Biology subjects

Yosef, N.

Publications and source records attributed to Yosef, N..

At least 55 records · Page 3Linked to original sources

bioRxiv2021

Single-cell multi-omic analysis of thymocyte development reveals NFAT as adriver of CD4/CD8 lineage commitment

The development of CD4 and CD8 T cells in the thymus is critical to adaptive immunity and is widely studied as a model of lineage commitment. Recognition of self-MHCI/II by the T cell antigen receptor (TCR) determines the lineage choice, but how distinct TCR signals drive transcriptional programs of lineage commitment remains largely unknown. We applied CITE-seq to measure RNA and surface proteins in thymocytes from wild-type and lineage-restricted mice to generate a comprehensive timeline of cell state for each lineage. These analyses revealed a sequential process whereby all thymocytes initiate CD4 lineage differentiation during an initial wave of TCR signaling, followed by a second TCR signaling wave that coincides with CD8 lineage specification. CITE-seq and pharmaceutical inhibition experiments implicate a TCR/calcineurin/NFAT/GATA3 axis in driving the CD4 fate. Overall, our data suggest that multiple redundant mechanisms contribute to the accuracy and efficiency of the lineage choice.

bioinformatics↗

bioRxiv2021

Critical genetic program for Drosophila imaginal disc regeneration revealed by single-cell analysis

Whether regeneration is primarily accomplished by re-activating gene regulatory networks used previously during development or by activating novel regeneration-specific transcriptional programs remains a longstanding question. Currently, most genes implicated in regeneration also function during development. Using single-cell transcriptomics in regenerating Drosophila wing discs, we identified two regeneration-specific cell populations within the blastema. They are each composed of cells that upregulate multiple genes encoding secreted proteins that promote regeneration. In this regenerative secretory zone, the transcription factor Ets21C controls the expression of multiple regenerationpromoting genes. While eliminating Ets21C function has no discernible effect on development, it severely compromises regeneration. This Ets21C-dependent gene regulatory network is also activated in blastema-like cells in tumorous discs, suggesting that pro-regenerative mechanisms can be co-opted by tumors to promote aberrant growth.

developmental biology↗

bioRxiv2021

Epitome: Predicting epigenetic events in novel cell types with multi-cell deep ensemble learning

The accumulation of large epigenomics data consortiums provides us with the opportunity to extrapolate existing knowledge to new cell types and conditions. We propose Epitome, a deep neural network that learns similarities of chromatin accessibility between well characterized reference cell types and a query cellular context, and copies over signal of transcription factor binding and modification of histones from reference cell types when chromatin profiles are similar to the query. Epitome achieves state-of-the-art accuracy when predicting transcription factor binding sites on novel cellular contexts, and can further improve predictions as more epigenetic signals are collected from both reference cell types and the query cellular context of interest.

bioinformatics↗

bioRxiv2021

Reconstructing unobserved cellular states from paired single-cell lineage tracing and transcriptomics data

AO_SCPLOWBSTRACTC_SCPLOWNovel experimental assays now simultaneously measure lineage relationships and transcriptomic states from single cells, thanks to CRISPR/Cas9-based genome engineering. These multimodal measurements allow researchers not only to build comprehensive phylogenetic models relating all cells but also infer transcriptomic determinants of consequential subclonal behavior. The gene expression data, however, is limited to cells that are currently present ("leaves" of the phylogeny). As a consequence, researchers cannot form hypotheses about unobserved, or "ancestral", states that gave rise to the observed population. To address this, we introduce TreeVAE: a probabilistic framework for estimating ancestral transcriptional states. TreeVAE uses a variational autoencoder (VAE) to model the observed transcriptomic data while accounting for the phylogenetic relationships between cells. Using simulations, we demonstrate that TreeVAE outperforms benchmarks in reconstructing ancestral states on several metrics. TreeVAE also provides a measure of uncertainty, which we demonstrate to correlate well with its prediction accuracy. This estimate therefore potentially provides a data-driven way to estimate how far back in the ancestor chain predictions could be made. Finally, using real data from lung cancer metastasis, we show that accounting for phylogenetic relationship between cells improves goodness of fit. Together, TreeVAE provides a principled framework for reconstructing unobserved cellular states from single cell lineage tracing data.

bioinformatics↗

bioRxiv2021

Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation

The function of mammalian cells is largely influenced by their tissue microenvironment. Advances in spatial transcriptomics open the way for studying these important determinants of cellular function by enabling a transcriptome-wide evaluation of gene expression in situ. A critical limitation of the current technologies, however, is that their resolution is limited to niches (spots) of sizes well beyond that of a single cell, thus providing measurements for cell aggregates which may mask critical interactions between neighboring cells of different types. While joint analysis with single-cell RNA-sequencing (scRNA-seq) can be leveraged to alleviate this problem, current analyses are limited to a discrete view of cell type proportion inside every spot. This limitation becomes critical in the common case where, even within a cell type, there is a continuum of cell states that cannot be clearly demarcated but reflects important differences in the way cells function and interact with their surroundings. To address this, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI), a probabilistic method for multi-resolution analysis for spatial transcriptomics that explicitly models continuous variation within cell types. Using simulations, we demonstrate that DestVI is capable of providing higher resolution compared to the existing methods and that it can estimate gene expression by every cell type inside every spot. We then introduce an automated pipeline that uses DestVI for analysis of single tissue slices and comparison between tissues. We apply this pipeline to study the immune crosstalk within lymph nodes to infection and explore the spatial organization of a mouse tumor model. In both cases, we demonstrate that DestVI can provide a high resolution and accurate spatial characterization of the cellular organization of these tissues, and that it is capable of identifying important cell-type-specific changes in gene expression - between different tissue regions or between conditions. DestVI is available as an open-source software package in the scvi-tools codebase (https://scvi-tools.org).

bioinformatics↗

bioRxiv2021

PeakVI: A Deep Generative Model for Single Cell Chromatin Accessibility Analysis

AO_SCPLOWBSTRACTC_SCPLOWSingle-cell ATAC sequencing (scATAC-seq) is a powerful and increasingly popular technique to explore the regulatory landscape of heterogeneous cellular populations. However, the high noise levels, degree of sparsity, and scale of the generated data make its analysis challenging. Here we present PeakVI, a probabilistic framework that leverages deep neural networks to analyze scATAC-seq data. PeakVI fits an informative latent space that preserves biological heterogeneity while correcting batch effects and accounting for technical effects such as library size and region-specific biases. Additionally, PeakVI provides a technique for identifying differential accessibility at a single region resolution, which can be used for cell-type annotation as well as identification of key cis-regulatory elements. We use public datasets to demonstrate that PeakVI is scalable, stable, robust to low-quality data, and outperforms current analysis methods on a range of critical analysis tasks. PeakVI is publicly available and implemented in the scvi-tools framework: https://docs.scvi-tools.org/.

bioinformatics↗

bioRxiv2021

scvi-tools: a library for deep probabilistic analysis of single-cell omics data

AO_SCPLOWBSTRACTC_SCPLOWProbabilistic models have provided the underpinnings for state-of-the-art performance in many single-cell omics data analysis tasks, including dimensionality reduction, clustering, differential expression, annotation, removal of unwanted variation, and integration across modalities. Many of the models being deployed are amenable to scalable stochastic inference techniques, and accordingly they are able to process single-cell datasets of realistic and growing sizes. However, the community-wide adoption of probabilistic approaches is hindered by a fractured software ecosystem resulting in an array of packages with distinct, and often complex interfaces. To address this issue, we developed scvi-tools (https://scvi-tools.org), a Python package that implements a variety of leading probabilistic methods. These methods, which cover many fundamental analysis tasks, are accessible through a standardized, easy-to-use interface with direct links to Scanpy, Seurat, and Bioconductor workflows. By standardizing the implementations, we were able to develop and reuse novel functionalities across different models, such as support for complex study designs through nonlinear removal of unwanted variation due to multiple covariates and reference-query integration via scArches. The extensible software building blocks that underlie scvi-tools also enable a developer environment in which new probabilistic models for single cell omics can be efficiently developed, benchmarked, and deployed. We demonstrate this through a code-efficient reimplementation of Stereoscope for deconvolution of spatial transcriptomics profiles. By catering to both the end user and developer audiences, we expect scvi-tools to become an essential software dependency and serve to formulate a community standard for probabilistic modeling of single cell omics.

bioinformatics↗

bioRxiv2021

Massively parallel reporter perturbation assay uncovers temporal regulatory architecture during neural differentiation

Gene regulatory elements play a key role in orchestrating gene expression during cellular differentiation, but what determines their function over time remains largely unknown. Here, we performed perturbation-based massively parallel reporter assays at seven early time points of neural differentiation to systematically characterize how regulatory elements and motifs within them guide cellular differentiation. By perturbing over 2,000 putative DNA binding motifs in active regulatory regions, we delineated four categories of functional elements, and observed that activity direction is mostly determined by the sequence itself, while the magnitude of effect depends on the cellular environment. We also find that fine-tuning transcription rates is often achieved by a combined activity of adjacent activating and repressing elements. Our work provides a blueprint for the sequence components needed to induce different transcriptional patterns in general and specifically during neural differentiation.

genomics↗

bioRxiv2021

Induction of a colitogenic phenotype in Th1 cells depends on IL-23R signaling

The cytokine receptor IL-23R plays a fundamental role in inflammation and autoimmunity. However, several observations have been difficult to reconcile under the assumption that only Th17 cells critically depend on IL-23 to acquire a pathogenic phenotype. Here, we report that Th1 cells differentiated in vitro with IL-12 + IL-21 show similar levels of IL-23R expression as in pathogenic Th17 cells. We demonstrate that IL-23R is required for Th1 cells to acquire a highly colitogenic phenotype. scRNAseq analysis of intestinal T cells enabled us to identify novel regulators induced by IL-23R-signaling in Th1 cells which differed from those expressed in Th17 cells. The perturbation of one of these regulators (CD160) in Th1 cells inhibited induction of colitis. In this process, we were able to uncouple IL-23R as a purely Th17 cell-specific factor and implicate IL-23R signaling as a pathogenic driver of Th1 cell-mediated tissue inflammation and disease.

immunology↗

bioRxiv2021

T cell self-reactivity during thymic development dictates the timing of positive selection

Functional tuning of T cells based on their degree of self-reactivity is established during positive selection in the thymus, although how positive selection differs for thymocytes with relatively low versus high self-reactivity is unclear. In addition, preselection thymocytes are highly sensitive to low-affinity ligands, but the mechanism underlying their enhanced TCR sensitivity is not fully understood. Here we show that murine thymocytes with low self-reactivity experience briefer TCR signals and complete positive selection more slowly than those with high self-reactivity. Additionally, we provide evidence that cells with low self-reactivity retain a preselection gene expression signature as they mature, including genes previously implicated in modulating TCR sensitivity and a novel group of ion channel genes. Our results imply that thymocytes with low self-reactivity down-regulate TCR sensitivity more slowly during positive selection, and suggest that modulation of membrane ion channel function may play a role in regulating TCR tuning throughout development. Impact StatementDeveloping T cells whose TCRs have relatively low reactivity experience very brief TCR signaling events, delayed positive selection, and do not fully down regulate their TCR sensitivity as they mature.

developmental biology↗

bioRxiv2021

Batf-mediated Epigenetic Control of Effector CD8+ T Cell Differentiation

The response of cytotoxic T cells to their cognate antigen involves rapid and broad changes in gene expression that are coupled with extensive chromatin remodeling. Here, we study the mechanisms by which the basic leucine zipper ATF-like transcription factor Batf helps regulate this process. Through genome-scale profiling, we observe critical roles for Batf in inducing transcriptional changes in stimulated naive cells, while affecting the chromatin at several levels, namely binding of key transcription factors, accessibility, and long range contacts. We identify a critical network of transcription factors that cooperate with Batf, including its binding partner Irf4, as well as Runx3 and T-bet, and demonstrate its synergistic activity in initiating aspects of the effector T cells transcriptional and epigenetic program in ectopically-induced fibroblasts. Our results provide a comprehensive resource for studying the epigenomic and transcriptomic landscape of effector differentiation of cytotoxic T cells.

immunology↗

bioRxiv2020

Mucosal vaccination with cyclic-di-nucleotide adjuvants induces effective T cell homing and IL-17 dependent protection against M. tuberculosis infection

The only licensed vaccine for tuberculosis, Mycobacterium bovis Bacille Calmette-Guerin (BCG), is not reliably effective against adult pulmonary tuberculosis. A major hurdle to tuberculosis vaccine development is incomplete understanding of successful immunity against the causative agent Mycobacterium tuberculosis. Recently, we demonstrated that a protein subunit vaccine adjuvanted with STING-activating cyclic-di-nucleotides (CDNs) robustly protects against tuberculosis infection in mice. Here we show mucosal vaccination with this vaccine induces production of T cells that home to lung parenchyma and penetrate lesions in the lung. Protection is partially dependent on IL-17, type I interferon (IFN), and IFN-{gamma}, while the transcription factor STAT-6 is dispensable. Single cell transcriptomics reveals mucosal vaccination with a CDN vaccine increases transcriptional heterogeneity in CD4 cells, including a significant population of non-classical IFN-{gamma} and IL-17 co-expressing Th1-Th17 cells, as well as markers of memory and activation. Th1-Th17 cells in vaccinated mice are enriched for expression of the T cell functional markers Tnfsf8 and Il1r1 relative to more conventional Th1 cells. These data provide critical insight into the immune mediators and diversity of T cell responses that can contribute to vaccine efficacy against M. tuberculosis infection.

immunology↗

bioRxiv2020

Single-cell transcriptomics of the Drosophila wing disc reveals instructive epithelium-to-myoblast interactions

In both vertebrates and invertebrates, generating a functional appendage requires interactions between ectoderm-derived epithelia and mesoderm-derived cells. To investigate such interactions, we used single-cell transcriptomics to generate a cell atlas of the Drosophila wing disc at two time points during development. Using these data, we investigate gene expression using a multi-layered model of the wing disc and catalogued ligand-receptor pairs that could mediate signaling between epithelial cells and adult muscle precursors (AMPs). We found that localized expression of the FGF ligands, Thisbe and Pyramus, in the disc epithelium regulates the number and location of the AMPs. In addition, Hedgehog ligand from the epithelium activates a specific transcriptional program within adjacent AMP cells, which is critical for proper formation of a subset of the direct flight muscles. More generally, our annotated atlas provides a global view of potential cell-cell interactions between subpopulations of epithelial and myogenic cells.

bioinformatics↗

bioRxiv2020

Alterations of multiple alveolar macrophage states in chronic obstructive pulmonary disease

Despite the epidemics of chronic obstructive pulmonary disease (COPD), the cellular and molecular mechanisms of this disease are far from being understood. Here, we characterize and classify the cellular composition within the alveolar space and peripheral blood of COPD patients and control donors using a clinically applicable single-cell RNA-seq technology corroborated by advanced computational approaches for: machine learning-based cell-type classification, identification of differentially expressed genes, prediction of metabolic changes, and modeling of cellular trajectories within a patient cohort. These high-resolution approaches revealed: massive transcriptional plasticity of macrophages in the alveolar space with increased levels of invading and proliferating cells, loss of MHC expression, reduced cellular motility, altered lipid metabolism, and a metabolic shift reminiscent of mitochondrial dysfunction in COPD patients. Collectively, single-cell omics of multi-tissue samples was used to build the first cellular and molecular framework for COPD pathophysiology as a prerequisite to develop molecular biomarkers and causal therapies against this deadly disease.

systems biology↗

bioRxiv2020

Joint probabilistic modeling of paired transcriptome and proteome measurements in single cells

AO_SCPLOWBSTRACTC_SCPLOWThe paired measurement of RNA and surface protein abundance in single cells with CITE-seq is a promising approach to connect transcriptional variation with cell phenotypes and functions. However, each data modality exhibits unique technical biases, making it challenging to conduct a joint analysis and combine these two views into a unified representation of cell state. Here we present Total Variational Inference (totalVI), a framework for the joint probabilistic analysis of paired RNA and protein data from single cells. totalVI probabilistically represents the data as a composite of biological and technical factors such as limited sensitivity of the RNA data, background in the protein data, and batch effects. To evaluate totalVI, we performed CITE-seq on immune cells from murine spleen and lymph nodes with biological replicates and with different antibody panels measuring over 100 surface proteins. With this dataset, we demonstrate that totalVI provides a cohesive solution for common analysis tasks like the integration of datasets with matched or unmatched protein panels, dimensionality reduction, clustering, evaluation of correlations between molecules, and differential expression testing. totalVI enables scalable, end-to-end analysis of paired RNA and protein data from single cells and is available as open-source software.

bioinformatics↗

bioRxiv2020

Continuous lineage recording reveals rapid, multidirectional metastasis in a lung cancer xenograft model in mouse

Cancer progression is characterized by rare, transient events which are nonetheless highly consequential to disease etiology and mortality. Detailed cell phylogenies can recount the history and chronology of these critical events - including metastatic seeding. Here, we applied our Cas9-based lineage tracer to study the subclonal dynamics of metastasis in a lung cancer xenograft mouse model, revealing the underlying rates, routes, and drivers of metastasis. We report deeply resolved phylogenies for tens of thousands of metastatically disseminated cancer cells. We observe surprisingly diverse metastatic phenotypes, ranging from metastasis-incompetent to aggressive populations. These phenotypic distinctions result from pre-existing, heritable, and characteristic differences in gene expression, and we demonstrate that these differentially expressed genes can drive invasiveness. Furthermore, metastases transit via diverse, multidirectional tissue routes and seeding topologies. Our work demonstrates the power of tracing cancer progression at unprecedented resolution and scale. One Sentence SummarySingle-cell lineage tracing and RNA-seq capture diverse metastatic behaviors and drivers in lung cancer xenografts in mice.

cancer biology↗

bioRxiv2020

Oleic acid Induces Tissue Resident FoxP3 Regulatory T cell Lineage Stability and Suppressive Functions

FoxP3 positive regulatory T cells (Tregs) rely on fatty acid {beta}-oxidation (FAO)-driven OXPHOS for differentiation and function. Recent data have demonstrated a role for Tregs in the maintenance of tissue homeostasis with tissue-resident Tregs possessing tissue-specific transcriptomes. However, specific signals that establish these tissue-resident Tregs programs are largely unknown. As Tregs metabolically rely on FAO, and considering the lipid-rich environments of tissues, we hypothesized that environmental lipids drive Treg homeostasis. Using human adipose tissue as a model for tissue residency, we identify oleic acid as the most prevalent free fatty acid in human adipose tissue. Mechanistically, oleic acid amplifies Treg FAO-driven OXPHOS metabolism, creating a positive feedback mechanism that induces the expression of Foxp3 and enhances phosphorylation of STAT5, which acts to stabilize the Treg lineage and increase suppressive function. Comparing the transcriptomic program induced by oleic acid to that of the pro-inflammatory arachidonic acid, we find that Tregs sorted from peripheral blood and adipose of healthy donors transcriptomically resemble the oleic acid in vitro treated Tregs, whereas Tregs obtained from the adipose tissue of relapsing-remitting MS patients more closely resemble an arachidonic acid profile. Finally, we find that oleic acid concentrations are reduced in the fat tissue of MS patients, and exposure of dysfunctional MS Tregs to oleic acid restores defects in their suppressive function. These data demonstrate the importance of fatty acids in regulating tissue inflammatory signals.

immunology↗

bioRxiv2020

Identifying Informative Gene Modules Across Modalities of Single Cell Genomics

Two fundamental aims that emerge when analyzing single-cell RNA-seq data are that of identifying which genes vary in an informative manner and determining how these genes organize into modules. Here we propose a general approach to these problems that operates directly on a given metric of cell-cell similarity, allowing for its integration with any method (linear or non linear) for identifying the primary axes of transcriptional variation between cells. Additionally, we show that when using multimodal data, our procedure can be used to identify genes whose expression reflects alternative notions of similarity between cells, such as physical proximity in a tissue or clonal relatedness in a cell lineage tree. In this manner, we demonstrate that while our method, called Hotspot, is capable of identifying genes that reflect nuanced transcriptional variability between T helper cells, it can also identify spatially-dependent patterns of gene expression in the cerebellum as well as developmentally-heritable expression signatures during embryogenesis.

bioinformatics↗