The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Joint profiling of chromatin accessibility and gene expression in thousands of single cells
Open accessAbstract
Single-cell chromatin and RNA analysis: Single-cell analyses have begun to provide insight into the differences among and within the individual cells that make up a tissue or organism. However, technological barriers owing to the small amount of material present in each single cell have prevented parallel analyses. Cao et al. present sci-CAR, a pooled barcode method that jointly analyzes both the RNA transcripts and chromatin profiles of single cells. By applying sci-CAR to lung adenocarcinoma cells and mouse kidney tissue, the authors demonstrate precision in assessing expression and genome accessibility at a genome-wide scale. The approach provides an improvement over bulk analysis, which can be confounded by differing cellular subgroups. Science , this issue p. 1380
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In the citing paper
“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 [36]
In the citing paper
“Furthermore, multimodal single-cell approaches are swiftly gaining traction within hepatology, allowing ‘combination readouts’ from the same cell, for example simultaneous acquisition of transcriptomic and epigenomic information, again maximising our ability to identify the key therapeutic targets in pathological cells.165,166”
From Understanding the cellular interactome of non-alcoholic fatty liver disease (2022) · cites this paper as [165]
In the citing paper
“Novel therapeutics developed for one fibrotic disorder may be applicable to a wide range of fibrotic diseases due to shared pathways across organs that are uncovered by this work.”
From Fibrosis: from mechanisms to medicines (2020) · cites this paper as [182]
In the citing paper
“The cardinal identity of a cell is probably already instilled at the postmitotic stage during development9,61,62,63,64, and its subsequent phenotypic differentiation and expression are regulated by extrinsic signals such as cell interactions and neural activity.”
From The diversity of GABAergic neurons and neural communication elements (2019) · cites this paper as [62]
In the citing paper
“To achieve higher throughput in single-cell analysis, Sci-CAR-seq combined single-cell combinatorial indexing55 with ATAC-seq to provide deep insights into chromatin states jointly with gene expression in over 11,200 nuclei56.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [56]
In the citing paper
“Single-cell multiomics allows uncovering of the mechanisms of gene regulation across different omics layers6–9 but lacks spatial information, which is crucial to understanding cellular function in tissue.”
From Spatial epigenome–transcriptome co-profiling of mammalian tissues (2023) · cites this paper as [8]
In the citing paper
“Single-cell measurements of transcriptional states, chromatin accessibility, and DNA methylation, have also provided a better understanding of the heterogeneity that exists in such regulatory mechanisms during hematopoiesis (Buenrostro et al., 2018; Cao et al., 2018; Ludwig et al., 2019b; Ulirsch et al., 2019).”
From Unraveling Hematopoiesis through the Lens of Genomics (2020) · cites this paper as Cao et al., 2018
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Additional file 3 of SHARE-Topic: Bayesian interpretable modeling of single-cell multi-omic data
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Additional file 5 of SHARE-Topic: Bayesian interpretable modeling of single-cell multi-omic data
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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
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Additional file 4 of CMOT: Cross-Modality Optimal Transport for multimodal inference
Alatkar, S. A., & Wang, D. (2024) · Figshare · doi:10.6084/m9.figshare.26603563
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Zhang, H., Mulqueen, R. M., Iannuzo, N., Farrera, D. O., Polverino, F., Galligan, J. J., Ledford, J. G., Adey, A., & Cusanovich, D. A. (2024) · Figshare · doi:10.6084/m9.figshare.26696670
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Additional file 2 of Bi-order multimodal integration of single-cell data
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Additional file 2 of CMOT: Cross-Modality Optimal Transport for multimodal inference
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Self-citation
Metadata
- Authors
- Cao, J., Cusanovich, D. A., Ramani, V., Aghamirzaie, D., Pliner, H. A., Hill, A. J., Daza, R. M., McFaline-Figueroa, J. L., Packer, J. S., Christiansen, L., Steemers, F. J., Adey, A. C., Trapnell, C., & Shendure, J.
- Affiliations
- University of Washington (US)
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Illumina (United States) (US)
- Oregon Health & Science University (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Cusanovich, Darren A.
- Ramani, Vijay
- Trapnell, Cole
- Shendure, Jay
- ORCID
- Venue
- Science
- Published
- 2018-09-28
- Volume
- 361
- Issue
- 6409
- Pages
- 1380-1385
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC6571013
- OpenAlex
- W2889236955
- Cited by
- 938
- References
- 42
- Citations by year
- 2026: 102 · 2025: 109 · 2024: 113 · 2023: 132 · 2022: 125 · 2021: 156 · 2020: 117 · 2019: 74 · 2018: 9
- FWCI
- 31.93
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation (DGE-1256082)
- National Institutes of Health (DP1HG007811, R01HG006283, DP2 HD088158, R35GM124704)
- Paul G. Allen Frontiers Foundation
- Howard Hughes Medical Institute
- W. M. Keck Foundation
- Dale. F. Frey Award for Breakthrough Scientists
- Alfred P. Sloan Foundation Research Fellowship
- National Heart, Lung, and Blood Institute (T32HL007828)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- chromatin accessibility, single-cell transcriptomics, combinatorial indexing, dexamethasone treatment, mouse kidney, cis-regulatory elements, gene expression regulation, single-cell multi-omics
- MeSH terms
- A549 Cells, Animals, Chromatin, Dexamethasone, Gene Expression Regulation, Humans, Kidney, Transcription, Genetic, Gene Expression Profiling, Genomics, NIH 3T3 Cells, Regulatory Elements, Transcriptional, Mice, HEK293 Cells, Single-Cell Analysis
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