Joint reconstruction of cis-regulatory interaction networks across multiple tissues using single-cell chromatin accessibility data

Abstract The rapid accumulation of single-cell chromatin accessibility data offers a unique opportunity to investigate common and specific regulatory mechanisms across different cell types. However, existing methods for cis-regulatory network reconstruction using single-cell chromatin accessibility data were only designed for cells belonging to one cell type, and resulting networks may be incomparable directly due to diverse cell numbers of different cell types. Here, we adopt a computational method to jointly reconstruct cis-regulatory interaction maps (JRIM) of multiple cell populations based on patterns of co-accessibility in single-cell data. We applied JRIM to explore common and specific regulatory interactions across multiple tissues from single-cell ATAC-seq dataset containing ~80 000 cells across 13 mouse tissues. Reconstructed common interactions among 13 tissues indeed relate to basic biological functions, and individual cis-regulatory networks show strong tissue specificity and functional relevance. More importantly, tissue-specific regulatory interactions are mediated by coordination of histone modifications and tissue-related TFs, and many of them may reveal novel regulatory mechanisms.

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Joint reconstruction of cis-regulatory interaction networks across multiple tissues using single-cell chromatin accessibility data

10.1101/721290 ◽

2019 ◽

Author(s):

Kangning Dong ◽

Shihua Zhang

Keyword(s):

Single Cell ◽

Regulatory Network ◽

Cell Types ◽

Chromatin Accessibility ◽

Computational Method ◽

Regulatory Mechanisms ◽

Regulatory Interactions ◽

Mouse Tissues ◽

Functional Relevance ◽

Different Cell Types

ABSTRACTThe rapid accumulation of single-cell chromatin accessibility data offers a unique opportunity to investigate common and specific regulatory mechanisms across different cell types. However, existing methods for cis-regulatory network reconstruction using single-cell chromatin accessibility data were only designed for cells belonging to one cell type, and resulting networks may be incomparable directly due to diverse cell numbers of different cell types. Here, we adopt a computational method to jointly reconstruct cis-regulatory interaction maps (JRIM) of multiple cell populations based on patterns of co-accessibility in single-cell data. We applied JRIM to explore common and specific regulatory interactions across multiple tissues from single-cell ATAC-seq dataset containing ~80,000 cells across 13 mouse tissues. Reconstructed common interactions among 13 tissues indeed relate to basic biological functions, and individual cis-regulatory network shows strong tissue specificity and functional relevance. More importantly, tissue-specific regulatory interactions are mediated by coordination of histone modifications and tissue related TFs, and many of them reveal novel regulatory mechanisms (e.g., a kidney-specific promoter-enhancer loop of clock-controlled gene Gys2).

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Predicting gene regulatory networks from cell atlases

10.1101/2020.08.21.261735 ◽

2020 ◽

Author(s):

Andreas Fønss Møller ◽

Kedar Nath Natarajan

Keyword(s):

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Cell Types ◽

Mouse Cell ◽

Cell Type ◽

Integrated Network ◽

Mouse Tissues ◽

Cell Type Specific ◽

Gene Regulatory

AbstractRecent single-cell RNA-sequencing atlases have surveyed and identified major cell-types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from 3 major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences including sampled tissues, sequencing depth and author assigned cell-type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell-types from specialised cell-type specific regulons. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell-types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wildtype and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data.SummaryIntegrated single-cell gene regulatory network from three mouse cell atlases captures global and cell-type specific regulatory modules and crosstalk, important for cellular identity.

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Functional Inference of Gene Regulation using Single-Cell Multi-Omics

10.1101/2021.07.28.453784 ◽

2021 ◽

Author(s):

Vinay K Kartha ◽

Fabiana M Duarte ◽

Yan Hu ◽

Sai Ma ◽

Jennifer G Chew ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Regulatory Networks ◽

Immunological Response ◽

Cell Types ◽

Regulatory Elements ◽

Chromatin Accessibility ◽

Human Blood Cells ◽

Functional Inference ◽

Omic Data

Cells require coordinated control over gene expression when responding to environmental stimuli. Here, we apply scATAC-seq and scRNA-seq in resting and stimulated human blood cells. Collectively, we generate ~91,000 single-cell profiles, allowing us to probe the cis -regulatory landscape of immunological response across cell types, stimuli and time. Advancing tools to integrate multi-omic data, we develop FigR - a framework to computationally pair scATAC-seq with scRNA-seq cells, connect distal cis -regulatory elements to genes, and infer gene regulatory networks (GRNs) to identify candidate TF regulators. Utilizing these paired multi-omic data, we define Domains of Regulatory Chromatin (DORCs) of immune stimulation and find that cells alter chromatin accessibility prior to production of gene expression at time scales of minutes. Further, the construction of the stimulation GRN elucidates TF activity at disease-associated DORCs. Overall, FigR enables the elucidation of regulatory interactions across single-cell data, providing new opportunities to understand the function of cells within tissues.

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Single-Nucleus Chromatin Accessibility Landscape Reveals Diversity in Regulatory Regions Across Distinct Adult Rat Cortex

Frontiers in Molecular Neuroscience ◽

10.3389/fnmol.2021.651355 ◽

2021 ◽

Vol 14 ◽

Author(s):

Yeya Yu ◽

Xiaoyu Wei ◽

Qiuting Deng ◽

Qing Lan ◽

Yiping Guo ◽

...

Keyword(s):

Single Cell ◽

Cell Fate ◽

Regulatory Networks ◽

Chromatin Accessibility ◽

Autism Spectrum ◽

Regulatory Mechanisms ◽

Sprague Dawley ◽

Regulatory Regions ◽

Rat Cortex ◽

Single Nucleus

Rats have been widely used as an experimental organism in psychological, pharmacological, and behavioral studies by modeling human diseases such as neurological disorders. It is critical to identify and characterize cell fate determinants and their regulatory mechanisms in single-cell resolutions across rat brain regions. Here, we applied droplet-based single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq) to systematically profile the single-cell chromatin accessibility across four dissected brain areas in adult Sprague–Dawley (SD) rats with a total of 59,023 single nuclei and identified 16 distinct cell types. Interestingly, we found that different cortex regions exhibit diversity in both cellular compositions and gene regulatory regions. Several cell-type-specific transcription factors (TFs), including SPI1, KLF4, KLF6, and NEUROD2, have been shown to play important roles during the pathogenesis of various neurological diseases, such as Alzheimer’s disease (AD), astrocytic gliomas, autism spectrum disorder (ASD), and intellectual disabilities. Therefore, our single-nucleus atlas of rat cortex could serve as an invaluable resource for dissecting the regulatory mechanisms underlying diverse cortex cell fates and further revealing the regulatory networks of neuropathogenesis.

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Fully-automated cell-type identification with specific markers extracted from single-cell transcriptomic data

10.1101/812131 ◽

2019 ◽

Author(s):

Aleksandr Ianevski ◽

Anil K Giri ◽

Tero Aittokallio

Keyword(s):

Single Cell ◽

Cell Types ◽

Computational Method ◽

Marker Genes ◽

Cell Type ◽

Cell Clusters ◽

Mouse Tissues ◽

Single Cell Type ◽

The Individual ◽

Selection Of

AbstractSingle-cell transcriptomics enables systematic charting of cellular composition of complex tissues. Identification of cell populations often relies on unsupervised clustering of cells based on the similarity of the scRNA-seq profiles, followed by manual annotation of cell clusters using established marker genes. However, manual selection of marker genes for cell-type annotation is a laborious and error-prone task since the selected markers must be specific both to the individual cell clusters and various cell types. Here, we developed a computational method, termed ScType, which enables data-driven selection of marker genes based solely on given scRNA-seq data. Using a compendium of 7 scRNA-seq datasets from various human and mouse tissues, we demonstrate how ScType enables unbiased, accurate and fully-automated single-cell type annotation by guaranteeing the specificity of marker genes both across cell clusters and cell types. The widely-applicable method is implemented as an interactive web-tool (https://sctype.fimm.fi), connected with comprehensive database of specific markers.

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Predicting gene regulatory networks from cell atlases

Life Science Alliance ◽

10.26508/lsa.202000658 ◽

2020 ◽

Vol 3 (11) ◽

pp. e202000658 ◽

Cited By ~ 1

Author(s):

Andreas Fønss Møller ◽

Kedar Nath Natarajan

Keyword(s):

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Cell Types ◽

Mouse Cell ◽

Cell Type ◽

Integrated Network ◽

Mouse Tissues ◽

Multiple Cell ◽

Gene Regulatory

Recent single-cell RNA-sequencing atlases have surveyed and identified major cell types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from three major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences, including sampled tissues, sequencing depth, and author assigned cell type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell types from specialised cell type–specific regulons. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wild-type and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data.

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The accessible chromatin landscape of the hippocampus at single-cell resolution

10.1101/407668 ◽

2018 ◽

Cited By ~ 2

Author(s):

John R. Sinnamon ◽

Kristof A. Torkenczy ◽

Michael W. Linhoff ◽

Sarah Vitak ◽

Hannah A. Pliner ◽

...

Keyword(s):

Single Cell ◽

Hippocampal Neurons ◽

Regulatory Networks ◽

Neuronal Cell ◽

Cell Types ◽

Chromatin Accessibility ◽

Cell Type Specificity ◽

High Quality ◽

Rapid Processing ◽

Accessible Chromatin

ABSTRACTHere we present a comprehensive map of the accessible chromatin landscape of the mouse hippocampus at single-cell resolution. Substantial advances of this work include the optimization of single-cell combinatorial indexing assay for transposase accessible chromatin (sci-ATAC-seq), a software suite,scitools, for the rapid processing and visualization of single-cell combinatorial indexing datasets, and a valuable resource of hippocampal regulatory networks at single-cell resolution. We utilized sci-ATAC-seq to produce 2,346 high-quality single-cell chromatin accessibility maps with a mean unique read count per cell of 29,201 from both fresh and frozen hippocampi, observing little difference in accessibility patterns between the preparations. Using this dataset, we identified eight distinct major clusters of cells representing both neuronal and non-neuronal cell types and characterized the driving regulatory factors and differentially accessible loci that define each cluster. We then applied a recently described co-accessibility framework,Cicero, which identified 146,818 links between promoters and putative distal regulatory DNA. Identified co-accessibility networks showed cell-type specificity, shedding light on key dynamic loci that reconfigure to specify hippocampal cell lineages. Lastly, we carried out an additional sci-ATAC-seq preparation from cultured hippocampal neurons (899 high-quality cells, 43,532 mean unique reads) that revealed substantial alterations in their epigenetic landscape compared to nuclei from hippocampal tissue. This dataset and accompanying analysis tools provide a new resource that can guide subsequent studies of the hippocampus.

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

10.1101/2020.07.17.204792 ◽

2020 ◽

Author(s):

Michael W. Dorrity ◽

Cris Alexandre ◽

Morgan Hamm ◽

Anna-Lena Vigil ◽

Stanley Fields ◽

...

Keyword(s):

Single Cell ◽

Regulatory Networks ◽

Cell Types ◽

Analytical Framework ◽

Chromatin Accessibility ◽

Poor Correlation ◽

Cell Type ◽

Tissue Heterogeneity ◽

Shared Information ◽

Regulatory Landscape

AbstractIn plants, chromatin accessibility – the primary mark of regulatory DNA – is relatively static across tissues and conditions. This scarcity of accessible sites that are dynamic or tissue-specific may be due in part to tissue heterogeneity in previous bulk studies. To assess the effects of tissue heterogeneity, we apply single-cell ATAC-seq to A. thaliana roots and identify thousands of differentially accessible sites, sufficient to resolve all major cell types of the root. However, even this vast increase relative to bulk studies in the number of dynamic sites does not resolve the poor correlation at individual loci between accessibility and expression. Instead, we find that the entirety of a cell’s regulatory landscape and its transcriptome each capture cell type identity independently. We leverage this shared information on cell identity to integrate accessibility and transcriptome data in order to characterize developmental progression, endoreduplication and cell division in the root. We further use the combined data to characterize cell type-specific motif enrichments of large transcription factor families and to link the expression of individual family members to changing accessibility at specific loci, taking the first steps toward resolving the direct and indirect effects that shape gene expression. Our approach provides an analytical framework to infer the gene regulatory networks that execute plant development.

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ZetaSuite, A Computational Method for Analyzing Multi-dimensional High-throughput Data, Reveals Genes with Opposite Roles in Cancer Dependency

10.1101/2021.08.29.458095 ◽

2021 ◽

Author(s):

Yajing Hao ◽

Changwei Shao ◽

Guofeng Zhao ◽

Xiang-Dong Fu

Keyword(s):

High Throughput ◽

Regulatory Networks ◽

Cell Types ◽

Computational Method ◽

Two Dimensional ◽

Experimental Conditions ◽

Term Survival ◽

High Throughput Data ◽

Genome Wide ◽

Different Cell Types

AbstractThe rapid advance of high-throughput technologies has enabled the generation of two-dimensional or even multi-dimensional high-throughput data, e.g., genome-wide siRNA screen (1st dimension) for multiple changes in gene expression (2nd dimension) in many different cell types or tissues or under different experimental conditions (3rd dimension). We show that the simple Z-based statistic and derivatives are no longer suitable for analyzing such data because of the accumulation of experimental noise and/or off-target effects. Here, we introduce ZetaSuite, a statistical package designed to score and rank hits from two-dimensional screens, construct regulatory networks based on response similarities, and eliminate off-targets. Applying this method to two large cancer dependency screen datasets, we identify not only genes critical for cell fitness, but also those required for constraining cell proliferation. Strikingly, most of those cancer constraining genes function in DNA replication/repair checkpoint, suggesting that cancer cells also need to protect their genomes for long-term survival.

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High-throughput single-cell chromatin accessibility CRISPR screens enable unbiased identification of regulatory networks in cancer

Nature Communications ◽

10.1038/s41467-021-23213-w ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Sarah E. Pierce ◽

Jeffrey M. Granja ◽

William J. Greenleaf

Keyword(s):

Transcription Factor ◽

Single Cell ◽

Cancer Cells ◽

High Throughput ◽

Regulatory Networks ◽

Temporal Dynamics ◽

Chromatin Accessibility ◽

Regulatory Regions ◽

Factor Binding ◽

Genome Wide

AbstractChromatin accessibility profiling can identify putative regulatory regions genome wide; however, pooled single-cell methods for assessing the effects of regulatory perturbations on accessibility are limited. Here, we report a modified droplet-based single-cell ATAC-seq protocol for perturbing and evaluating dynamic single-cell epigenetic states. This method (Spear-ATAC) enables simultaneous read-out of chromatin accessibility profiles and integrated sgRNA spacer sequences from thousands of individual cells at once. Spear-ATAC profiling of 104,592 cells representing 414 sgRNA knock-down populations reveals the temporal dynamics of epigenetic responses to regulatory perturbations in cancer cells and the associations between transcription factor binding profiles.

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