The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin
Open accessPublished version of a preprint
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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Additional file 1 of Bi-order multimodal integration of single-cell data
Dou, J., Liang, S., Mohanty, V., Miao, Q., Huang, Y., Liang, Q., Cheng, X., Kim, S., Choi, J., Li, Y., et al. (2022) · Figshare · doi:10.6084/m9.figshare.19738253
Additional file 6 of SHARE-Topic: Bayesian interpretable modeling of single-cell multi-omic data
Kazwini, N. E., & Sanguinetti, G. (2024) · Figshare · doi:10.6084/m9.figshare.26684847
Thibodeau, A., Eroğlu, A., McGinnis, C. S., Lawlor, N. T., Nehar-Belaid, D., Kursawe, R., Marcheş, R., Conrad, D. N., Kuchel, G. A., Gartner, Z. J., et al. (2021) · Figshare · doi:10.6084/m9.figshare.16551022
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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