The DOI matches its Crossref record; 68 of the 69 references it cites were found in a registry or an index.
A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility
Open accessAbstract
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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“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]
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In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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
In the citing paper
“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]
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Citations
Cites
Trimmomatic: a flexible trimmer for Illumina sequence data
Bolger, A. M., Lohse, M., & Usadel, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu170 · Cited by 65,548
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437
Model-based Analysis of ChIP-Seq (MACS)
Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-9-r137 · Cited by 18,386
MEME SUITE: tools for motif discovery and searching
Bailey, T. L., Boden, M., Buske, F. A., Frith, M., Grant, C. E., Clementi, L., Ren, J., Li, W. W., & Noble, W. S. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp335 · Cited by 10,648
deepTools2: a next generation web server for deep-sequencing data analysis
Ramírez, F., Ryan, D. P., Grüning, B., Bhardwaj, V., Kilpert, F., Richter, A. S., Heyne, S., Dündar, F., & Manke, T. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw257 · Cited by 8,096
Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314
Self-citationSpatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y., & Greenleaf, W. J. (2013) · Nature Methods · doi:10.1038/nmeth.2688 · Cited by 6,422
Research works printed but not linked: 2
- Cao, 2017, Comprehensive single-cell transcriptional profiling of a multicellular organism, Science, 357, 661, 10.1126/science.aam8940 · DOI · Paper page
- Krijthe, J.H. (2015). Rtsne: T-Distributed Stochastic Neighbor Embedding using Barnes-Hut Implementation, URL: https://github.com/jkrijthe/Rtsne. · DOI
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Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
Integrated analysis of multimodal single-cell data
Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zagar, M., et al. (2020) · doi:10.1101/2020.10.12.335331 · Cited by 15,508
Chromatin accessibility and the regulatory epigenome
Klemm, S. L., Shipony, Z., & Greenleaf, W. J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-018-0089-8 · Cited by 1,793
Single-cell chromatin state analysis with Signac
Stuart, T., Srivastava, A., Madad, S., Lareau, C. A., & Satija, R. (2021) · Nature Methods · doi:10.1038/s41592-021-01282-5 · Cited by 1,769
ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis
Granja, J. M., Corces, M. R., Pierce, S. E., Bagdatli, S. T., Choudhry, H., Chang, H. Y., & Greenleaf, W. J. (2021) · Nature Genetics · doi:10.1038/s41588-021-00790-6 · Cited by 1,392
Metadata
- 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
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