scholarly journals Comprehensive analysis of single cell ATAC-seq data with SnapATAC

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Rongxin Fang ◽  
Sebastian Preissl ◽  
Yang Li ◽  
Xiaomeng Hou ◽  
Jacinta Lucero ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Expression Patterns ◽  
Regulatory Elements ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
Open Chromatin ◽  
Cell Type ◽  
Process Data ◽  
Cell Type Specific

AbstractIdentification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators.

scholarly journals SnapATAC: A Comprehensive Analysis Package for Single Cell ATAC-seq

2019 ◽  
Author(s):  
Rongxin Fang ◽  
Sebastian Preissl ◽  
Yang Li ◽  
Xiaomeng Hou ◽  
Jacinta Lucero ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Expression Patterns ◽  
Sampling Technique ◽  
Regulatory Elements ◽  
Cellular Heterogeneity ◽  
Low Rank ◽  
Specific Gene ◽  
Open Chromatin ◽  
Process Data

AbstractIdentification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by heterogeneity of the samples. Single cell analysis of transposase-accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volumes of data could pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC can efficiently dissect cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, a sampling technique that generates the low rank embedding for large-scale dataset, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC was applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis revealed ∼370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate transcriptional regulators in each of the cell types.

Single-cell RNA-seq reveals the transcriptional landscape in ischemic stroke

2021 ◽  
pp. 0271678X2110267
Author(s):  
Kai Zheng ◽  
Lingmin Lin ◽  
Wei Jiang ◽  
Lin Chen ◽  
Xiyue Zhang ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Single Cell ◽  
Expression Patterns ◽  
Cellular Heterogeneity ◽  
Ependymal Cells ◽  
Specific Gene ◽  
Specific Cell ◽  
Cell Type ◽  
Cns Inflammation ◽  
Cell Type Specific

Ischemic stroke (IS) is a detrimental neurological disease with limited treatments options. It has been challenging to define the roles of brain cell subsets in IS onset and progression due to cellular heterogeneity in the CNS. Here, we employed single-cell RNA sequencing (scRNA-seq) to comprehensively map the cell populations in the mouse model of MCAO (middle cerebral artery occlusion). We identified 17 principal brain clusters with cell-type specific gene expression patterns as well as specific cell subpopulations and their functions in various pathways. The CNS inflammation triggered upregulation of key cell type-specific genes unpublished before. Notably, microglia displayed a cell differentiation diversity after stroke among its five distinct subtypes. Importantly, we found the potential trajectory branches of the monocytes/macrophage’s subsets. Finally, we also identified distinct subclusters among brain vasculature cells, ependymal cells and other glia cells. Overall, scRNA-seq revealed the precise transcriptional changes during neuroinflammation at the single-cell level, opening up a new field for exploration of the disease mechanisms and drug discovery in stroke based on the cell-subtype specific molecules.

scholarly journals Bulk Tissue Cell Type Deconvolution with Multi-Subject Single-Cell Expression Reference

2018 ◽  
Author(s):  
Xuran Wang ◽  
Jihwan Park ◽  
Katalin Susztak ◽  
Nancy R. Zhang ◽  
Mingyao Li
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Tissue Cell ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Type Specific ◽  
Cell Expression

AbstractWe present MuSiC, a method that utilizes cell-type specific gene expression from single-cell RNA sequencing (RNA-seq) data to characterize cell type compositions from bulk RNA-seq data in complex tissues. When applied to pancreatic islet and whole kidney expression data in human, mouse, and rats, MuSiC outperformed existing methods, especially for tissues with closely related cell types. MuSiC enables characterization of cellular heterogeneity of complex tissues for identification of disease mechanisms.

scholarly journals A cis-regulatory atlas in maize at single-cell resolution

2020 ◽  
Author(s):  
Alexandre P. Marand ◽  
Zongliang Chen ◽  
Andrea Gallavotti ◽  
Robert J. Schmitz
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Regulatory Elements ◽  
Cellular Heterogeneity ◽  
Cell Type ◽  
Crop Species ◽  
Cell Functions ◽  
Cell Type Specific ◽  
Type Specification ◽  
Accessible Chromatin

ABSTRACTCis-regulatory elements (CREs) encode the genomic blueprints for coordinating spatiotemporal gene expression programs underlying highly specialized cell functions. To identify CREs underlying cell-type specification and developmental transitions, we implemented single-cell sequencing of Assay for Transposase Accessible Chromatin in an atlas of Zea mays organs. We describe 92 distinct states of chromatin accessibility across more than 165,913 putative CREs, 56,575 cells, and 52 known cell-types in maize using a novel implementation of regularized quasibinomial logistic regression. Cell states were largely determined by combinatorial accessibility of transcription factors (TFs) and their binding sites. A neural network revealed that cell identity could be accurately predicted (>0.94) solely based on TF binding site accessibility. Co-accessible chromatin recapitulated higher-order chromatin interactions, with distinct sets of TFs coordinating cell type-specific regulatory dynamics. Pseudotime reconstruction and alignment with Arabidopsis thaliana trajectories identified conserved TFs, associated motifs, and cis-regulatory regions specifying sequential developmental progressions. Cell-type specific accessible chromatin regions were enriched with phenotype-associated genetic variants and signatures of selection, revealing the major cell-types and putative CREs targeted by modern maize breeding. Collectively, our analysis affords a comprehensive framework for understanding cellular heterogeneity, evolution, and cis-regulatory grammar of cell-type specification in a major crop species.

scholarly journals CellWalkR: An R Package for integrating single-cell and bulk data to resolve regulatory elements

2021 ◽  
Author(s):  
Pawel F. Przytycki ◽  
Katherine S. Pollard
Keyword(s):  
Random Walk ◽  
Single Cell ◽  
R Package ◽  
Regulatory Elements ◽  
Random Walk Model ◽  
Open Chromatin ◽  
Cell Type ◽  
Regulatory Regions ◽  
Bulk Data ◽  
Cell Type Specific

AbstractWhile single-cell open chromatin (scATAC-seq) data allows for the identification of cell type-specific regulatory regions, it is much sparser than bulk data. CellWalkR is an R package that performs an integration of external labeling and bulk epigenetic data with scATAC-seq using a network-based random walk model to help overcome this sparsity. Outputs include cell type labels for individual cells and regulatory regions.Availability and implementationCellWalkR is freely available as an R package under a GNU GPL-2.0 License, and can be accessed from https://github.com/PFPrzytycki/CellWalkR with an accompanying vignette for analyzing example data.

scholarly journals Single-cell network biology for resolving cellular heterogeneity in human diseases

2020 ◽  
Vol 52 (11) ◽  
pp. 1798-1808
Author(s):  
Junha Cha ◽  
Insuk Lee
Keyword(s):  
Single Cell ◽  
Gene Networks ◽  
Cell Types ◽  
Network Biology ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
Transcriptome Data ◽  
Cell Type ◽  
Cell Network ◽  
Cell Type Specific

AbstractUnderstanding cellular heterogeneity is the holy grail of biology and medicine. Cells harboring identical genomes show a wide variety of behaviors in multicellular organisms. Genetic circuits underlying cell-type identities will facilitate the understanding of the regulatory programs for differentiation and maintenance of distinct cellular states. Such a cell-type-specific gene network can be inferred from coregulatory patterns across individual cells. Conventional methods of transcriptome profiling using tissue samples provide only average signals of diverse cell types. Therefore, reconstructing gene regulatory networks for a particular cell type is not feasible with tissue-based transcriptome data. Recently, single-cell omics technology has emerged and enabled the capture of the transcriptomic landscape of every individual cell. Although single-cell gene expression studies have already opened up new avenues, network biology using single-cell transcriptome data will further accelerate our understanding of cellular heterogeneity. In this review, we provide an overview of single-cell network biology and summarize recent progress in method development for network inference from single-cell RNA sequencing (scRNA-seq) data. Then, we describe how cell-type-specific gene networks can be utilized to study regulatory programs specific to disease-associated cell types and cellular states. Moreover, with scRNA data, modeling personal or patient-specific gene networks is feasible. Therefore, we also introduce potential applications of single-cell network biology for precision medicine. We envision a rapid paradigm shift toward single-cell network analysis for systems biology in the near future.

scholarly journals Enhancers and Transcriptional Regulation

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. SCI-14-SCI-14
Author(s):  
Joanna Wysocka
Keyword(s):  
Gene Expression ◽  
Transcriptional Regulation ◽  
Conflicts Of Interest ◽  
Expression Patterns ◽  
Regulatory Elements ◽  
Specific Gene ◽  
Regulatory Sequence ◽  
Cell Type ◽  
Control Of Gene Expression ◽  
Cell Type Specific

Abstract Interactions between the genome and its cellular and signaling environments, which ultimately occur at the level of chromatin, are the key to comprehending how cell-type-specific gene expression patterns arise and are maintained during development or are misregulated in disease. Central to the cell type-specific transcriptional regulation are distal cis-regulatory elements called enhancers, which function in a modular way to provide exquisite spatiotemporal control of gene expression during development. We are using a combination of genomic, genetic, biochemical, and single-cell approaches to investigate how enhancers are activated in response to developmental stimuli, how they communicate with target promoters over large genomic distances to regulate transcriptional outputs, what is the role of chromatin modification and remodeling in facilitating or restricting enhancer activity and how regulatory sequence change leads to the phenotypic divergence in humans. I will discuss our latest results on the mechanisms underlying enhancer function and gene regulation in development and disease. Disclosures No relevant conflicts of interest to declare.

scholarly journals Genetic influences on cell type specific gene expression and splicing during neurogenesis elucidate regulatory mechanisms of brain traits

2020 ◽  
Author(s):  
Nil Aygün ◽  
Angela L. Elwell ◽  
Dan Liang ◽  
Michael J. Lafferty ◽  
Kerry E. Cheek ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regulatory Elements ◽  
Regulatory Mechanisms ◽  
Specific Gene ◽  
Cell Type ◽  
Specific Expression ◽  
Risk Variants ◽  
Allele Specific ◽  
Cell Type Specific ◽  
Regulatory Effects

SummaryInterpretation of the function of non-coding risk loci for neuropsychiatric disorders and brain-relevant traits via gene expression and alternative splicing is mainly performed in bulk post-mortem adult tissue. However, genetic risk loci are enriched in regulatory elements of cells present during neocortical differentiation, and regulatory effects of risk variants may be masked by heterogeneity in bulk tissue. Here, we map e/sQTLs and allele specific expression in primary human neural progenitors (n=85) and their sorted neuronal progeny (n=74). Using colocalization and TWAS, we uncover cell-type specific regulatory mechanisms underlying risk for these traits.

New insights into promoter–enhancer communication mechanisms revealed by dynamic single-molecule imaging

2021 ◽  
Author(s):  
Jieru Li ◽  
Alexandros Pertsinidis
Keyword(s):  
Gene Expression ◽  
Single Molecule ◽  
Target Genes ◽  
Regulatory Elements ◽  
Specific Gene ◽  
Single Molecule Imaging ◽  
Cell Type ◽  
Regulatory Information ◽  
Cell Type Specific ◽  
Target Promoters

Establishing cell-type-specific gene expression programs relies on the action of distal enhancers, cis-regulatory elements that can activate target genes over large genomic distances — up to Mega-bases away. How distal enhancers physically relay regulatory information to target promoters has remained a mystery. Here, we review the latest developments and insights into promoter–enhancer communication mechanisms revealed by live-cell, real-time single-molecule imaging approaches.

scholarly journals Dynamic changes in cis-regulatory occupancy by Six1 and its cooperative interactions with distinct cofactors drive lineage-specific gene expression programs during progressive differentiation of the auditory sensory epithelium

2020 ◽  
Vol 48 (6) ◽  
pp. 2880-2896 ◽  
Author(s):  
Jun Li ◽  
Ting Zhang ◽  
Aarthi Ramakrishnan ◽  
Bernd Fritzsch ◽  
Jinshu Xu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regulatory Elements ◽  
Sensory Epithelium ◽  
Hair Bundle ◽  
Specific Gene ◽  
Cell Type ◽  
Specific Gene Expression ◽  
Wide Range ◽  
Cell Type Specific ◽  
Lineage Specific Gene

Abstract The transcription factor Six1 is essential for induction of sensory cell fate and formation of auditory sensory epithelium, but how it activates gene expression programs to generate distinct cell-types remains unknown. Here, we perform genome-wide characterization of Six1 binding at different stages of auditory sensory epithelium development and find that Six1-binding to cis-regulatory elements changes dramatically at cell-state transitions. Intriguingly, Six1 pre-occupies enhancers of cell-type-specific regulators and effectors before their expression. We demonstrate in-vivo cell-type-specific activity of Six1-bound novel enhancers of Pbx1, Fgf8, Dusp6, Vangl2, the hair-cell master regulator Atoh1 and a cascade of Atoh1’s downstream factors, including Pou4f3 and Gfi1. A subset of Six1-bound sites carry consensus-sequences for its downstream factors, including Atoh1, Gfi1, Pou4f3, Gata3 and Pbx1, all of which physically interact with Six1. Motif analysis identifies RFX/X-box as one of the most significantly enriched motifs in Six1-bound sites, and we demonstrate that Six1-RFX proteins cooperatively regulate gene expression through binding to SIX:RFX-motifs. Six1 targets a wide range of hair-bundle regulators and late Six1 deletion disrupts hair-bundle polarity. This study provides a mechanistic understanding of how Six1 cooperates with distinct cofactors in feedforward loops to control lineage-specific gene expression programs during progressive differentiation of the auditory sensory epithelium.

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