Biology subjects

Regev, A.

Publications and source records attributed to Regev, A..

At least 127 records · Page 7Linked to original sources

bioRxiv2020

Metabolic and Epigenomic Regulation of Th17/Treg Balance by the Polyamine Pathway

Cellular metabolism can orchestrate immune cell function. We previously demonstrated that lipid biosynthesis represents one such gatekeeper to Th17 cell functional state. Utilizing Compass, a transcriptome-based algorithm for prediction of metabolic flux, we constructed a comprehensive metabolic circuitry for Th17 cell function and identified the polyamine pathway as a candidate metabolic node, the flux of which regulates the inflammatory function of T cells. Testing this prediction, we found that expression and activities of enzymes of the polyamine pathway were enhanced in pathogenic Th17 cells and suppressed in regulatory T cells. Perturbation of the polyamine pathway in Th17 cells suppressed canonical Th17 cell cytokines and promoted the expression of Foxp3, accompanied by dramatic shift in transcriptome and epigenome, transitioning Th17 cells into a Treg-like state. Genetic and chemical perturbation of the polyamine pathway resulted in attenuation of tissue inflammation in an autoimmune disease model of central nervous system, with changes in T cell effector phenotype.

immunology↗

bioRxiv2020

Single-nucleus RNA-Seq reveals a new type of brown adipocyte regulating thermogenesis

Adipose tissue usually is classified as either white, brown or beige/brite, based on whether it functions as an energy storage or thermogenic organ(Cannon and Nedergaard, 2004; Rosen and Spiegelman, 2014). It serves as an important regulator of systemic metabolism, exemplified by the fact that dysfunctional adipose tissue in obesity leads to a host of secondary metabolic complications such as diabetes, cardiovascular diseases and cancer(Hajer et al., 2008; Lauby-Secretan et al., 2016). In addition, adipose tissue is an important endocrine organ, which regulates the function of other metabolic tissues through paracrine and endocrine signals(Scheele and Wolfrum, 2019; Scherer, 2006). Work in recent years has demonstrated that tissue heterogeneity is an important factor regulating the functionality of various organs(Cao et al., 2017; Ginhoux et al., 2016; Park et al., 2018). Here we used single nucleus analysis in mice and men to deconvolute adipocyte heterogeneity. We are able to identify a novel subpopulation of adipocytes whose abundance is low in mice (2-8%) and which is increased under higher ambient temperatures. Interestingly, this population is abundant in humans who live close to thermoneutrality. We demonstrate that this novel adipocyte subtype functions as a paracrine cell regulating the activity of brown adipocytes through acetate-mediated regulation of thermogenesis. These findings could explain, why human brown adipose tissue is substantially less active than mouse tissue and targeting this pathway in humans might be utilized to restore thermogenic activity of this tissue.

cell biology↗

bioRxiv2019

Multiplexed single-cell profiling of post-perturbation transcriptional responses to define cancer vulnerabilities and therapeutic mechanism of action

Assays to study cancer cell responses to pharmacologic or genetic perturbations are typically restricted to using simple phenotypic readouts such as proliferation rate or the expression of a marker gene. Information-rich assays, such as gene-expression profiling, are generally not amenable to efficient profiling of a given perturbation across multiple cellular contexts. Here, we developed MIX-Seq, a method for multiplexed transcriptional profiling of post-perturbation responses across a mixture of samples with single-cell resolution, using SNP-based computational demultiplexing of single-cell RNA-sequencing data. We show that MIX-Seq can be used to profile responses to chemical or genetic perturbations across pools of 100 or more cancer cell lines, and combine it with Cell Hashing to further multiplex additional experimental conditions, such as multiple post-treatment time points or drug doses. Analyzing the high-content readout of scRNA-seq reveals both shared and context-specific transcriptional response components that can identify drug mechanism of action and can be used to predict long-term cell viability from short-term transcriptional responses to treatment.

cancer biology↗

bioRxiv2019

Shuffle-Seq: En masse combinatorial encoding for n-way genetic interaction screens

Genetic interactions, defined as the non-additive phenotypic impact of combinations of genes, are a hallmark of the mapping from genotype to phenotype. However, genetic interactions remain challenging to systematically test given the massive number of possible combinations. In particular, while large-scale screening efforts in yeast have quantified pairwise interactions that affect cell viability, or synthetic lethality, between all pairs of genes as well as for a limited number of three-way interactions, it has previously been intractable to perform the large screens needed to comprehensively assess interactions in a mammalian genome. Here, we develop Shuffle-Seq, a scalable method to assay genetic interactions. Shuffle-Seq leverages the co-inheritance of genetically encoded barcodes in dividing cells and can scale in proportion to sequencing throughput. We demonstrate the technical validity of Shuffle-Seq and apply it to screening for mechanisms underlying drug resistance in a melanoma model. Shuffle-Seq should allow screens of hundreds of millions of combinatorial perturbations and facilitate the understanding of genetic dependencies and drug sensitivities.

genomics↗

bioRxiv2019

Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces

Single-cell RNA-Seq (scRNA-seq) has become an invaluable tool for studying biological systems in health and diseases. While dimensionality reduction is a crucial step in interpreting the relation between cells based on scRNA-seq, current methods often are hampered by "crowding" of cells in the center of the latent space, biased by batch effects, or inadequately capture developmental relationships. Here, we introduced scPhere, a scalable deep generative model to embed cells into low-dimensional hyperspherical or hyperbolic spaces, as a more accurate representation of the data. ScPhere resolves cell crowding, corrects multiple, complex batch factors, facilitates interactive visualization of large datasets, and gracefully uncovers pseudotemporal trajectories. We demonstrate scPhere on six large datasets in complex tissue from human patients or animal development, demonstrating how it controls for both technical and biological factors and highlights complex cellular relations and biological insights.

bioinformatics↗

bioRxiv2019

Cumulus: a cloud-based data analysis framework for large-scale single-cell and single-nucleus RNA-seq

Massively parallel single-cell and single-nucleus RNA-seq (sc/snRNA-seq) have opened the way to systematic tissue atlases in health and disease, but as the scale of data generation is growing, so does the need for computational pipelines for scaled analysis. Here, we developed Cumulus, a cloud-based framework for analyzing large scale sc/snRNA-seq datasets. Cumulus combines the power of cloud computing with improvements in algorithm implementations to achieve high scalability, low cost, user-friendliness, and integrated support for a comprehensive set of features. We benchmark Cumulus on the Human Cell Atlas Census of Immune Cells dataset of bone marrow cells and show that it substantially improves efficiency over conventional frameworks, while maintaining or improving the quality of results, enabling large-scale studies.

bioinformatics↗

bioRxiv2019

MAUDE: Inferring expression changes in sorting-based CRISPR screens

Pooled CRISPR screens allow high-throughput interrogation of genetic elements that alter expression of a reporter gene readout. New computational methods are needed to model these data. We created MAUDE (Mean Alterations Using Discrete Expression) for quantifying the impact of guide RNAs on a target genes expression in a pooled, sorting-based expression screen. MAUDE quantifies guide-level effects by modeling the distribution of cells across sorting expression bins. It then combines guides to estimate the statistical significance and effect size of targeted genetic elements. We show that MAUDE significantly improves over previous approaches and provide experimental design guidelines to best leverage MAUDE.

bioinformatics↗

bioRxiv2019

Pan-cancer single cell RNA-seq uncovers recurring programs of cellular heterogeneity

Cultured cell lines are the workhorse of cancer research, but it is unclear to what extent they recapitulate the cellular heterogeneity observed among malignant cells in tumors, given the absence of a native tumor microenvironment. Here, we used multiplexed single cell RNA-seq to profile ~200 cancer cell lines. We uncovered expression programs that are recurrently heterogeneous within many cancer cell lines and are largely independent of observed genetic diversity. These programs of heterogeneity are associated with diverse biological processes, including cell cycle, senescence, stress and interferon responses, epithelial-to-mesenchymal transition, and protein maturation and degradation. Notably, some of these recurrent programs recapitulate those seen in human tumors, suggesting a prominent role of intrinsic plasticity in generating intra-tumoral heterogeneity. Moreover, the data allowed us to prioritize specific cell lines as model systems of cellular plasticity. We used two such models to demonstrate the dynamics, regulation and drug sensitivities associated with a cancer senescence program also observed in human tumors. Our work describes the landscape of cellular heterogeneity in diverse cancer cell lines, and identifies recurrent patterns of expression heterogeneity that are shared between tumors and specific cell lines and can thus be further explored in follow up studies.

cancer biology↗

bioRxiv2019

An endogenous glucocorticoid-cytokine signaling circuit promotes CD8+ T cell dysfunction in the tumor microenvironment

Identifying signals in the tumor microenvironment (TME) that promote CD8+ T cell dysfunction can inform improved therapeutic approaches for cancer. Here, we identify that Nr3c1, the gene encoding the glucocorticoid receptor (GR), is highly expressed in dysfunctional CD8+ tumor-infiltrating lymphocytes (TILs). The GR transactivates expression of multiple checkpoint receptors and loss of GR in CD8+ T cells limits dysfunctional phenotype in CD8+ TILs resulting in improved tumor growth control. We show that glucocorticoids can be produced in the TME and that they co-operate with the immunosuppressive cytokine IL-27 to promote the dysfunction gene program in CD8+ T cells. The presence of the glucocorticoid + IL27 signature in CD8+ TILs correlates with failure to respond to checkpoint blockade in melanoma patients, highlighting the relevance of this immunoregulatory glucocorticoid-cytokine circuit in tumor tissue.

immunology↗

bioRxiv2019

Genetic predisposition to myeloproliferative neoplasms implicates hematopoietic stem cell biology

Myeloproliferative neoplasms (MPNs) are blood cancers characterized by excessive production of mature myeloid cells that result from the acquisition of somatic driver mutations in hematopoietic stem cells (HSCs)1. While substantial progress has been made to define the causal somatic mutation profile for MPNs2, epidemiologic studies indicate a significant heritable component for the disease that is among the highest known for all cancers3. However, only a limited set of genetic risk loci have been identified, and the underlying biological mechanisms leading to MPN acquisition remain unexplained. Here, to define the inherited risk profile, we conducted the largest genome-wide association study of MPNs to date (978,913 individuals with 3,224 cases) and identified 14 genome-wide significant loci, as well as a polygenic signature that increases the odds for disease acquisition by nearly 3-fold between the top and median deciles. Interestingly, we find a shared genetic architecture between MPN risk and several hematopoietic traits spanning distinct lineages, as well as an association between increased MPN risk and longer leukocyte telomere length, collectively implicating HSC function and self-renewal. Strikingly, we find a significant enrichment for risk variants mapping to accessible chromatin in HSCs compared with other hematopoietic populations. Finally, gene mapping identifies modulators of HSC biology and targeted variant-to-function analyses suggest likely roles for CHEK2 and GFI1B in altering HSC function to confer disease risk. Overall, we demonstrate the power of human genetic studies to illuminate a previously unappreciated mechanism for MPN risk through modulation of HSC function.

genetics↗

bioRxiv2019

In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with Autism risk genes

The thousands of disease risk genes and loci identified through human genetic studies far outstrip our current capacity to systematically study their functions. New experimental approaches are needed for functional investigations of large panels of genes in a biologically relevant context. Here, we developed a scalable genetic screen approach, in vivo Perturb-Seq, and applied this method to the functional evaluation of 35 autism spectrum disorder (ASD) de novo loss-of-function risk genes. Using CRISPR-Cas9, we introduced frameshift mutations in these risk genes in pools, within the developing brain in utero, and then performed single-cell RNA-Seq in the postnatal brain. We identified cell type-specific gene signatures from both neuronal and glial cell classes that are affected by genetic perturbations, and pointed at elements of both convergent and divergent cellular effects across this cohort of ASD risk genes. In vivo Perturb-Seq pioneers a systems genetics approach to investigate at scale how diverse mutations affect cell types and states in the biologically relevant context of the developing organism.

genomics↗

bioRxiv2019

Aging-associated alterations in the mammary gland revealed by single-cell RNA sequencing

Aging of the mammary gland is closely associated with increased susceptibility to diseases such as cancer, but there have been limited systematic studies of aging-induced alterations within this organ. We performed high-throughput single-cell RNA-sequencing (scRNA-seq) profiling of mammary tissues from young and old nulliparous mice, including both epithelial and stromal cell types. Our analysis identified altered proportions and distinct gene expression patterns in numerous cell populations as a consequence of the aging process, independent of parity and lactation. In addition, we detected a subset of luminal cells that express both hormone-sensing and alveolar markers and decrease in relative abundance with age. These data provide a high-resolution landscape of aging mammary tissues, with potential implications for normal tissue functions and cancer predisposition.

cell biology↗

bioRxiv2019

The enteric nervous system of the human and mouse colon at a single-cell resolution

As the largest branch of the autonomic nervous system, the enteric nervous system (ENS) controls the entire gastrointestinal tract, but remains incompletely characterized. Here, we develop RAISIN RNA-seq, which enables the capture of intact single nuclei along with ribosome-bound mRNA, and use it to profile the adult mouse and human colon to generate a reference map of the ENS at a single-cell resolution. This map reveals an extraordinary diversity of neuron subsets across intestinal locations, ages, and circadian phases, with conserved transcriptional programs that are shared between human and mouse. These data suggest possible revisions to the current model of peristalsis and molecular mechanisms that may allow enteric neurons to orchestrate tissue homeostasis, including immune regulation and stem cell maintenance. Human enteric neurons specifically express risk genes for neuropathic, inflammatory, and extra-intestinal diseases with concomitant gut dysmotility. Our study therefore provides a roadmap to understanding the ENS in health and disease.

genomics↗

bioRxiv2019

Compressed sensing for imaging transcriptomics

Tissue and organ function rely on the organization of cells and molecules in specific spatial structures. In order to understand these structures and how they relate to tissue function in health and disease, we would ideally be able to rapidly profile gene expression over large tissue volumes. To this end, in recent years multiple molecular assays have been developed that can image from a dozen to ~100 individual proteins1-3 or RNAs4-10 in a sample at single-cell resolution, with barcodes to allow multiplexing across genes. These approaches have serious limitations with respect to (i) the number of genes that can be studied; and (ii) imaging time, due to the need for high-resolution to resolve individual signals. Here, we show that both challenges can be overcome by introducing an approach that leverages the biological fact that gene expression is often structured across both cells and tissue organization. We develop Composite In Situ Imaging (CISI), that combines this biological insight with algorithmic advances in compressed sensing to achieve greater efficiency. We demonstrate that CISI accurately recovers the spatial abundance of each of 37 individual genes from 11 composite measurements in 12 bisected mouse brain coronal sections covering 180mm2 and 476,276 cells without the need for spot-level resolution. CISI achieves the current scale of multiplexing with two orders of magnitude greater efficiency, and can be leveraged in combination with existing methods to multiplex far beyond current scales.

molecular biology↗

bioRxiv2019

Opposing immune and genetic forces shape oncogenic programs in synovial sarcoma

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.

cancer biology↗

bioRxiv2019

Single-cell profiles of retinal neurons differing in resilience to injury reveal neuroprotective genes

Neuronal types in the central nervous system differ dramatically in their resilience to injury or insults. Here we studied the selective resilience of mouse retinal ganglion cells (RGCs) following optic nerve crush (ONC), which severs their axons and leads to death of ~80% of RGCs within 2 weeks. To identify expression programs associated with differential resilience, we first used single-cell RNA-seq (scRNA-seq) to generate a comprehensive molecular atlas of 46 RGC types in adult retina. We then tracked their survival after ONC, characterized transcriptomic, physiological, and morphological changes that preceded degeneration, and identified genes selectively expressed by each type. Finally, using loss- and gain-of-function assays in vivo, we showed that manipulating some of these genes improved neuronal survival and axon regeneration following ONC. This study provides a systematic framework for parsing type-specific responses to injury, and demonstrates that differential gene expression can be used to reveal molecular targets for intervention.

neuroscience↗

bioRxiv2019

Combinatorial prediction of gene-marker panels from single-cell transcriptomic data.

Single-cell transcriptomic studies are identifying novel cell populations with exciting functional roles in various in vivo contexts, but identification of succinct gene-marker panels for such populations remains a challenge. In this work we introduce COMET, a computational framework for the identification of candidate marker panels consisting of one or more genes for cell populations of interest identified with single-cell RNA-seq data. We show that COMET outperforms other methods for the identification of single-gene panels, and enables, for the first time, prediction of multi-gene marker panels ranked by relevance. Staining by flow-cytometry assay confirmed the accuracy of COMETs predictions in identifying marker-panels for cellular subtypes, at both the single- and multi-gene levels, validating COMETs applicability and accuracy in predicting favorable marker-panels from transcriptomic input. COMET is a general non-parametric statistical framework and can be used as-is on various high-throughput datasets in addition to single-cell RNA-sequencing data. COMET is available for use via a web interface (http://www.cometsc.com) or a standalone software package (https://github.com/MSingerlab/COMETSC).

bioinformatics↗

bioRxiv2019

Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects

Single-cell RNA sequencing (scRNA-seq) is the leading technique for charting the molecular properties of individual cells. The latest methods are scalable to thousands of cells, enabling in-depth characterization of sample composition without prior knowledge. However, there are important differences between scRNA-seq techniques, and it remains unclear which are the most suitable protocols for drawing cell atlases of tissues, organs and organisms. We have generated benchmark datasets to systematically evaluate techniques in terms of their power to comprehensively describe cell types and states. We performed a multi-center study comparing 13 commonly used single-cell and single-nucleus RNA-seq protocols using a highly heterogeneous reference sample resource. Comparative and integrative analysis at cell type and state level revealed marked differences in protocol performance, highlighting a series of key features for cell atlas projects. These should be considered when defining guidelines and standards for international consortia, such as the Human Cell Atlas project.

genomics↗