Biology subjects

Gevirtzman, L.

Publications and source records attributed to Gevirtzman, L..

5 recordsLinked to original sources

bioRxiv2022

Matrix prior for data transfer between single cell data types in latent Dirichlet allocation

Single cell ATAC-seq (scATAC-seq) enables the mapping of regulatory elements in fine-grained cell types. Despite this advance, analysis of the resulting data is challenging, and large scale scATAC-seq data are difficult to obtain and expensive to generate. This motivates a method to leverage information from previously generated large scale scATAC-seq or scRNA-seq data to guide our analysis of new scATAC-seq datasets. We analyze scATAC-seq data using latent Dirichlet allocation (LDA), a Bayesian algorithm that was developed to model text corpora, summarizing documents as mixtures of topics defined based on the words that distinguish the documents. When applied to scATAC-seq, LDA treats cells as documents and their accessible sites as words, identifying "topics" based on the cell type-specific accessible sites in those cells. Previous work used uniform symmetric priors in LDA, but we hypothesized that nonuniform matrix priors generated from LDA models trained on existing data sets may enable improved detection of cell types in new data sets, especially if they have relatively few cells. In this work, we test this hypothesis in scATAC-seq data from whole C. elegans nematodes and SHARE-seq data from mouse skin cells. We show that nonsymmetric matrix priors for LDA improve our ability to capture cell type information from small scATAC-seq datasets.

bioinformatics↗

bioRxiv2022

Whole-body gene expression atlas of an adult metazoan

Animals are integrated organ systems composed of interacting cells whose structure and function are in turn defined by their active genes. Understanding what distinguishes physiological and disease states therefore requires systemic knowledge of the gene activities that define the distinct cells that make up an animal. Towards this goal, this study reports the first single-cell resolution transcriptional atlas of a fertile multicellular organism: Caenorhabditis elegans. The scRNA-Seq compendium of wild-type young adult C. elegans comprises 159 distinct cell types with 18,033 genes expressed across cell types. Fewer than 300 of these genes are housekeeping genes as evidenced by their consistent expression across cell types and conditions, and by their basic and essential functions; 170 of these housekeeping genes are conserved across phyla. The 362 transcription factors with available ChIP-Seq data are linked to patterns of gene expression of different cell types. To identify potential interactions between cell types, we used the in silico tool cell2cell to predict molecular patterns reflecting both known and uncharacterized intercellular interactions across the C. elegans body. Finally, we present WormSeq (wormseq.org), a web interface that, among other functions, enables users to query gene expression across cell types, identify cell-type specific and potential housekeeping genes, analyze candidate ligand-receptors mediating communication between cells, and study promiscuous and cell-specific transcription factors. The datasets, analyses, and tools presented here will enable the generation of testable hypotheses about the cell and organ-specific function of genes in diverse biological contexts.

genomics↗

bioRxiv2020

Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes

Recently developed single cell technologies allow researchers to characterize cell states at ever greater resolution and scale. C. elegans is a particularly tractable system for studying development, and recent single cell RNA-seq studies characterized the gene expression patterns for nearly every cell type in the embryo and at the second larval stage (L2). Gene expression patterns are useful for learning about gene function and give insight into the biochemical state of different cell types; however, in order to understand these cell types, we must also determine how these gene expression levels are regulated. We present the first single cell ATAC-seq study in C. elegans. We collected data in L2 larvae to match the available single cell RNA-seq data set, and we identify tissue-specific chromatin accessibility patterns that align well with existing data, including the L2 single cell RNA-seq results. Using a novel implementation of the latent Dirichlet allocation algorithm, we leverage the single-cell resolution of the sci-ATAC-seq data to identify accessible loci at the level of individual cell types, providing new maps of putative cell type-specific gene regulatory sites, with promise for better understanding of cellular differentiation and gene regulation in the worm.

genomics↗

bioRxiv2019

To mock or not: a comprehensive comparison of mock IP and DNA input for ChIP-seq

Chromatin immunoprecipitation (IP) followed by sequencing (ChIP-seq) is the gold standard to detect genome-wide DNA-protein binding. The binding sites of transcription factors facilitate many biological studies. Of emerging concern is the abundance of spurious sites in ChIP-seq, which are mainly caused by uneven genomic sonication and nonspecific interactions between chromatin and antibody. A "mock" IP is designed to correct for both factors, whereas a DNA input control corrects only for uneven sonication. However, a mock IP is more susceptible to technical noise than a DNA input, and empirically, these two controls perform similarly for ChIP-seq. Therefore, DNA input is currently being used almost exclusively. With a large dataset, we demonstrate that using a DNA input control results in a definable set of spurious sites, and their abundance is tightly associated with the intrinsic properties of the ChIP-seq sample. For example, compared to human cell lines, samples such as human tissues and whole worm and fly have more accessible genomes, and thus have more spurious sites. The large and varying abundance of spurious sites may impede comparative studies across multiple samples. In contrast, using a mock IP as control substantially removes these spurious sites, resulting in high-quality binding sites and facilitating their comparability across samples. Although outperformed by mock IP, DNA input is still informative and has unique advantages. Therefore, we have developed a method to use both controls in combination to further improve binding site detection.

genomics↗

bioRxiv2017

MIP-MAP: High-Throughput Mapping of Caenorhabditis elegans Temperature Sensitive Mutants via Molecular Inversion Probes

Temperature sensitive (TS) alleles are important tools for the genetic and functional analysis of essential genes in many model organisms. While isolating TS alleles is not difficult, determining the TS-conferring mutation can be problematic. Even with whole-genome sequencing (WGS) data there is a paucity of predictive methods for identifying TS alleles from DNA sequence alone. We assembled 173 TS lethal mutants of Caenorhabditis elegans and used WGS to identify several hundred mutations per strain. We leveraged single molecule molecular inversion probes (MIPs) to sequence variant sites at high depth in the cross-progeny of TS mutants and a mapping strain with identified sequence variants but no apparent phenotypic differences from the reference N2 strain. By sampling for variants at ~1Mb intervals across the genome we genetically mapped mutant alleles at a resolution comparable to current standards in a process we call MIP-MAP. The MIP-MAP protocol, however, permits high-throughput sequencing of multiple TS mutation mapping libraries at less than 200K reads per library. Using MIP-MAP on a subset of TS mutants, via a competitive selection assay and standard recombinant mutant selection, we defined TS-associated intervals of 3Mb or less. Our results suggest this collection of strains contains a diverse library of TS alleles for genes involved in development and reproduction. MIP-MAP is a robust method to genetically map mutations in both viable and essential genes. The MIPs protocol should allow high-throughput tracking of genetic variants in any mixed population.

genomics↗