Biology subjects

Yosef, N.

Publications and source records attributed to Yosef, N..

At least 73 records · Page 4Linked to original sources

bioRxiv2020

Polyamine Metabolism Regulates the T Cell Epigenome Through Hypusination

We report here a central role for polyamines in T cell differentiation and function. Deficiency in ornithine decarboxylase (ODC), a critical enzyme for polyamine synthesis, resulted in a profound failure of CD4+ T cells to adopt correct subset specification, underscored by ectopic expression of multiple cytokines and lineage-defining transcription factors across TH1, TH2, TH17, and Treg polarizing conditions, and enhanced colitogenic potential. T cells deficient in deoxyhypusine synthase (DHPS) or deoxyhypusine hydroxylase (DOHH), which sequentially utilize polyamines to generate hypusine, phenocopied Odc-deficient T cells, and mice in which T cells lacked Dhps or Dohh developed colitis. Polyamine-hypusine pathway enzyme deficiency caused widespread chromatin and transcriptional dysregulation accompanied by alterations in histone methylation, histone acetylation, and TCA cycle metabolites. Epigenetic modulation by 2-hydroxyglutarate, or histone acetyltransferase inhibition, restored CD4+ T cell subset specification. Thus, polyamine synthesis via hypusine is critical for maintaining the epigenome to focus TH cell subset fidelity.

immunology↗

bioRxiv2020

In Silico Modeling of Metabolic State in Single Th17 Cells Reveals Novel Regulators of Inflammation and Autoimmunity

Cellular metabolism, a key regulator of immune responses, is difficult to study with current technologies in individual cells Here, we present Compass, an algorithm to characterize the metabolic state of cells based on single-cell RNA-Seq and flux balance analysis. We applied Compass to associate metabolic states with functional variability (pathogenic potential) amongst Th17 cells and recovered a metabolic switch between glycolysis and fatty acid oxidation, akin to known differences between Th17 and Treg cells, as well as novel targets in amino-acid pathways, which we tested through targeted metabolic assays. Compass further predicted a particular glycolytic reaction (phosphoglycerate mutase -- PGAM) that promotes an anti-inflammatory Th17 phenotype, contrary to the common understanding of glycolysis as pro-inflammatory. We demonstrate that PGAM inhibition leads non-pathogenic Th17 cells to adopt a pro-inflammatory transcriptome and induce autoimmunity in vivo. Compass is broadly applicable for characterizing metabolic states of cells and relating metabolic heterogeneity to other cellular phenotypes.

genomics↗

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↗

bioRxiv2019

Coverage-dependent bias creates the appearance of binary splicing in single cells

Single cell RNA sequencing provides powerful insight into the factors that determine each cells unique identity, including variation in transcription and RNA splicing among diverse cell types. Previous studies led to the surprising observation that alternative splicing outcomes among single cells are highly variable and follow a bimodal pattern: a given cell consistently produces either one or the other isoform for a particular splicing choice, with few cells producing both isoforms. Here we show that this pattern arises almost entirely from technical limitations. We analyzed single cell alternative splicing in human and mouse single cell RNA-seq datasets, and modeled them with a probablistic simulator. Our simulations show that low gene expression and low capture efficiency distort the observed distribution of isoforms in single cells. This gives the appearance of a binary isoform distribution, even when the underlying reality is consistent with more than one isoform per cell. We show that accounting for the true amount of information recovered can produce biologically meaningful measurements of splicing in single cells.

genomics↗

bioRxiv2019

Inference of Single-Cell Phylogenies from Lineage Tracing Data

The pairing of CRISPR/Cas9-based gene editing with massively parallel single-cell readouts now enables large-scale lineage tracing. However, the rapid growth in complexity of data from these assays has outpaced our ability to accurately infer phylogenetic relationships. To address this, we provide three resources. First, we introduce Cassiopeia - a suite of scalable and theoretically grounded maximum parsimony approaches for tree reconstruction. Second, we provide a simulation framework for evaluating algorithms and exploring lineage tracer design principles. Finally, we generate the most complex experimental lineage tracing dataset to date - consisting of 34,557 human cells continuously traced over 15 generations, 71% of which are uniquely marked - and use it for benchmarking phylogenetic inference approaches. We show that Cassiopeia outperforms traditional methods by several metrics and under a wide variety of parameter regimes, and provide insight into the principles for the design of improved Cas9-enabled recorders. Together these should broadly enable large-scale mammalian lineage tracing efforts. Cassiopeia and its benchmarking resources are publicly available at www.github.com/YosefLab/Cassiopeia.

bioinformatics↗

bioRxiv2019

A Joint Model of RNA Expression and Surface Protein Abundance in Single Cells

Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) combines unbiased single-cell transcriptome measurements with surface protein quantification comparable to flow cytometry, the gold standard for cell type identification. However, current analysis pipelines cannot address the two primary challenges of CITE-seq data: combining both modalities in a shared latent space that harnesses the power of the paired measurements, and handling the technical artifacts of the protein measurement, which is obscured by non-negligible background noise. Here we present Total Variational Inference (totalVI), a fully probabilistic end-to-end framework for normalizing and analyzing CITE-seq data, based on a hierarchical Bayesian model. In totalVI, the mRNA and protein measurements for each cell are generated from a low-dimensional latent random variable unique to that cell, representing its cellular state. totalVI uses deep neural networks to specify conditional distributions. By leveraging advances in stochastic variational inference, it scales easily to millions of cells. Explicit modeling of nuisance factors enables totalVI to produce denoised data in both domains, as well as a batch-corrected latent representation of cells for downstream analysis tasks.

bioinformatics↗

bioRxiv2019

Detecting Zero-Inflated Genes in Single-Cell Transcriptomics Data

In single-cell RNA sequencing data, biological processes or technical factors may induce an overabundance of zero measurements. Existing probabilistic approaches to interpreting these data either model all genes as zero-inflated, or none. But the overabundance of zeros might be gene-specific. Hence, we propose the AutoZI model, which, for each gene, places a spike-and-slab prior on a mixture assignment between a negative binomial (NB) component and a zero-inflated negative binomial (ZINB) component. We approximate the posterior distribution under this model using variational inference, and employ Bayesian decision theory to decide whether each gene is zero-inflated. On simulated data, AutoZI outperforms the alternatives. On negative control data, AutoZI retrieves predictions consistent to a previous study on ERCC spike-ins and recovers similar results on control RNAs. Applied to several datasets and instances of the 10x Chromium protocol, AutoZI allows both biological and technical interpretations of zero-inflation. Finally, AutoZIs decisions on mouse embyronic stem-cells suggest that zero-inflation might be due to transcriptional bursting.

bioinformatics↗

bioRxiv2019

Deep Generative Models for Detecting Differential Expression in Single Cells

Detecting differentially expressed genes is important for characterizing subpopulations of cells. However, in scRNA-seq data, nuisance variation due to technical factors like sequencing depth and RNA capture efficiency obscures the underlying biological signal. First, we show that deep generative models, which combined Bayesian statistics and deep neural networks, better estimate the log-fold-change in gene expression levels between subpopulations of cells. Second, we use Bayesian decision theory to detect differentially expressed genes while controlling the false discovery rate. Our experiments on simulated and real datasets show that our approach out-performs state-of-the-art DE frameworks. Finally, we introduce a technique for improving the posterior approximation, and show that it also improves differential expression performance.

cell biology↗

bioRxiv2019

Cytoplasmic mRNA decay factors modulate RNA polymerase II processivity in 5’ and 3’ gene regions in yeast

mRNA levels are determined by the balance between mRNA synthesis and decay. Factors that mediate both processes, including the 5 to 3 exonuclease Xrn1, are responsible for the cross talk between the two processes in a manner that buffers steady-state mRNA levels. However, these proteins roles in transcription remain elusive and controversial. Applying NET-seq to yeast cells, we show that Xrn1 functions mainly as a transcriptional activator and that its disruption manifests via the reduction of RNA polymerase II (Pol II) occupancy downstream of transcription start sites. We combine our data and novel mathematical modeling of transcription to suggest that transcription initiation and elongation of targeted genes is modulated by Xrn1. Furthermore, Pol II occupancy markedly increases near cleavage and polyadenylation sites in xrn1{Delta} cells while its activity decreases, a characteristic feature of backtracked Pol II. We also provide indirect evidence that Xrn1 is involved in transcription termination downstream of polyadenylation sites. Two additional decay factors, Dhh1 and Lsm1, seem to function similarly to Xrn1 in transcription, perhaps as a complex, while the decay factors Ccr4 and Rpb4 also perturb transcription in other ways. Interestingly, DFs are capable of differentiating between SAGA- and TFIID-dominated promoters. These two classes of genes respond differently to XRN1 deletion in mRNA synthesis and differentially utilize mRNA decay pathways, raising the possibility that one distinction between the two types of genes lies in the mechanism(s) that balance these processes.

genomics↗

bioRxiv2019

A Canal-Associated Neuron cAMP signalling pathway that regulates C. elegans larval development

Caenorhabditis elegans larval development requires the function of the two Canal-Associated Neurons (CANs): killing the CANs by laser microsurgery or disrupting their development by mutating the gene ceh-10 results in early larval arrest. How these cells promote larval development, however, remains a mystery. In screens for mutations that bypass CAN function, we identified the gene kin-29, which encodes a member of the Salt-Inducible Kinase (SIK) family and a component of a conserved pathway that regulates various C. elegans phenotypes. Like kin-29 loss, gain-of-function mutations in genes that may act upstream of kin-29 or growth in cyclic-AMP analogs bypassed ceh-10 larval arrest, suggesting that a conserved adenylyl cyclase/PKA pathway inhibits KIN-29 to promote larval development and that loss of CAN function results in dysregulation of KIN-29 and larval arrest. The adenylyl cyclase ACY-2 mediates CAN-dependent larval development: acy-2 mutant larvae arrested development with a similar phenotype to ceh-10 mutants, and the arrest phenotype was suppressed by mutations in kin-29. ACY-2 is predominantly expressed in the CANs, and we provide evidence that the acy-2 functions in the CANs to promote larval development. By contrast, cell-specific expression experiments suggest that kin-29 acts in both the hypodermis and neurons, but not in the CANs. Based on our findings, we propose that cAMP produced by ACY-2 in the CANs acts in neighboring neurons and hypodermal cells where it activates PKA and inhibits KIN-29 to promote larval development. We discuss how this conserved pathway could be partitioned between two cells.

developmental biology↗

bioRxiv2019

Harmonization and Annotation of Single-cell Transcriptomics data with Deep Generative Models

As single-cell transcriptomics becomes a mainstream technology, the natural next step is to integrate the accumulating data in order to achieve a common ontology of cell types and states. However, owing to various nuisance factors of variation, it is not straightforward how to compare gene expression levels across data sets and how to automatically assign cell type labels in a new data set based on existing annotations. In this manuscript, we demonstrate that our previously developed method, scVI, provides an effective and fully probabilistic approach for joint representation and analysis of cohorts of single-cell RNA-seq data sets, while accounting for uncertainty caused by biological and measurement noise. We also introduce single-cell ANnotation using Variational Inference (scANVI), a semi-supervised variant of scVI designed to leverage any available cell state annotations -- for instance when only one data set in a cohort is annotated, or when only a few cells in a single data set can be labeled using marker genes. We demonstrate that scVI and scANVI compare favorably to the existing methods for data integration and cell state annotation in terms of accuracy, scalability, and adaptability to challenging settings such as a hierarchical structure of cell state labels. We further show that different from existing methods, scVI and scANVI represent the integrated datasets with a single generative model that can be directly used for any probabilistic decision making task, using differential expression as our case study. scVI and scANVI are available as open source software and can be readily used to facilitate cell state annotation and help ensure consistency and reproducibility across studies.

bioinformatics↗

bioRxiv2019

MPRAnalyze - A statistical framework for Massively Parallel Reporter Assays

Massively parallel reporter assays (MPRAs) are a technique that enables testing thousands of regulatory DNA sequences and their variants in a single, quantitative experiment. Despite growing popularity, there is lack of statistical methods that account for the different sources of uncertainty inherent to these assays, thus effectively leveraging their promise. Development of such methods could help enhance our ability to identify regulatory sequences in the genome, understand their function under various setting, and ultimately gain a better understanding of how the regulatory code and its alteration lead to phenotypic consequence. Here we present MPRAnalyze: a statistical framework dedicated to analyzing MPRA count data. MPRAnalyze addresses the major questions that are posed in the context of MPRA experiments: estimating the magnitude of the effect of a regulatory sequence in a single condition setting, and comparing differential activity of regulatory sequences across multiple conditions. The framework uses a nested construction of generalized linear models to account for uncertainty in both DNA and RNA observations, controls for various sources of unwanted variation, and incorporates negative controls for robust hypothesis testing, thereby providing clear quantitative answers in complex experimental settings. We demonstrate the robustness, accuracy and applicability of MPR-Analyze on simulated data and published data sets and compare it against the existing analysis methodologies. MPRAnalyze is implemented as an R package and is publicly available through Bioconductor [1].

bioinformatics↗

bioRxiv2018

Functional Interpretation of Single-Cell Similarity Maps

We present VISION, a tool for annotating the sources of variation in single cell RNA-seq data in an automated, unbiased and scalable manner. VISION operates directly on the manifold of cell-cell similarity and employs a flexible annotation approach that can operate either with or without preconceived stratification of the cells into groups or along a continuum. We demonstrate the utility of VISION using a relatively homogeneous set of B cells from a cohort of lupus patients and healthy controls and show that it can derive important sources of cellular variation and link them to clinical phenotypes in a stratification free manner. VISION produces an interactive, low latency and feature rich web-based report that can be easily shared amongst researchers.

bioinformatics↗

bioRxiv2018

Single-cell transcriptomics identifies drivers of local inflammation in multiple sclerosis

Cerebrospinal fluid (CSF) protects the central nervous system (CNS) and analyzing CSF aids the diagnosis of CNS diseases, but our understanding of CSF leukocytes remains superficial. Here, we firstly provide a transcriptional map of single leukocytes in CSF compared to blood. Leukocyte composition and transcriptome were compartment-specific with CSF-enrichment of myeloid dendritic cells and a border-associated phenotype of monocytes.\n\nWe secondly tested how multiple sclerosis (MS) - an autoimmune disease of the CNS - affected both compartments. MS increased transcriptional diversity in blood, while it preferentially increased cell type diversity in CSF. In addition to the known expansion of B lineage cells, we identified an increase of cytotoxic-phenotype and follicular T helper (TFH) cells in the CSF. In mice, TFH cells accordingly promoted B cell infiltration into the CNS and severity of MS animal models. Immune mechanisms in MS are thus highly compartmentalized and indicate local T/B cell interaction.

immunology↗

bioRxiv2018

Reconstructing B cell receptor sequences from short-read single cell RNA-sequencing with BRAPeS

RNA-sequencing of single B cells provides simultaneous measurements of the cell state and its binding specificity. However, in order to uncover the latter further reconstruction of the B cell receptor (BCR) sequence is needed. We present BRAPeS, an algorithm for reconstructing BCRs from short-read paired-end single cell RNA-sequencing. BRAPeS is accurate and achieves a high success rate even at very short (25bp) read length, which can decrease the cost and increase the number of cells that can be analyzed compared to long reads. BRAPeS is publicly available in the following link: https://github.com/YosefLab/BRAPeS.

bioinformatics↗

bioRxiv2018

SymSim: simulating multi-faceted variability in single cell RNA sequencing

The abundance of new computational methods for processing and interpreting transcriptomes at a single cell level raises the need for in-silico platforms for evaluation and validation. Simulated datasets which resemble the properties of real datasets can aid in method development and prioritization as well as in questions in experimental design by providing an objective ground truth. Here, we present SymSim, a simulator software that explicitly models the processes that give rise to data observed in single cell RNA-Seq experiments. The components of the SymSim pipeline pertain to the three primary sources of variation in single cell RNA-Seq data: noise intrinsic to the process of transcription, extrinsic variation that is indicative of different cell states (both discrete and continuous), and technical variation due to low sensitivity and measurement noise and bias. Unlike other simulators, the parameters that govern the simulation process directly represent meaningful properties such as mRNA capture rate, the number of PCR cycles, sequencing depth, or the use of unique molecular identifiers. We demonstrate how SymSim can be used for benchmarking methods for clustering and differential expression and for examining the effects of various parameters on their performance. We also show how SymSim can be used to evaluate the number of cells required to detect a rare population and how this number deviates from the theoretical lower bound as the quality of the data decreases. SymSim is publicly available as an R package and allows users to simulate datasets with desired properties or matched with experimental data.

bioinformatics↗

bioRxiv2018

Massively parallel characterization of regulatory dynamics during neural induction

The molecular components governing neural induction remain largely unknown. Here, we applied a suite of genomic and computational tools to comprehensively identify these components. We performed RNA-seq, ChIP-seq (H3K27ac, H3K27me3) and ATAC-seq on human embryonic stem cells (hESCs) at seven early neural differentiation time points (0-72 hours) and identified thousands of induced genes and regulatory regions. We analyzed the function of ~2,500 selected regions using massively parallel reporter assays at all time points. We found numerous temporal enhancers that correlated with similarly timed epigenetic marks and gene expression. Development of a prioritization method that incorporated all genomic data identified key transcription factors (TFs) involved in neural induction. Individual overexpression of eleven TFs and several combinations in hESCs found novel neural induction regulators. Combined, our results provide a comprehensive map of genes and functional regulatory elements involved in neural induction and identify master regulator TFs that are instrumental for this process.\n\nOne Sentence SummaryUsing numerous genomic assays and computational tools we characterized the dynamic changes that take place during neural induction.

genomics↗

bioRxiv2018

Mango: Distributed Visualization for Genomic Analysis

The decreasing cost of DNA sequencing over the past decade has led to an explosion of available sequencing datasets, leaving us with terabytes to petabytes of data to explore and analyze. It is critical for analysts in research and clinical settings to be able to develop new data-driven hypotheses from these datasets through bias identification, analysis of data quality, and testing different algorithms and parameter settings. However, current interactive tools for sequence analysis are designed to run on single machines that do not scale to the size of modern genomic datasets, and rely on precomputed static views, rather than allowing direct interaction with the primary dataset. Mango is a genomic sequence visualization and analysis platform that removes these constraints regarding scalability and staticity by leveraging the power of multi-node compute clusters in the cloud to allow interactive analysis over terabytes of sequencing data. Mango provides both a genome browser graphical user interface and programmable notebook form factor to allow users of varying analytical experience to explore large sequencing datasets on both private clusters and in the cloud. These tools provide a flexible environment for interactive exploration of genomic datasets, while surpassing the computational limits of single-node genomic visualization tools.

bioinformatics↗