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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Such DNA variation data can be combined with chromatin accessibility and thus form a three-dimensional informative 'regulome' in the genome.
“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]
The Abstract links single-cell chromatin-accessibility variation to three-dimensional genome organization and calls this analysis informative about the “regulome.” The Main section describes correlations in accessibility across genomic regions. Neither passage describes combining DNA sequence-variation data with accessibility data, so that part of the claim is unsupported. (LLM, conf=0.87)
Experimental — accuracy not yet measuredTwo methods of scATAC-seq developed in 2015 enabled exploration of chromatin accessibility at the single-cell level.
“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]
The Main section says the authors developed scATAC-seq and used it to map DNA accessibility in individual cells. Extended Data Figure 1 describes development of that method, but the passages do not establish that this paper developed two scATAC-seq methods. (LLM, conf=0.88)
Experimental — accuracy not yet measuredNew methods have recently emerged that overcome many key limitations of earlier technologies, including single-cell methods, higher-resolution 3C, MPRAs, and high-throughput CRISPR screens.
“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]
The Main section describes scATAC-seq as a method for mapping open chromatin in individual cells and reports its use across several cell types. It presents partitioning cells by state and studying small or rare samples as future possibilities, rather than demonstrating identification of cell-type-specific open chromatin in complex tissues. The separately cited 3C, MPRA, and CRISPR methods are not attributed to this paper. (LLM, conf=0.84)
Experimental — accuracy not yet measuredSingle-cell ATAC-seq has enabled in vivo profiling of accessible chromatin at the scale of a whole organism.
“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]
The Main section (page unavailable) reports that scATAC-seq maps accessible chromatin in individual cells. Its experiments used captured cells from cell lines; the paper does not report in vivo profiling at the scale of a whole organism. (LLM, conf=0.88)
Experimental — accuracy not yet measuredOther single-cell methods have been developed to measure single-cell DNA methylation and single-cell open chromatin using ATAC-seq.
“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]
The scATAC-seq features used here were originally developed for non-perturbed cells, for purposes such as identifying cell types, discovering cell-type-specific regulatory elements, and reconstructing differentiation trajectories.
“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]
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.
“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]
There are two experimental methods to perform scATAC-seq: a microfluidic platform for physical isolation of single cells and combinatorial cellular indexing of sorted nuclei.
“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]
scATAC-seq 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 transcription factor motif dynamics.
“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]
scATAC-seq was able to capture distinct regulatory states and trajectories, leading to the finding of a continuous regulatory landscape that underlies human haematopoiesis.
“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]
Buenrostro et al. showed that chromatin accessibility variance in K562 myeloid leukaemia cells was associated with specific transcription factor-binding sites, including GATA1/2, JUN and STAT2.
“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]
Functionally related genomic elements, such as all the sites bound by the same transcription factor, would co-vary within the same cell, providing a higher-order aggregate signal that allows one to group cells by their shared inferred state, as well as to determine the extent of variation in the activity of the associated factors between cells.
“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]
Simultaneous analysis of protein and RNA or of epigenomic profiles and RNA can provide mechanistic explanations that relate variations in the state of regulator proteins or cis-regulatory loci to variation in target transcripts.
“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]
scATAC-seq measures genome-wide chromatin accessibility in individual cells.
“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]
Prior single-cell ATAC-seq methods either have low throughput, from hundreds to thousands of cells per experiment, or yield low-complexity data with thousands of fragments per cell.
“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]
dscATAC-seq reproduced known variation in transcription factor binding motif activity across single GM12878 cells.
“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]
Chromatin accessibility from bulk ATAC-seq and DNase-seq and the aggregate chromatin accessibility across different single-cell technologies were highly correlated.
“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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- 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 · 2014: 2
- 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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