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Single-cell chromatin accessibility reveals principles of regulatory variation


Cell-to-cell variation is a universal feature of life that affects a wide range of biological phenomena, from developmental plasticity1,2 to tumour heterogeneity3. Although recent advances have improved our ability to document cellular phenotypic variation4,5,6,7,8, 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)9 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 compartments10 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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Figure 1: Single-cell ATAC-seq provides an accurate measure of chromatin accessibility genome-wide.
Figure 2: Trans-factors are associated with single-cell epigenomic variability.
Figure 3: Cell-type-specific epigenomic variability.
Figure 4: Structured cis-variability across single epigenomes.

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Gene Expression Omnibus

Data deposits

All data has been deposited in GEO under the accession number GSE65360. Fluidigm C1 scripts for performing scATAC-seq are available at


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This work was supported by National Institutes of Health (NIH) P50HG007735 (to H.Y.C. and W.J.G.), UH2 AR067676 and Lifespan Extension Foundation (H.Y.C.), U19AI057266 (to W.J.G.) and the Rita Allen Foundation (to W.J.G.) and the Baxter Foundation Faculty Scholar Grant (to W.J.G); H.Y.C. is an Early Career Scientist of the Howard Hughes Medical Institute. J.D.B. acknowledges support from the National Science Foundation Graduate Research Fellowships and NIH training grant T32HG000044 for support. M.P.S. acknowledges the NIH and the National Human Genome Research Institute (NHGRI) for funding through 5U54HG00455805. We thank members of Greenleaf and Chang laboratories, as well as the Fluidigm team, including L. Xi for discussions. We acknowledge the S. Kim laboratory for assistance with FACS sorting and the C. Bustamante laboratory for help with sequencing. We also thank R. Nichols, C. Mazumdar, V. Sebastiano and V. Risca for cells.

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Authors and Affiliations



J.D.B., H.Y.C. and W.J.G. conceived of the method. J.D.B., B.W., M.G. and D.R. developed the Fluidigm C1 microfluidic protocols. B.W. performed all scATAC-seq experiments with supervision from J.D.B. U.M.L. conducted the flow analysis, immunostains and drug treatments. J.D.B. developed and implemented the analysis infrastructure with input from W.J.G. All authors interpreted the data and wrote the manuscript. W.J.G. and H.Y.C. supervised all aspects of this work.

Corresponding authors

Correspondence to Howard Y. Chang or William J. Greenleaf.

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Competing interests

Stanford University has filed a provisional patent application on the methods described, and J.D.B., H.Y.C. and W.J.G. are named as inventors. D.R. and M.L.G. declare competing financial interests as employees of Fluidigm Corp.

Extended data figures and tables

Extended Data Figure 1 Methods development for assaying single epigenomes.

a, scATAC-seq workflow for steps performed both on and off the integrated fluidic chip (IFC). b, c, The development of an efficient Tn5 release protocol designed to permit downstream enzymatic reactions without DNA purification. b, An in vitro electrophoretic mobility gel shift assay using a fluorescently labelled PCR product (lane 1), showing a stable Tn5-DNA complex (lane 2) dissociated with 50 mM EDTA (lane 3) or 0.1% SDS (lane 4). c, Workflow and associated table of conditions used to optimize release protocol, showing conditions that markedly improve fragment yield over no release conditions or purifying DNA. Fragments released represents the fold gain in library diversity, as measured by quantitative PCR (qPCR). d, qPCR fluorescence traces of 96 libraries generated using scATAC-seq. For all subsequent libraries we used a total of 14 PCR cycles (dotted line). e, f, A bar plot of per-cell library sequencing depth (e) and fraction of duplicate reads (f), showing each library was sequenced to varying depths to a similar fraction of duplicate reads.

Extended Data Figure 2 scATAC-seq data recapitulate bulk ATAC-seq characteristics.

a, Fragments observed in open chromatin peaks identified from aggregate scATAC-seq data (n = 384 libraries) are highly correlated with reads observed from bulk ATAC-seq in GM12878 cells. b, Histogram of aggregated read starts around all transcription start sites (TSS) (in K562 cells) comparing ensemble approaches, including 500 cell ATAC-seq reported in a previous publication, to scATAC-seq shows high enrichment above background level of reads. c, DNA fragment size distribution of ATAC-seq fragments from single cells (grey) and the average of all single cells (red) display characteristic nucleosome-associated periodicity. d, Phase-contrast (left) and epifluorescence images (right) of captured cell no. 4 displaying characteristic live cell stain (Calcein) and exclusion of ethidium bromide. e, Histogram of read starts around TSSs for cell no. 4 shows high enrichment. f, DNA fragment size distribution for cell no. 4 showing nucleosomal periodicity. g, Images similar to d showing staining of cell no. 83, suggesting low viability due to ethidium bromide staining. h, Histogram of read starts around transcription start sites shows lower enrichment than cell no. 4. i, DNA fragment size distribution for cell no. 83. j, Images similar to d showing staining of cell no. 33 suggesting viability. k, Histogram of read starts around transcription start sites of this cell shows low levels of enrichment. l, DNA fragment size distribution showing no nucleosome-associated periodicity.

Extended Data Figure 3 Fragment recovery metrics within scATAC-seq libraries.

a, Accessibility across all peaks (n = 50,000) in GM12878 cells. b, Accessibility across all annotated promoters in GM12878 cells. Typical promoters used for subsequent analysis are boxed with dotted lines. c, d, Recovery of typical promoters shown in a within single cells within observed (c) and extrapolated (d) data using measures of predicted library complexity.

Extended Data Figure 4 scATAC-seq data analysis pipeline and validation of bias normalization.

a–c, Standard deviation of log-fold change in reads across cells within peaks binned by deciles of peak intensity (a), Tn5 bias (b) and GC bias (c). df, Variability scores (incorporating bias normalization) within the same peaks shown in ac, peaks are binned by deciles of peak intensity (d), Tn5 bias (e) and GC bias (f). g, h, Log-fold change versus deviation scores across single K562 cells for GATA1 ChIP-seq target sites (g) and peaks (h) containing a Nanog motif. i, j, Variability scores for factors (purple) and the permuted background (grey) ranked by number of peak associations (i) and the mean accessibility per annotated peak (j). k, l, K562 single-cell data sets showing the effect on variability scores as a function of downsampling fragments. Fidelity after downsampling is measured with correlation (k) and dynamic range (l) relative to the complete data set.

Extended Data Figure 5 Biological replicates and measurement error analysis.

ac, Observed changes in variability comparing the merged set of replicates (K562) to each individual biological replicate. Error bars represent one standard deviation of the variability scores after bootstrapping cells from each replicate. df, Correlation of errors computed using three distinct approaches.

Extended Data Figure 6 Characterization of high-variance trans-factors in K562 cells.

ad, Distribution of GATA1 (a), GATA2 (b), actin (c) and CTCF (d) fluorescence observed by flow cytometry. Distributions in grey depict isotype controls. e, Bi-clustered heat map of single-cell deviations as observed within K562 cells (n = 239). Labels on right identify co-clustering of related factors. f, Bi-clustered heat map of single-cell deviations observed from permuted data. g, Projection of factor loadings onto principal component 1 versus 5 from principal component (PC) analysis of heatmap shown in e. Factor loadings do not vary along PC5, although peaks associated with regions with different replication timings (Repli-Seq) have strong variation along this axis. h, i, Venn diagrams showing variability of GATA1 and/or GATA2 (h), cJUN and/or GATA2 and CEBPB and/or GATA2 (co-)occurring ChIP-seq sites (i). j, The −log10(P values) of calculated changes in co-occurring ChIP-seq sites shown in Fig. 2g. k, Distribution of accessibility among GATA1 only, GATA2 only, and shared sites. l, Mean accessibility from GATA1 only, GATA2 only, and shared sites in k, error bars represent one standard deviation generated by bootstrapping ChIP-seq peaks.

Extended Data Figure 7 Drug treatments modulate factor variability.

a, b, Change in variability of untreated K562 cells versus cells treated with imatinib (a) and JUN inhibitor (b) show increase of variability in factors associated with the cell cycle or S phase and JUN factors, respectively. cf, Flow cytometry data depicting DNA content, using DAPI or propidium iodide, in control K562 cells (c) or cells showing altered cell-cycle status after treatment with cell-cycle inhibitor (d), imatinib (e) or JUN inhibitor (f).

Extended Data Figure 8 Transcription factor motif correlation and variability across chromatin state.

a, Hierarchical bi-clustering of high-variance transcription factor motif annotations using the Pearson correlation. b, c, Variability of regions associated with chromatin states (b), as identified in ref. 26, and histone modifications (c).

Extended Data Figure 9 Cis-variability analysis within single cells.

a, Interchromosomal chromosome 1 co-correlations of deviation scores within single cells calculated for bins of 25 peaks within GM12878 cells. b, Distribution, using density estimation, of correlation values shown in a. cg, Analysis of cis-correlation (identical to Fig. 4) for representative chromosomes 7, 11, 12, 17 and 20. Correlation between scATAC-seq cis-correlation and chromosome conformation capture methods for each chromosome in GM12878 (h) and K562 (i) cells.

Extended Data Figure 10 Measurements of individual peaks within single cells.

a, The distribution of GATA1 deviation scores for single K562 cells. b, c, Volcano plots of non-GATA1 (b) and GATA1 (c) peaks in K562 cells, P values were calculated using a binomial test. d, The distribution of NF-κB deviation scores for single GM12878 cells. e, f, Volcano plots of non-NF-κB (e) and NF-κB (f) peaks in GM12878 cells, P values were calculated using a binomial test. Inset numbers show the number of points in upper left or upper right quadrants of the panel. g, Accessibility at a genomic locus, showing (top) aggregate NF-κB low (blue) and NF-κB high (red) profiles, (middle) single GM12878 cells ranked by NF-κB deviations scores and (bottom) unranked single cells.

Supplementary information

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Buenrostro, J., Wu, B., Litzenburger, U. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

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