Biology subjects

Sorger, P.

Publications and source records attributed to Sorger, P..

16 recordsLinked to original sources

bioRxiv2026

Multiscale harmonization and semantic integration of biomedical data enable biological insights through immersive exploration

Single-cell atlassing efforts like the Human BioMolecular Atlas Program (HuBMAP) and the Cellular Senescence Network (SenNet) are producing multiscale datasets across the healthy, adult, human body, but this data is typically explored only on 2D screens with limited 3D affordances, even though understanding a cells location within a tissue, organ, and body requires reasoning across many orders of spatial magnitude. The Human Reference Atlas (HRA) provides standard terminologies and a Common Coordinate Framework (CCF) for harmonizing such data spatially and semantically. Building on the HRA Organ Gallery in virtual reality (VR) application, we present "HRA: Powers of Ten," which integrates, harmonizes, and visualizes biological data from these efforts immersively in VR using a Multiscale Elevator System that lets users descend, like riding an elevator through an inverted skyscraper, from a whole body view of 81 reference organs to datasets across 5 organs (lymph node, brain, large intestine, small intestine, liver), 5 assay types (CODEX, Visium, v-CyCIF, Xenium, SBF-SEM), and 4 spatial scales. Contributed by Data Providers, these VR scenes enable biological insights into senescence patterns, cellular neighborhoods, 3D tissue reconstruction, and subcellular liver architecture. A standard operating procedure supports adding further datasets. Freely available on the Meta Store to over 20 million headset owners, the application, data, and code are open-source.

bioinformatics↗

bioRxiv2025

Antigen reactivity defines tissue-resident memory and exhausted T cells in tumours

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.

immunology↗

bioRxiv2024

Integrating spatial profiles and cancer genomics to identify immune-infiltrated mismatch repair proficient colorectal cancers

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.

cancer biology↗

bioRxiv2024

TYK2 as a novel therapeutic target in Alzheimer Disease

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.

neuroscience↗

bioRxiv2023

Cell Spotter (CSPOT): A machine-learning approach to automated cell spotting and quantification of highly multiplexed tissue images

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.

bioinformatics↗

bioRxiv2020

Multiplexed proteomics and imaging of resolving and lethal SARS-CoV-2 infection in the lung

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.

pathology↗

bioRxiv2020

Machine Learning Identifies Novel Candidates for DrugRepurposing in Alzheimer's Disease

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.

neuroscience↗

bioRxiv2020

Exploring the understudied human kinome for research and therapeutic opportunities

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.

systems biology↗

bioRxiv2020

Interpretative guides for interacting with tissue atlas and digital pathology data using the Minerva browser

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.

scientific communication and education↗

bioRxiv2019

Highly multiplexed immunofluorescence images and single-cell data of immune markers in tonsil and lung cancer

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.\n\nMETADATA SUMMARY\n\nO_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

cancer biology↗

bioRxiv2018

Comparing the efficacy of cancer therapies between subgroups in basket trials

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.

cancer biology↗

bioRxiv2018

Cheminformatics tools for analyzing and designing optimized small molecule libraries

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.

systems biology↗

bioRxiv2017

Bioentities: a resource for entity recognition and relationship resolution in biomedical text mining

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.\n\nResultsIn 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.\n\nConclusionFamPlex 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.

bioinformatics↗

bioRxiv2017

Therapeutically advantageous secondary targets of abemaciclib identified by multi-omics profiling of CDK4/6 inhibitors

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.\n\nSIGNIFICANCEThe 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.

cancer biology↗

bioRxiv2017

Receptor-Based Mechanism of Relative Sensing in Mammalian Signaling Networks

Detecting relative rather than absolute changes in external signals enables cells to make decisions in fluctuating environments and diverse biological contexts. However, how mammalian signaling networks store the memories of past stimuli and use them to compute relative signals is not well understood. Using the growth factor-activated PI3K-Akt signaling pathway, we develop computational and analytical models, and experimentally validate a novel mechanism of relative sensing in mammalian cells. This non-transcriptional mechanism relies on a new form of cellular memory, where cells effectively encode past stimulation levels in the abundance of cognate receptors on the cell surface. We show the robustness and specificity of the relative sensing for two physiologically important ligands, epidermal growth factor (EGF) and hepatocyte growth factor (HGF), and across wide ranges of background stimuli. The described memory and sensing mechanism could play a role in multiple other sensory cascades where stimulation leads to a proportional reduction in the abundance of cell surface receptors.

systems biology↗

bioRxiv2017

A simple open-source method for highly multiplexed imaging of single cells in tissues and tumours

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.

cancer biology↗