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Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin
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Abstract
Cell differentiation and function are regulated across multiple layers of gene regulation, including modulation of gene expression by changes in chromatin accessibility. However, differentiation is an asynchronous process precluding a temporal understanding of regulatory events leading to cell fate commitment. Here we developed simultaneous high-throughput ATAC and RNA expression with sequencing (SHARE-seq), a highly scalable approach for measurement of chromatin accessibility and gene expression in the same single cell, applicable to different tissues. Using 34,774 joint profiles from mouse skin, we develop a computational strategy to identify cis-regulatory interactions and define domains of regulatory chromatin (DORCs) that significantly overlap with super-enhancers. During lineage commitment, chromatin accessibility at DORCs precedes gene expression, suggesting that changes in chromatin accessibility may prime cells for lineage commitment. We computationally infer chromatin potential as a quantitative measure of chromatin lineage-priming and use it to predict cell fate outcomes. SHARE-seq is an extensible platform to study regulatory circuitry across diverse cells in tissues.
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“Milo may also be applicable to single-cell assays other than scRNA-seq, including multi-omic assays [35–39].”
From Differential abundance testing on single-cell data using k-nearest neighbor graphs (2022) · cites this paper as [39]
In the citing paper
“Increasing throughput, SHARE-seq was built based on SPLiT-seq and Paired-seq and uses several rounds of hybridization blocking to label mRNA and chromatin fragments in single cells60.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [60]
In the citing paper
“By contrast, paired experimental methods (10x Multiome, SHARE-seq60, DBiT-seq123, SNARE-seq57, CITE-seq68 and others) are easier to work with computationally as different modalities can be tethered to a single cell or a single spot; however, the sequencing capacity of each modality causes the overall sample size to decrease.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [60]
In the citing paper
“Although computational methods can integrate data from multiple omics10, they cannot readily uncover the mechanistic links between different omics layers.”
From Spatial epigenome–transcriptome co-profiling of mammalian tissues (2023) · cites this paper as [10]
In the citing paper
“which may indicate lineage priming10,27 of these genes in embryonic brain development.”
From Spatial epigenome–transcriptome co-profiling of mammalian tissues (2023) · cites this paper as [10]
In the citing paper
“suggesting lineage priming of these genes in expression10,27.”
From Spatial epigenome–transcriptome co-profiling of mammalian tissues (2023) · cites this paper as [10]
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Integrated analysis of multimodal single-cell data
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Metadata
- Authors
- Ma, S., Zhang, B., LaFave, L. M., Earl, A. S., Chiang, Z., Hu, Y., Ding, J., Brack, A., Kartha, V. K., Tay, T., Law, T., Lareau, C., Hsu, Y.-C., Regev, A., & Buenrostro, J. D.
- Affiliations
- Broad Institute (US)
- Harvard University (US)
- Corresponding author
- Regev, Aviv
- Buenrostro, Jason D.
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 183
- Issue
- 4
- Pages
- 1103-1116.e20
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC7669735
- OpenAlex
- W3093858607
- Cited by
- 1,146
- References
- 95
- Citations by year
- 2026: 223 · 2025: 224 · 2024: 215 · 2023: 183 · 2022: 175 · 2021: 107 · 2020: 15
- FWCI
- 39.26
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 376 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Cell (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-nd)
- Funders
- Chan Zuckerberg Initiative
- Klarman Cell Observatory, Broad Institute
- National Institutes of Health
- Richard and Susan Smith Family Foundation
- Allen Institute
- 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 multi-omics, cis-regulatory interactions, super-enhancers, cell fate prediction, lineage commitment, mouse skin, cell differentiation
- MeSH terms
- Animals, Cell Differentiation, Cell Line, Chromatin, Enhancer Elements, Genetic, Female, Gene Expression Regulation, Histones, Mice, Inbred C57BL, Protein Processing, Post-Translational, RNA, Cell Lineage, Gene Expression Profiling, Single-Cell Analysis
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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.
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