Biology subjects

Regev, A.

Publications and source records attributed to Regev, A..

At least 73 records · Page 4Linked to original sources

bioRxiv2022

SnFFPE-Seq: towards scalable single nucleus RNA-Seq of formalin-fixed paraffin-embedded (FFPE) tissue

Profiling cellular heterogeneity in formalin-fixed paraffin-embedded (FFPE) tissues is key to characterizing clinical specimens for biomarkers, therapeutic targets, and drug responses. Here, we optimize methods for isolating intact nuclei and single nucleus RNA-Seq from FFPE tissues in the mouse brain, and demonstrate a pilot application to a human clinical specimen of lung adenocarcinoma. Our method opens the way to broad applications of snRNA-Seq to archival tissues, including clinical samples.

genomics↗

bioRxiv2022

Spatial host-microbiome sequencing

Mucosal and barrier tissues such as the gut, lung or skin, are composed of a complex network of cells and microbes forming a tight niche that prevents pathogen colonization and supports host-microbiome symbiosis. Characterizing these networks at high molecular and cellular resolution is crucial for our understanding of homeostasis and disease. Spatial transcriptomics has emerged as a key technology to positionally profile RNAs at high resolution in tissues. Here, we present spatial host-microbiome sequencing, an all-sequencing based approach that captures tissue histology, polyadenylated RNAs and bacterial 16S sequences directly from tissues on spatially barcoded glass surfaces. We apply our approach to the mouse gut as a model system, use a novel deep learning approach for data mapping and detect spatial niches impacted by microbial biogeography. Spatial host-microbiome sequencing should enhance study of native host-microbe interactions in health and disease.

genomics↗

bioRxiv2022

Temporal single cell atlas of non-neuronal retinal cells reveals dynamic, coordinated multicellular responses to central nervous system injury

Non-neuronal cells play key roles in the complex cellular interplay that follows central nervous system (CNS) insult. To understand this interplay at a tissue level, we generated a single-cell atlas of immune, glial and retinal pigment epithelial cells from adult mouse retina before and at multiple time points after axonal transection (optic nerve crush; ONC), identifying rare and undescribed subsets, and delineating changes in cell composition, expression programs, and interactions. Computational analysis charted an inflammatory cascade after injury with three phases. The early phase consisted of reactivation of retinal macroglia and microglia, providing chemotactic signals for immune infiltration, concurrent with infiltration of CCR2+ monocytes from the circulation. In the second phase, these differentiated to macrophage subsets resembling resident border-associated macrophages. In parallel, a multicellular interferon program, likely driven by microglia-derived type-I interferon, was synchronously activated across resident glia, expanding beyond rare interferon-responding subsets of glia unexpectedly present in the naive retina. Our findings provide insights regarding post-injury CNS tissue dynamics and a framework to decipher cellular circuitry, spatial relationships and molecular interactions following tissue injury.

immunology↗

bioRxiv2022

Single cell RNA-seq by mostly-natural sequencing by synthesis

Massively parallel single cell RNA-seq (scRNA-seq) for diverse applications, from cell atlases to functional screens, is increasingly limited by sequencing costs, and large-scale low-cost sequencing can open many additional applications, including patient diagnostics and drug screens. Here, we adapted and systematically benchmarked a newly developed, mostly-natural sequencing by synthesis method for scRNA-seq. We demonstrate successful application in four scRNA-seq case studies of different technical and biological types, including 5 and 3 scRNA-seq, human peripheral blood mononuclear cells from a single individual and in multiplex, as well as Perturb-Seq. Our data show comparable results to existing technology, including compatibility with state-of-the-art scRNA-seq libraries independent of the sequencing technology used - thus providing an enhanced cost-effective path for large scale scRNA-seq.

genomics↗

bioRxiv2022

A global atlas of substrate specificities for the human serine/threonine kinome

Protein phosphorylation is one of the most widespread post-translational modifications in biology. With the advent of mass spectrometry-based phosphoproteomics, more than 200,000 sites of serine and threonine phosphorylation have been reported, of which several thousand have been associated with human diseases and biological processes. For the vast majority of phosphorylation events, it is not yet known which of the more than 300 protein Ser/Thr kinases encoded in the human genome is responsible. Here, we utilize synthetic peptide libraries to profile the substrate sequence specificity of nearly every functional human Ser/Thr kinase. Viewed in its entirety, the substrate specificity of the kinome was substantially more diverse than expected and was driven extensively by negative selectivity. Our kinome-wide dataset was used to computationally annotate and identify the most likely protein kinases for every reported phosphorylation site in the human Ser/Thr phosphoproteome. For the small minority of phosphosites where the protein kinases involved have been previously identified, our predictions were in excellent agreement. When this approach was applied to examine the signaling response of tissues and cell lines to hormones, growth factors, targeted inhibitors, and environmental or genetic perturbations, it revealed unexpected insights into pathway complexity and compensation. Overall, these studies reveal the full extent of substrate specificity of the human Ser/Thr kinome, illuminate cellular signaling responses, and provide a rich resource to link unannotated phosphorylation events to biological pathways.

systems biology↗

bioRxiv2022

Single-cell multiomics atlas of organoid development uncovers longitudinal molecular programs of cellular diversification of the human cerebral cortex

Realizing the full utility of brain organoids as experimental systems to study human cortical development requires understanding whether organoids replicate the cellular and molecular events of this complex process precisely, reproducibly, and with fidelity to the embryo. Here we present a comprehensive single-cell transcriptomic, epigenetic, and spatial atlas of human cortical organoid development, comprising over 610,000 cells, spanning initial generation of neural progenitors through production of differentiated neuronal and glial subtypes. We define the lineage relationships and longitudinal molecular trajectories of cortical cell types during development in organoids, and show that developmental processes of cellular diversification in organoids correlate closely to endogenous ones, irrespective of metabolic state. Using this data, we identify genes with predicted human-specific roles in lineage establishment, and discover a developmental origin for the transcriptional diversity of human callosal projection neurons, a population that has undergone dramatic expansion and diversification during human evolution. Our work provides a comprehensive, single-cell molecular map of human corticogenesis in vitro, identifying developmental trajectories and molecular mechanisms associated with human cellular diversification.

neuroscience↗

bioRxiv2022

The chromatin landscape of Th17 cells reveals mechanisms of diversification of regulatory and pro-inflammatory states

Th17 cells are a heterogenous cell population consisting of non-pathogenic Th17 cells (npTh17) that contribute to tissue homeostasis and pathogenic Th17 cells (pTh17) that are potent mediators of tissue inflammation. To reveal regulatory mechanisms underlying Th17 heterogeneity, we performed combined ATAC-seq and RNA-seq and discovered substantial differences in the chromatin landscape of npTh17 and pTh17 cells both in vitro and in vivo. Compared to other CD4+ T cell subsets, npTh17 cells share accessible chromatin programs with Tregs, and pTh17 cells have an intermediate profile spanning features of npTh17 cells and Th1 cells. Integrating single-cell ATAC-seq and single-cell RNA-seq, we inferred self-reinforcing and mutually exclusive regulatory networks controlling the different cell states and predicted transcription factors (TFs) shaping the chromatin landscape of Th17 cell pathogenicity. We validated one novel TF, BACH2, which promotes immunomodulatory npTh17 programs and restrains pro-inflammatory Th1-like programs in Th17 cells and showed genetic evidence for protective variants in the human BACH2 locus associated with multiple sclerosis. Our work uncovered mechanisms that regulate Th17 heterogeneity, revealed shared regulatory programs with other CD4+ T cell subsets, and identified novel drivers of Th17 pathogenicity as potential targets to mitigate autoimmunity.

immunology↗

bioRxiv2022

Leukemia-intrinsic determinants of CAR-T response revealed by in vivo genome-wide CRISPR screening.

CAR-T therapy is a promising new treatment modality for B-cell malignancies. However, the majority of patients inevitably go on to experience disease relapse through largely unknown means. To investigate leukemia-intrinsic CAR-T resistance mechanisms, we performed genome-wide CRISPR-Cas9 loss-of-function screens in an immunocompetent murine model of B-cell acute lymphoblastic leukemia (B-ALL) utilizing a novel, modular guide RNA library. We identified IFN{gamma}/JAK/STAT signaling and components of antigen processing and presentation pathway as key mediators of resistance to CAR-T therapy in vivo, but not in vitro. Transcriptional characterization of this model demonstrated an upregulation of these pathways in CAR-T treated relapsed tumors, and examination of data from CAR-T treated patients with B-ALL revealed an association between poor outcomes and increased expression of JAK/STAT/MHC-I in leukemia cells. Overall, our data identify an unexpected mechanism of resistance to CAR-T therapy in which tumor cell interaction with CAR-T cells in vivo induces expression of an adaptive T-cell resistance program in tumor cells.

cancer biology↗

bioRxiv2022

Remodeling of human colon plasma cell repertoire in ulcerative colitis

Plasma cells (PCs) constitute a significant fraction of cells in colonic mucosa and contribute to inflammatory lymphocytic infiltrates in ulcerative colitis (UC). While gut PCs secrete 3-5 g of immunoglobulins daily, including IgA antibodies that target colitogenic bacteria, their role in UC is not known. Here, we combined B cell sorting with single-cell VDJ- and RNA-seq and monoclonal antibody (mAb) testing to characterize the colonic PC repertoire in healthy individuals and patients with UC. We show that a large fraction of B cell clones is shared between different colon regions and that inflammation in UC disrupts this landscape, causing clonal expansion and isotype skewing from IgA1 and IgA2 to IgG1. mAbs produced from expanded PC clones show low polyreactivity and autoreactivity and target specific bacterial strains. Expression profiles of individual PCs from inflamed and non-inflamed colon regions indicate that inflammation is associated with up-regulation of the unfolded protein response (UPR) and antigen presentation genes. Together, our results characterize the microbiome-specific PC response in the colon, its disruption in UC and how PCs might contribute to inflammation in UC.

immunology↗

bioRxiv2022

Power analysis for spatial omics

As spatially-resolved multiplex profiling of RNA and proteins becomes more prominent, it is increasingly important to understand the statistical power available to test specific hypotheses when designing and interpreting such experiments. Ideally, it would be possible to create an oracle that predicts sampling requirements for generalized spatial experiments. However, the unknown number of relevant spatial features and the complexity of spatial data analysis makes this challenging. Here, we enumerate multiple parameters of interest that should be considered in the design of a properly powered spatial omics. We introduce a method for tunable in silico tissue generation, and use it with spatial profiling datasets to construct an exploratory computational framework for single cell spatial power analysis. Finally, we demonstrate that our framework can be applied across diverse spatial data modalities and tissues of interest.

bioinformatics↗

bioRxiv2022

Heterochronic parabiosis reprograms the mouse brain transcriptome by shifting aging signatures in multiple cell types

Aging is a complex process involving transcriptomic changes associated with deterioration across multiple tissues and organs, including the brain. Recent studies using heterochronic parabiosis have shown that various aspects of aging-associated decline are modifiable or even reversible. To better understand how this occurs, we performed single-cell transcriptomic profiling of young and old mouse brains following parabiosis. For each cell type, we catalogued alterations in gene expression, molecular pathways, transcriptional networks, ligand-receptor interactions, and senescence status. Our analyses identified gene signatures demonstrating that heterochronic parabiosis regulates several hallmarks of aging in a cell-type-specific manner. Brain endothelial cells were found to be especially malleable to this intervention, exhibiting dynamic transcriptional changes that affect vascular structure and function. These findings suggest novel strategies for slowing deterioration and driving regeneration in the aging brain through approaches that do not rely on disease-specific mechanisms or actions of individual circulating factors.

neuroscience↗

bioRxiv2021

A genetic disorder reveals a hematopoietic stem cell regulatory network co-opted in leukemia

The molecular regulation of human hematopoietic stem cell (HSC) maintenance is therapeutically important, but limitations in experimental systems and interspecies variation have constrained our knowledge of this process. Here, we have studied a rare genetic disorder due to MECOM haploinsufficiency, characterized by an early-onset absence of HSCs in vivo. By generating a faithful model of this disorder in primary human HSCs and coupling functional studies with integrative single-cell genomic analyses, we uncover a key transcriptional network involving hundreds of genes that is required for HSC maintenance. Through our analyses, we nominate cooperating transcriptional regulators and identify how MECOM prevents the CTCF-dependent genome reorganization that occurs as HSCs differentiate. Strikingly, we show that this transcriptional network is co-opted in high-risk leukemias, thereby enabling these cancers to acquire stem cell properties. Collectively, we illuminate a regulatory network necessary for HSC self-renewal through the study of a rare experiment of nature.

cell biology↗

bioRxiv2021

Raman2RNA: Live-cell label-free prediction of single-cell RNA expression profiles by Raman microscopy

Single cell RNA-Seq (scRNA-seq) and other profiling assays have opened new windows into understanding the properties, regulation, dynamics, and function of cells at unprecedented resolution and scale. However, these assays are inherently destructive, precluding us from tracking the temporal dynamics of live cells, in cell culture or whole organisms. Raman microscopy offers a unique opportunity to comprehensively report on the vibrational energy levels of molecules in a label-free and non-destructive manner at a subcellular spatial resolution, but it lacks in genetic and molecular interpretability. Here, we developed Raman2RNA (R2R), an experimental and computational framework to infer single-cell expression profiles in live cells through label-free hyperspectral Raman microscopy images and multi-modal data integration and domain translation. We used spatially resolved single-molecule RNA-FISH (smFISH) data as anchors to link scRNA-seq profiles to the paired spatial hyperspectral Raman images, and trained machine learning models to infer expression profiles from Raman spectra at the single-cell level. In reprogramming of mouse fibroblasts into induced pluripotent stem cells (iPSCs), R2R accurately (r>0.96) inferred from Raman images the expression profiles of various cell states and fates, including iPSCs, mesenchymal-epithelial transition (MET) cells, stromal cells, epithelial cells, and fibroblasts. R2R outperformed inference from brightfield images, showing the importance of spectroscopic content afforded by Raman microscopy. Raman2RNA lays a foundation for future investigations into exploring single-cell genome-wide molecular dynamics through imaging data, in vitro and in vivo.

genomics↗

bioRxiv2021

ECLIPSER: identifying causal cell types and genes for complex traits through single cell enrichment of e/sQTL-mapped genes in GWAS loci

SummaryECLIPSER was developed to identify pathogenic cell types and cell type-specific genes that may affect complex disease susceptibility and trait variation by integrating single cell data with known GWAS loci. ECLIPSER maps genes to GWAS loci for a given complex trait based on expression and splicing quantitative trait loci (e/sQTLs) and other functional data, and tests whether the mapped genes are enriched for cell type-specific expression in particular cell types using single-cell/nucleus RNA-seq data from one or more tissues of interest. A Bayesian Fishers exact test is used to compute fold-enrichment significance. We demonstrate the application of ECLIPSER on various skin diseases and traits using snRNA-seq of healthy human skin samples. Availability and ImplementationThe source code and documentation for ECLIPSER and a Jupyter notebook for generating output tables and figures are available at https://github.com/segrelabgenomics/ECLIPSER. The source code for GWASvar2gene that maps genes to GWAS loci based on e/sQTLs is available at https://github.com/segrelabgenomics/GWASvar2gene. The analysis presented here used data from GTEx (https://gtexportal.org/home/datasets) and Open Targets Genetics (https://genetics-docs.opentargets.org/data-access/graphql-api), but can also be applied to other GWAS variant lists and QTL studies. Data used to reproduce the results of the paper are available in Supplementary data.

bioinformatics↗

bioRxiv2021

A single cell atlas of human and mouse white adipose tissue

White adipose tissue (WAT), once regarded as morphologically and functionally bland, is now recognized to be dynamic, plastic, heterogenous, and involved in a wide array of biological processes including energy homeostasis, glucose and lipid handling, blood pressure control, and host defense1. High fat feeding and other metabolic stressors cause dramatic changes in adipose morphology, physiology, and cellular composition1, and alterations in adiposity are associated with insulin resistance, dyslipidemia, and type 2 diabetes (T2D)2. Here, we provide detailed cellular atlases of human and murine subcutaneous and visceral white fat at single cell resolution across a range of body weight. We identify subpopulations of adipocytes, adipose stem and progenitor cells (ASPCs), vascular, and immune cells and demonstrate commonalities and differences across species and dietary conditions. We link specific cell types to increased risk of metabolic disease, and we provide an initial blueprint for a comprehensive set of interactions between individual cell types in the adipose niche in leanness and obesity. These data comprise an extensive resource for the exploration of genes, traits, and cell types in the function of WAT across species, depots, and nutritional conditions.

physiology↗

bioRxiv2021

Pan-cancer mapping of single T cell profiles reveals a TCF1:CXCR6-CXCL16 regulatory axis essential for effective anti-tumor immunity

Unleashing cytotoxic CD8+ T cells for effective cancer treatment requires understanding T cell states across different tumor microenvironments. Here, we developed an algorithm to recover both shared and tumor type specific programs and used it to analyze a scRNA-seq compendium of 38,852 CD8+ T cells from 141 patients spanning nine different human cancers. We uncovered a pan-cancer T cell dysfunction program that was predictive of clinical responses to immunotherapy and highlighted CXCR6 as a pan-cancer marker of dysfunctional T cells. In mouse models, CXCR6 increased following checkpoint blockade and was repressed by TCF1. Its ligand, CXCL16, was expressed primarily by myeloid cells, and was co-regulated with antigen presentation genes. CXCR6 deletion decreased Tox, CX3CR1, and Bcl2 expression, predisposing dysfunctional PD-1+Tim3+CD8+ T cells to apoptosis, and compromising tumor growth control. Our approach discovered a TCF1:CXCR6-CXCL16 regulatory axis essential for effective anti-tumor immunity, revealing a new perspective on T cell dysfunction and new opportunities for modulating this cell state.

immunology↗

bioRxiv2021

Hematopoiesis at single cell resolution spanning human development and maturation

Hematopoiesis is a process of constitutive regeneration whereby hematopoietic stem and progenitor cells (HSPCs) replenish mature blood cells. During maturation and aging, HSPCs shift their output to support the demands of prenatal development and postnatal maturation both at homeostasis and in response to stress. How HSPC ontogeny changes throughout life is unknown; studies to date have largely focused on specific individual ages, particularly at single cell resolution. Here, we performed single cell RNA-seq of human HSPCs from early prenatal development into mature adulthood. We observed shifts in HSPC transcriptional states and differentiation trajectories over time. We identified age-specific gene expression patterns throughout human maturation and developed methods for identifying, prospectively purifying, and functionally validating age-specific HSC states. Together, our findings define the temporal maturation of human HSPCs and uncover principles applicable to age-biased blood diseases. SummarySingle cell RNA sequencing reveals that the mechanisms of human hematopoietic stem and progenitor cell (HSPC) fate commitment change over a lifetime from gestation to mature adulthood.

developmental biology↗

bioRxiv2021

Targeted in silico characterization of fusion transcripts in tumor and normal tissues via FusionInspector

MotivationGene fusions play a key role as driver oncogenes in tumors, and their reliable discovery and detection are important for cancer research, diagnostics, prognostics and guiding personalized therapy. While discovering gene fusions from genome sequencing can be laborious and costly, the resulting "fusion transcripts" can be recovered from RNA-seq data of tumor and normal samples. However, alleged and putative fusion transcripts can also arise from multiple sources other than chromosomal rearrangements, including cis- or trans-splicing events, experimental artifacts during RNA-seq or computational errors of transcriptome reconstruction methods. Understanding how to discern, interpret, categorize, and verify predicted fusion transcripts is essential for consideration in clinical settings and prioritization for further research. SummaryHere, we present FusionInspector for in silico characterization and interpretation of candidate fusion transcripts from RNA-seq and exploration of their sequence and expression characteristics. We applied FusionInspector to thousands of tumor and normal transcriptomes, and identified statistical and experimental features enriched among biologically impactful fusions. Through clustering and machine learning, we identified large collections of fusions potentially relevant to tumor and normal biological processes. We show that biologically relevant fusions are enriched for relatively high expression of the fusion transcript, imbalanced fusion allelic ratios, and canonical splicing patterns, and are deficient in sequence microhomologies detected between partner genes. We demonstrate that FusionInspector accurately validates fusion transcripts in silico, and helps identify and characterize numerous understudied fusions in tumor and normal tissues samples. FusionInspector is freely available as open source for screening, characterization, and visualization of candidate fusions via RNA-seq, and helps with transparent explanation and interpretation of machine learning predictions and their experimental sources. HighlightsO_LIFusionInspector software for supervised analysis of candidate fusion transcripts C_LIO_LIClustering of recurrent fusion transcripts resolves biologically relevant fusions C_LIO_LIIdentification of distinguishing characteristics of known and novel fusion transcripts in tumor and normal tissues C_LI

bioinformatics↗