The DOI matches its Crossref record; 87 of the 87 references it cites were found in a registry or an index.
Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion
Open accessPublished version of a preprint
Abstract
Understanding complex tissues requires single-cell deconstruction of gene regulation with precision and scale. Here, we assess the performance of a massively parallel droplet-based method for mapping transposase-accessible chromatin in single cells using sequencing (scATAC-seq). We apply scATAC-seq to obtain chromatin profiles of more than 200,000 single cells in human blood and basal cell carcinoma. In blood, application of scATAC-seq enables marker-free identification of cell type-specific cis- and trans-regulatory elements, mapping of disease-associated enhancer activity and reconstruction of trajectories of cellular differentiation. In basal cell carcinoma, application of scATAC-seq reveals regulatory networks in malignant, stromal and immune cells in the tumor microenvironment. Analysis of scATAC-seq profiles from serial tumor biopsies before and after programmed cell death protein 1 blockade identifies chromatin regulators of therapy-responsive T cell subsets and reveals a shared regulatory program that governs intratumoral CD8+ T cell exhaustion and CD4+ T follicular helper cell development. We anticipate that scATAC-seq will enable the unbiased discovery of gene regulatory factors across diverse biological systems.
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“Note that these features were originally developed for scATAC-seq data on non-perturbed cells, with goals such as the identification of cell type, discovery of cell type-specific regulatory elements, or reconstruction of cellular differentiation trajectories43,61.”
From scPerturb: harmonized single-cell perturbation data (2024) · cites this paper as [61]
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“Cells were filtered, keeping those with a TSS enrichment of at least five and more than 1,000 unique fragments.”
From Heme induces innate immune memory (2019) · cites this paper as [58]
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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
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353
Self-citationHighly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
Spatial 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
Cancer immunotherapy using checkpoint blockade
Ribas, A., & Wolchok, J. D. (2018) · Science · doi:10.1126/science.aar4060 · Cited by 6,791
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
Self-citationIntegrative analysis of 111 reference human epigenomes
Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368 · cited 9×
“We identified 31 scATAC-seq clusters, which we classified using three parallel approaches: (1) chromatin accessibility of individual cis-elements (ATAC-seq peaks), (2) gene activity scores, which were computed from the accessibility of several enhancers linked to a single gene promoter20, and (3) TF activity, as compu…”
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Dimensionality reduction for visualizing single-cell data using UMAP
Becht, E., McInnes, L., Healy, J., Dutertre, C.-A., Kwok, I. W. H., Ng, L. G., Ginhoux, F., & Newell, E. W. (2019) · Nature Biotechnology · doi:10.1038/nbt.4314 · Cited by 5,358
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Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., Rotem, A., Rodman, C., Lian, C., Murphy, G., et al. (2016) · Science · doi:10.1126/science.aad0501 · Cited by 4,944
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Self-citationArchR is a scalable software package for integrative single-cell chromatin accessibility analysis
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Metadata
- Authors
- Satpathy, A. T., Granja, J. M., Yost, K. E., Qi, Y., Meschi, F., McDermott, G. P., Olsen, B. N., Mumbach, M. R., Pierce, S. E., Corces, M. R., Shah, P., Bell, J. C., Jhutty, D., Nemec, C. M., Wang, J., Wang, L., Yin, Y., Giresi, P. G., Chang, A. L. S., Zheng, G. X. Y., Greenleaf, W. J., & Chang, H. Y.
- Affiliations
- Stanford Medicine (US)
- 10X Genomics (United States) (US)
- Chan Zuckerberg Biohub San Francisco (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Zheng, Grace X. Y.
- Greenleaf, William J.
- Chang, Howard Y.
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2019-08
- Volume
- 37
- Issue
- 8
- Pages
- 925-936
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC7299161
- OpenAlex
- W2965985232
- Cited by
- 980
- References
- 86
- Citations by year
- 2026: 121 · 2025: 170 · 2024: 145 · 2023: 162 · 2022: 137 · 2021: 157 · 2020: 75 · 2019: 12
- FWCI
- 33.66
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Nature Biotechnology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | Office of Extramural Research, National Institutes of Health (K08CA23188-01, P50HG007735)
- Parker Foundation
- Howard Hughes Medical Institute
- 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 › Chromatin Remodeling and Cancer
- Keywords
- single-cell ATAC-seq, immune cell development, basal cell carcinoma, tumor microenvironment, T cell exhaustion, CD8+ T cells, enhancer activity, anti-PD-1 therapy, gene regulatory networks, next-generation sequencing
- MeSH terms
- Bone Marrow Cells, Cell Line, Chromatin, Computer Simulation, Gene Expression Regulation, Hematopoiesis, Humans, Leukocytes, Mononuclear, T-Lymphocytes, Transcription Factors, Single-Cell Analysis, High-Throughput Nucleotide Sequencing
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