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Single-cell chromatin accessibility reveals principles of regulatory variation
Open accessAbstract
Cell-to-cell variation is a universal feature of life that affects a wide range of biological phenomena, from developmental plasticity to tumour heterogeneity. Although recent advances have improved our ability to document cellular phenotypic variation, the fundamental mechanisms that generate variability from identical DNA sequences remain elusive. Here we reveal the landscape and principles of mammalian DNA regulatory variation by developing a robust method for mapping the accessible genome of individual cells by assay for transposase-accessible chromatin using sequencing (ATAC-seq) integrated into a programmable microfluidics platform. Single-cell ATAC-seq (scATAC-seq) maps from hundreds of single cells in aggregate closely resemble accessibility profiles from tens of millions of cells and provide insights into cell-to-cell variation. Accessibility variance is systematically associated with specific trans-factors and cis-elements, and we discover combinations of trans-factors associated with either induction or suppression of cell-to-cell variability. We further identify sets of trans-factors associated with cell-type-specific accessibility variance across eight cell types. Targeted perturbations of cell cycle or transcription factor signalling evoke stimulus-specific changes in this observed variability. The pattern of accessibility variation in cis across the genome recapitulates chromosome compartments de novo, linking single-cell accessibility variation to three-dimensional genome organization. Single-cell analysis of DNA accessibility provides new insight into cellular variation of the 'regulome'.
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In the citing paper
“Such DNA variation data can be combined with chromatin accessibility and thus form a three-dimensional informative “regulome” in the genome (312).”
From Epigenetic Modifications in Stress Response Genes Associated With Childhood Trauma (2019) · cites this paper as [312]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“These studies revealed regulatory heterogeneity within immunophenotypically sorted cellular populations that are governed by diverse regulatory programmes at individual cis-regulatory elements and by relatively simple TF motif dynamics.”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [58]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Two methods of scATAC-seq were developed in 2015, which enabled the exploration of chromatin accessibility at the single-cell level.”
From Single-cell sequencing to multi-omics: technologies and applications (2024) · cites this paper as [111]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“To this point, new methods have recently emerged that overcome many of the key limitations of earlier technologies. These include single-cell (‘sc’) methods to identify cell-type-specific open chromatin in complex tissues82,83; higher resolution chromosome conformation capture [G] methods to more finely map enhancer–promoter contacts84; MPRAs to dissect or trap enhancer activity on a reporter”
From Towards a comprehensive catalogue of validated and target-linked human enhancers (2020) · cites this paper as [83]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“For example, single-cell ATAC-seq has enabled the in vivo profiling of accessible chromatin at the scale of a whole organism82,83.160,161.”
From Towards a comprehensive catalogue of validated and target-linked human enhancers (2020) · cites this paper as [83]
Experimental — accuracy not yet measured
In the citing paper
“Other single-cell methods have been developed to measure single-cell DNA methylation [182] and single-cell open chromatin using ATAC-seq [183, 184].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [183]
In the citing paper
“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 [43]
In the citing paper
“the microfluidics approach of Buenrostro et al. can use a commercially available Tn5 transposase, whereas combinatorial indexing requires a self-made Tn5 transposase tagged with different barcodes.”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [58]
In the citing paper
“There are two experimental methods to perform scATAC-seq, either based on a microfluidic platform (Fluidigm C1) for physical isolation of single cells [58] or on combinatorial cellular indexing of sorted nuclei (sciATAC-seq) [59].”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [58]
In the citing paper
“Interestingly, scATAC-seq was able to capture the distinct regulatory states and trajectories, leading to the finding of a continuous regulatory landscape that underlies human haematopoiesis [58].”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [58]
In the citing paper
“Buenrostro et al. [58] showed that chromatin accessibility variance in K562 myeloid leukaemia cells was associated with specific TF-binding sites (including GATA1/2, JUN and STAT2).”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [58]
In the citing paper
“To overcome this intrinsic sparseness, several studies26,28 have reasoned that functionally related genomic elements, such as all the sites bound by the same transcription factor, would co-vary within the same cell (analogous to the observation above on transcript co-regulation to infer circuits), providing a higher-order aggregate signal that allows one to group cells by their shared inferred”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [28]
In the citing paper
“Similarly, simultaneous analysis of protein and RNA or of epigenomic profiles and RNA can provide mechanistic explanations that relate variations in the state of regulator proteins3 or cis-regulatory loci27,28 to variation in target transcripts.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [28]
In the citing paper
“single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) measures genome-wide chromatin accessibility in individual cells133,134”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [134]
In the citing paper
“However, these current methods for single-cell ATAC-seq (scATAC-seq)18,19 either remain relatively low throughput (hundreds to thousands of cells per experiment) or provide low-complexity data (thousands of fragments per cell).”
From Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility (2019) · cites this paper as [18]
In the citing paper
“Notably, dscATAC-seq recapitulated known variation in the activity of TF binding motifs across single GM12878 cells, as previously reported18,26 (Supplementary Fig. 3d).”
From Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility (2019) · cites this paper as [18]
In the citing paper
“We found that chromatin accessibility from bulk ATAC-seq8 and DNase-seq2 and the aggregate chromatin accessibility across the different single-cell technologies18,19,24,25 were highly correlated (Fig. 1c,d).”
From Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility (2019) · cites this paper as [18]
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Metadata
- Authors
- Buenrostro, J. D., Wu, B., Litzenburger, U. M., Ruff, D., Gonzales, M. L., Snyder, M. P., Chang, H. Y., & Greenleaf, W. J.
- Affiliations
- Howard Hughes Medical Institute (US)
- Stanford Medicine (US)
- Standard BioTools (United States) (US)
- Corresponding author
- Chang, Howard Y.
- Greenleaf, William J.
- ORCID
- Venue
- Nature
- Published
- 2015-06-17
- Volume
- 523
- Issue
- 7561
- Pages
- 486-490
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC4685948
- OpenAlex
- W1602702667
- Cited by
- 2,308
- References
- 31
- Citations by year
- 2026: 206 · 2025: 258 · 2024: 240 · 2023: 262 · 2022: 251 · 2021: 298 · 2020: 223 · 2019: 186 · 2018: 157 · 2017: 124 · 2016: 75 · 2015: 20
- FWCI
- 54.59
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- National Human Genome Research Institute (P50HG007735, U54HG004558, 5U54HG00455805, T32HG000044)
- National Institute of Allergy and Infectious Diseases (U19AI057266)
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (UH2AR067676)
- 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
- single-cell chromatin accessibility, single-cell ATAC-seq, cell-to-cell variability, transcription factors, cis-regulatory elements, 3D genome organization, regulome, microfluidics, cell cycle
- MeSH terms
- Animals, Cell Compartmentation, Cell Cycle, Cell Line, Cells, Chromatin, DNA, Humans, Transcription Factors, Signal Transduction, Genome, Human, Transposases, Microfluidics, Epigenesis, Genetic, Epigenomics, Single-Cell Analysis
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