A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility
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We applied a combinatorial indexing assay, sci-ATAC-seq, to profile genome-wide chromatin accessibility in ∼100,000 single cells from 13 adult mouse tissues. We identify 85 distinct patterns of chromatin accessibility, most of which can be assigned to cell types, and ∼400,000 differentially accessible elements. We use these data to link regulatory elements to their target genes, to define the transcription factor grammar specifying each cell type, and to discover in vivo correlates of heterogeneity in accessibility within cell types. We develop a technique for mapping single cell gene expression data to single-cell chromatin accessibility data, facilitating the comparison of atlases. By intersecting mouse chromatin accessibility with human genome-wide association summary statistics, we identify cell-type-specific enrichments of the heritability signal for hundreds of complex traits. These data define the in vivo landscape of the regulatory genome for common mammalian cell types at single-cell resolution.
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Single-cell ATAC-seq has enabled in vivo profiling of accessible chromatin at the scale of a whole organism.
“For example, single-cell ATAC-seq has enabled the in vivo profiling of accessible chromatin at the scale of a whole organism82,83.160,161.”
From Towards a comprehensive catalogue of validated and target-linked human enhancers (2020) · cites this paper as [160]
CellOracle has assembled a base GRN from a mouse scATAC-seq atlas to support GRN inference without requiring sample-specific scATAC-seq datasets.
“To support GRN inference without requiring sample-specific scATAC-seq datasets, we have assembled a base GRN from a mouse scATAC-seq atlas13.”
From Dissecting cell identity via network inference and in silico gene perturbation (2023) · cites this paper as [13]
Single-cell DNA methylation and chromatin accessibility studies have begun to reveal cell-type-specific genome-wide epigenetic landscapes and gene regulatory networks in the brain.
“Similarly, single-cell DNA methylation and chromatin accessibility studies have begun to reveal cell-type-specific genome-wide epigenetic landscapes and gene regulatory networks in the brain12–15.”
From A multimodal cell census and atlas of the mammalian primary motor cortex (2021) · cites this paper as [15]
Many types of features have been used to quantify snATAC data, including gene promoters, putative enhancers, or combinations of regions sharing common DNA sequence motifs.
“Many types of features have been used to quantify snATAC data, including gene promoters, putative enhancers, or combinations of regions sharing common DNA sequence motifs (Bravo González-Blas et al., 2019; Cusanovich et al., 2018; Fang et al., 2019; Schep et al., 2017).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Cusanovich et al., 2018
A largely consistent pattern was observed when comparing the clustering with latent semantic index clustering of the dscATAC-seq data.
“and we observed a largely consistent pattern when compared to dimensionality reduction and clustering using the latent semantic index (LSI) of our dscATAC-seq data as has been previously performed27,28 (Supplementary Fig. 4e).”
From Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility (2019) · cites this paper as [27]
Reanalysis of published sciATAC-seq data from two mouse brains identified 13 clusters using the same computational approach.
“For comparison with previous techniques, we also analyzed published sciATAC-seq data from two mouse brains27, where we identified 13 clusters using the same computational approach (Supplementary Fig. 4f).”
From Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility (2019) · cites this paper as [27]
New technologies can quantify DNA methylation, chromatin accessibility, and in situ expression across thousands to millions of cells.
“new technologies can now measure DNA methylation (Luo et al., 2017; Mulqueen et al., 2018), chromatin accessibility (Cusanovich et al., 2018), and in situ expression (Coskun and Cai, 2016; Moffitt and Zhuang, 2016; Wang et al., 2018), in thousands to millions of cells.”
From Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity (2019) · cites this paper as Cusanovich et al., 2018
Cellular heterogeneity in the human brain poses significant challenges for unraveling the gene regulatory architecture because the brain comprises heterogeneous cell populations with distinct gene expression and chromatin accessibility profiles.
“However, cellular heterogeneity poses significant challenges for unraveling the complexity in the gene regulatory architecture of the human brain, as it is comprised of heterogeneous cell populations, mostly different types of neurons and glia, each of which display distinct gene expression3,8–10 and chromatin accessibility profiles11–14.”
From Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders (2021) · cites this paper as [12]
Integrating ncRNA expression measurements with scATAC-seq data enabled mapping cell type specificity of small ncRNA expressed in the adult mouse brain.
“integrating our ncRNA expression measurements with the scATAC-seq data (28) enabled us to map cell type specificity of small ncRNA expressed in the adult mouse brain.”
From A mouse tissue atlas of small noncoding RNA (2020) · cites this paper as [28]
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WashU Epigenome Browser update 2022
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The evolving concept of cell identity in the single cell era
Morris, S. A. (2019) · Development · doi:10.1242/dev.169748 · Cited by 154 · see citing works
Cell lineage and communication network inference via optimization for single-cell transcriptomics
Wang, S., Karikomi, M. K., MacLean, A. L., & Nie, Q. H. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz204 · Cited by 153 · see citing works
Single-cell ATAC sequencing analysis: From data preprocessing to hypothesis generation
Baek, S., & Lee, I. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.06.012 · Cited by 151 · see citing works
High-throughput single-сell sequencing in cancer research
Jia, Q., Chu, H., Jin, Z., Long, H., & Zhu, B. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-00990-4 · Cited by 152 · see citing works
scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics
Song, D., Wang, Q., Yan, G., Liu, T., Sun, T., & Li, J. J. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01772-1 · Cited by 148 · see citing works
Beytebiere, J. R., Trott, A. J., Greenwell, B. J., Osborne, C. A., Vitet, H., Spence, J., Yoo, S. H., Chen, Z., Takahashi, J. S., Ghaffari, N., et al. (2019) · Genes & Development · doi:10.1101/gad.322198.118 · Cited by 151 · see citing works
Recent advances in single-cell sequencing technologies
Wen, L., & Tang, F. (2022) · Precision Clinical Medicine · doi:10.1093/pcmedi/pbac002 · Cited by 127 · see citing works
EpiScanpy: integrated single-cell epigenomic analysis
Danese, A., Richter, M. L., Chaichoompu, K., Fischer, D. S., Theis, F. J., & Colomé‐Tatché, M. (2021) · Nature Communications · doi:10.1038/s41467-021-25131-3 · Cited by 131 · see citing works
- Authors
- Cusanovich, D. A., Hill, A. J., Aghamirzaie, D., Daza, R. M., Pliner, H. A., Berletch, J. B., Filippova, G. N., Huang, X., Christiansen, L., DeWitt, W. S., Lee, C., Regalado, S. G., Read, D. F., Steemers, F. J., Disteche, C. M., Trapnell, C., & Shendure, J.
- Affiliations
- University of Washington (US)
- Illumina (United States) (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Trapnell, Cole
- Shendure, Jay
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 174
- Issue
- 5
- Pages
- 1309-1324.e18
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC6158300
- OpenAlex
- W2887910040
- Cited by
- 759
- References
- 69
- Citations by year
- 2026: 51 · 2025: 73 · 2024: 103 · 2023: 95 · 2022: 116 · 2021: 131 · 2020: 106 · 2019: 70 · 2018: 14
- FWCI
- 29.17
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- W. M. Keck Foundation
- National Human Genome Research Institute (5T32HG000035-23)
- National Heart, Lung, and Blood Institute (T32HL007828)
- National Institutes of Health (R01HG006283, DP2HD088158, R01GM046883, DP1HG007811)
- National Science Foundation
- Alfred P. Sloan Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Keywords
- chromatin accessibility, single-cell chromatin accessibility, combinatorial indexing, cell types, regulatory elements, cellular heterogeneity, genome-wide association summary statistics, mouse tissues, regulatory genome
- MeSH terms
- Animals, Chromatin, Gene Expression Regulation, Humans, Male, Mammals, Mice, Inbred C57BL, Transcription Factors, Genome, Human, Cluster Analysis, Epigenesis, Genetic, Mice, Genome-Wide Association Study, Epigenomics, Single-Cell Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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