scholarly journals Efficient pre-processing of Single-cell ATAC-seq data

2021 ◽  
Author(s):  
Fan Gao ◽  
Lior Pachter
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Healthy Adult ◽  
Chromatin Interaction ◽  
Open Chromatin ◽  
Cell Type ◽  
Cell Groups ◽  
Cell Type Specific ◽  
Interaction Traces ◽  
Regulatory Landscape

The primary tool currently used to pre-process 10X Chromium single-cell ATAC-seq data is Cell Ranger, which can take very long to run on standard datasets. To facilitate rapid pre-processing that enables reproducible workflows, we present a suite of tools called scATAK for pre-processing single-cell ATAC-seq data that is 18 times faster than Cell Ranger on human samples, and that uses 33% less RAM when 8 CPU threads are used. Our tool can also calculate chromatin interaction potential matrices, and generate open chromatin signals and interaction traces for cell groups. We demonstrate the utility of scATAK in an exploration of the chromatin regulatory landscape of a healthy adult human brain and show that it can reveal cell-type-specific features. scATAK is available at https://pachterlab.github.io/scATAK/.

scholarly journals Improving Chromatin-Interaction Prediction Using Single-Cell Open-Chromatin Profiles and Making Insight Into the Cis-Regulatory Landscape of the Human Brain

2021 ◽  
Vol 12 ◽  
Author(s):  
Neetesh Pandey ◽  
Omkar Chandra ◽  
Shreya Mishra ◽  
Vibhor Kumar
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Long Range ◽  
Target Genes ◽  
Interaction Analysis ◽  
Chromatin Interaction ◽  
Open Chromatin ◽  
Cell Type ◽  
Range Interaction ◽  
Long Range Interactions

Single-cell open-chromatin profiles have the potential to reveal the pattern of chromatin-interaction in a cell type. However, currently available cis-regulatory network prediction methods using single-cell open-chromatin profiles focus more on local chromatin interactions despite the fact that long-range interactions among genomic sites play a significant role in gene regulation. Here, we propose a method that predicts both short and long-range interactions among genomic sites using single-cell open chromatin profiles. Our method, termed as single-cell epigenome based chromatin-interaction analysis (scEChIA) exploits signal imputation and refined L1 regularization. For a few single-cell open-chromatin profiles, scEChIA outperformed other tools even in terms of accuracy of prediction. Using scEChIA, we predicted almost 0.7 million interactions among genomic sites across seven cell types in the human brain. Further analysis revealed cell type for connection between genes and expression quantitative trait locus (eQTL) in the human brain and making insight about target genes of human-accelerated-elements and disease-associated mutations. Our analysis enabled by scEChIA also hints about the possible action of a few transcription factors (TFs), especially through long-range interaction in brain endothelial cells.

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 Capturing cell type-specific chromatin structural patterns by applying topic modeling to single-cell Hi-C data

2019 ◽  
Author(s):  
Hyeon-Jin Kim ◽  
Galip Gürkan Yardımcı ◽  
Giancarlo Bonora ◽  
Vijay Ramani ◽  
Jie Liu ◽  
...  
Keyword(s):  
Single Cell ◽  
Topic Modeling ◽  
Biological Information ◽  
Chromatin Interaction ◽  
Cell Type ◽  
3D Genome ◽  
Genome Wide ◽  
Significant Barrier ◽  
Chromatin Structural ◽  
Cell Type Specific

AbstractSingle-cell Hi-C (scHi-C) interrogates genome-wide chromatin interaction in individual cells, allowing us to gain insights into 3D genome organization. However, the extremely sparse nature of scHi-C data poses a significant barrier to analysis, limiting our ability to tease out hidden biological information. In this work, we approach this problem by applying topic modeling to scHi-C data. Topic modeling is well-suited for discovering latent topics in a collection of discrete data. For our analysis, we generate twelve different single-cell combinatorial indexed Hi-C (sciHi-C) libraries from five human cell lines (GM12878, H1Esc, HFF, IMR90, and HAP1), consisting over 25,000 cells. We demonstrate that topic modeling is able to successfully capture cell type differences from sciHi-C data in the form of “chromatin topics.” We further show enrichment of particular compartment structures associated with locus pairs in these topics.

scholarly journals 3D Epigenomic Characterization Reveals Insights Into Gene Regulation and Lineage Specification During Corticogenesis

2020 ◽  
Author(s):  
Michael Song ◽  
Mark-Phillip Pebworth ◽  
Xiaoyu Yang ◽  
Armen Abnousi ◽  
Changxu Fan ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Human Brain ◽  
Radial Glia ◽  
Fine Tuning ◽  
Open Chromatin ◽  
Cell Type ◽  
Lineage Specification ◽  
Novel Approach ◽  
Cell Type Specific ◽  
Lineage Specific Genes

AbstractLineage-specific epigenomic changes during human corticogenesis have previously remained elusive due to challenges with tissue heterogeneity and sample availability. Here, we analyze cis-regulatory chromatin interactions, open chromatin regions, and transcriptomes for radial glia, intermediate progenitor cells, excitatory neurons, and interneurons isolated from mid-gestational human brain samples. We show that chromatin looping underlies transcriptional regulation for lineage-specific genes, with transcription factor motifs, families of transposable elements, and disease-associated variants enriched at distal interacting regions in a cell type-specific manner. A subset of promoters exhibit unusually high degrees of chromatin interactivity, which we term super interactive promoters. Super interactive promoters are enriched for critical lineage-specific genes, suggesting that interactions at these loci contribute to the fine-tuning of cell type-specific transcription. Finally, we present CRISPRview, a novel approach for validating distal interacting regions in primary cells. Our study presents the first characterization of cell type-specific 3D epigenomic landscapes during human corticogenesis, advancing our understanding of gene regulation and lineage specification during human brain development.

scholarly journals Multiscale Co-Expression in the Brain

2020 ◽  
Author(s):  
Benjamin D. Harris ◽  
Megan Crow ◽  
Stephan Fischer ◽  
Jesse Gillis
Keyword(s):  
Motor Cortex ◽  
Single Cell ◽  
Rna Sequencing ◽  
Primary Motor Cortex ◽  
Cell Types ◽  
Cell Type ◽  
Single Cell Rna Sequencing ◽  
Cell Type Specific ◽  
The Brain ◽  
Regulatory Landscape

ABSTRACTSingle-cell RNA-sequencing (scRNAseq) data can reveal co-regulatory relationships between genes that may be hidden in bulk RNAseq due to cell type confounding. Using the primary motor cortex data from the Brain Initiative Cell Census Network (BICCN), we study cell type specific co-expression across 500,000 cells. Surprisingly, we find that the same gene-gene relationships that differentiate cell types are evident at finer and broader scales, suggesting a consistent multiscale regulatory landscape.

scholarly journals The regulatory landscape of Arabidopsis thaliana roots at single-cell resolution

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Michael W. Dorrity ◽  
Cristina M. Alexandre ◽  
Morgan O. Hamm ◽  
Anna-Lena Vigil ◽  
Stanley Fields ◽  
...  
Keyword(s):  
Arabidopsis Thaliana ◽  
Single Cell ◽  
Regulatory Networks ◽  
Cell Types ◽  
Analytical Framework ◽  
Cell Type ◽  
Tissue Heterogeneity ◽  
Shared Information ◽  
Cell Type Specific ◽  
Regulatory Landscape

AbstractThe scarcity of accessible sites that are dynamic or cell type-specific in plants may be due in part to tissue heterogeneity in bulk studies. To assess the effects of tissue heterogeneity, we apply single-cell ATAC-seq to Arabidopsis thaliana roots and identify thousands of differentially accessible sites, sufficient to resolve all major cell types of the root. We find that the entirety of a cell’s regulatory landscape and its transcriptome independently capture cell type identity. We leverage this shared information on cell identity to integrate accessibility and transcriptome data to characterize developmental progression, endoreduplication and cell division. We further use the combined data to characterize cell type-specific motif enrichments of transcription factor families and link the expression of family members to changing accessibility at specific loci, resolving direct and indirect effects that shape expression. Our approach provides an analytical framework to infer the gene regulatory networks that execute plant development.

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 epigenomic atlas of the developing human brain and organoids

2019 ◽  
Author(s):  
Ryan S. Ziffra ◽  
Chang N. Kim ◽  
Amy Wilfert ◽  
Tychele N. Turner ◽  
Maximilian Haeussler ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Cell Fate ◽  
Cell Types ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Chromatin State ◽  
Cell Type ◽  
Fate Specification ◽  
Cell Type Specific

AbstractDynamic changes in chromatin accessibility coincide with important aspects of neuronal differentiation, such as fate specification and arealization and confer cell type-specific associations to neurodevelopmental disorders. However, studies of the epigenomic landscape of the developing human brain have yet to be performed at single-cell resolution. Here, we profiled chromatin accessibility of >75,000 cells from eight distinct areas of developing human forebrain using single cell ATAC-seq (scATACseq). We identified thousands of loci that undergo extensive cell type-specific changes in accessibility during corticogenesis. Chromatin state profiling also reveals novel distinctions between neural progenitor cells from different cortical areas not seen in transcriptomic profiles and suggests a role for retinoic acid signaling in cortical arealization. Comparison of the cell type-specific chromatin landscape of cerebral organoids to primary developing cortex found that organoids establish broad cell type-specific enhancer accessibility patterns similar to the developing cortex, but lack many putative regulatory elements identified in homologous primary cell types. Together, our results reveal the important contribution of chromatin state to the emerging patterns of cell type diversity and cell fate specification and provide a blueprint for evaluating the fidelity and robustness of cerebral organoids as a model for cortical development.

scholarly journals Single-cell interactomes of the human brain reveal cell-type specific convergence of brain disorders

2019 ◽  
Author(s):  
Shahin Mohammadi ◽  
Jose Davila-Velderrain ◽  
Manolis Kellis
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Gene Networks ◽  
Specific Activity ◽  
Gene Interaction ◽  
Reference Network ◽  
Brain Disorders ◽  
Cell Type ◽  
Human Interactome ◽  
Cell Type Specific

AbstractThe reference human interactome has been instrumental in the systems-level study of the molecular inner workings of the cell, providing a framework to analyze the network context of disease associated gene perturbations. However, reference organismal interactomes do not capture the tissue- and cell type-specific context in which proteins and modules preferentially act. Emerging single-cell profiling technologies, which survey the transcriptional cell-state distribution of complex tissues, could be used to infer the single-cell context of gene interactions. Here we introduce SCINET (Single-Cell Imputation and NETwork construction), a computational framework that reconstructs an ensemble of cell type-specific interactomes by integrating a global, context-independent reference interactome with a single-cell gene expression profile. SCINET addresses technical challenges of single-cell data by robustly imputing, transforming, and normalizing the initially noisy and sparse expression data. Subsequently, cell-level gene interaction probabilities and group-level gene interaction strengths are computed, resulting in cell type specific interactomes. We use SCINET to analyze the human cortex, reconstructing interactomes for the major cell types of the adult human brain. We identify network neighborhoods composed of topologically-specific genes that are central for cell-type influence but not for global interactome connectivity. We use the reconstructed interactomes to analyze the specificity and modularity of perturbations associated with neurodegenerative, neuropsychiatric, and neoplastic brain disorders; finding high variability across diseases, yet overall consistency in patterns of cell-type convergence for diseases of the same group. We infer for each disorder group disease gene networks with preferential cell-type specific activity that can aid the design and interpretation of cell-type resolution experiments. Finally, focusing on the pleiotropy of schizophrenia and bipolar disorder, we show how cell type specific interactomes enable the identification of disease genes with preferential influence on neuronal, glial, or glial-neuronal cells. The SCINET framework is applicable to any organism, cell-type/tissue, and reference network; it is freely available athttps://github.com/shmohammadi86/SCINET.

scholarly journals Cell Type-Specific Chromatin Accessibility Analysis in the Mouse and Human Brain

2020 ◽  
Author(s):  
Devin Rocks ◽  
Ivana Jaric ◽  
Lydia Tesfa ◽  
John M. Greally ◽  
Masako Suzuki ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Human Brain ◽  
Brain Tissue ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Open Chromatin ◽  
Cell Type ◽  
Accessibility Analysis ◽  
Cell Type Specific ◽  
Study Designs

ABSTRACTThe Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) is becoming increasingly popular in the neuroscience field where chromatin regulation is thought to be involved in neurodevelopment, activity-dependent gene regulation, hormonal and environmental responses, and the pathophysiology of neuropsychiatric disorders. The advantages of using this assay include a small amount of material needed, relatively simple and fast protocol, and the ability to capture a range of gene regulatory elements with a single assay. However, with increasing interest in chromatin research, it is an imperative to have feasible, reliable assays that are compatible with a range of neuroscience study designs in both animals and humans. Here we tested three different protocols for neuronal chromatin accessibility analysis, including a varying brain tissue freezing method followed by fluorescent-activated nuclei sorting (FANS) and the ATAC-seq analysis. Our study shows that the cryopreservation method impacts the number of open chromatin regions that can be identified from frozen brain tissue using the cell-type specific ATAC-seq assay. However, we show that all three protocols generate consistent and robust data and enable the identification of functional regulatory elements, promoters and enhancers, in neuronal cells. Our study also implies that the broad biological interpretation of chromatin accessibility data is not significantly affected by the freezing condition. In comparison to the mouse brain analysis, we reveal the additional challenges of doing chromatin analysis on post mortem human brain tissue. However, we also show that these studies are revealing important cell type-specific information about gene regulation in the human brain. Overall, the ATAC-seq coupled with FANS is a powerful method to capture cell-type specific chromatin accessibility information in the mouse and human brain. Our study provides alternative brain preservation methods that generate high quality ATAC-seq data while fitting in different study designs, and further encourages the use of this method to uncover the role of epigenetic (dys)regulation in healthy and malfunctioning brain.

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