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
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
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
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
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
HTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927
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
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
Flagged first, then by citations
Research works printed but not linked: 3
- 10.1126/science.aam8940 · DOI · Paper page
- 10.1016/j.cell.2018.02.001 · DOI · Paper page
- 10.18637/jss.v033.i01 · DOI · Paper page
Cited by
Sorted by citations
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
The single-cell transcriptional landscape of mammalian organogenesis
Cao, J., Spielmann, M., Qiu, X., Huang, X., Ibrahim, D. M., Hill, A. J., Zhang, F., Mundlos, S., Christiansen, L., Steemers, F. J., et al. (2019) · Nature · doi:10.1038/s41586-019-0969-x · Cited by 4,490
Self-citationFibrosis: from mechanisms to medicines
Henderson, N. C., Rieder, F., & Wynn, T. A. (2020) · Nature · doi:10.1038/s41586-020-2938-9 · Cited by 1,868
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
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
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 ›
Are you an author, or do you see a problem with this page? Request a review
