman, J.</dc:creator>
<dc:creator>Gebhardt, T.</dc:creator>
<dc:creator>Mueller, S. N.</dc:creator>
<dc:creator>Riddiough, G. E.</dc:creator>
<dc:creator>Perini, M. V.</dc:creator>
<dc:creator>Tsao, S. C. H.</dc:creator>
<dc:creator>Speed, T. P.</dc:creator>
<dc:creator>Sorger, P.</dc:creator>
<dc:creator>Loi, S.</dc:creator>
<dc:creator>Carbone, F. R.</dc:creator>
<dc:creator>Gras, S.</dc:creator>
<dc:creator>Fisher, T. S.</dc:creator>
<dc:creator>Baaten, B. J.</dc:creator>
<dc:creator>Dawson, M. A.</dc:creator>
<dc:creator>Mackay, L. K.</dc:creator>
<dc:date>2025-07-23</dc:date>
<dc:identifier>doi:10.1101/2025.07.23.666465</dc:identifier>
<dc:title><![CDATA[Antigen reactivity defines tissue-resident memory and exhausted T cells in tumours]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-07-23</prism:publicationDate>
<prism:section></prism:section>
</item>
</rdf:RDF>
