Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state
Open accessAbstract
Chromatin profiling provides a versatile means to investigate functional genomic elements and their regulation. However, current methods yield ensemble profiles that are insensitive to cell-to-cell variation. Here we combine microfluidics, DNA barcoding and sequencing to collect chromatin data at single-cell resolution. We demonstrate the utility of the technology by assaying thousands of individual cells and using the data to deconvolute a mixture of ES cells, fibroblasts and hematopoietic progenitors into high-quality chromatin state maps for each cell type. The data from each single cell are sparse, comprising on the order of 1,000 unique reads. However, by assaying thousands of ES cells, we identify a spectrum of subpopulations defined by differences in chromatin signatures of pluripotency and differentiation priming. We corroborate these findings by comparison to orthogonal single-cell gene expression data. Our method for single-cell analysis reveals aspects of epigenetic heterogeneity not captured by transcriptional analysis alone.
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Recent progress in epigenome analysis has included assays for single-cell ChIP-seq (Drop-ChIP), single-cell ATAC-seq, single-cell Hi-C, and single-cell DNA methylation.
“Although most high-throughput single-cell profiling studies to date have focused on RNA and protein, recent progress in epigenome analysis185 has included assays for single-cell ChIP-seq (Drop-ChIP26), single-cell ATAC-seq27,28, single-cell Hi-C29 (a genome-wide derivative of the chromosome conformation capture (3C) technique), and single-cell DNA methylation22–25.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [26]
Because the probability of detecting a signal at a particular locus is very low, clustering of single-cell ChIP-seq profiles using locus-based correlations between them is highly sensitive to algorithm parameters and global technical attributes, such as mean single-cell coverage.
“For example, because the probability of detecting a signal (finding a read) at a particular locus is very low, clustering of single-cell ChIP-seq profiles using locus-based correlations between them is highly sensitive to algorithm parameters and global technical attributes, such as mean single-cell coverage26.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [26]
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 [26]
Existing single-cell epigenomic methods are relatively low throughput and costly.
“Technological innovations have enabled the development of single-cell epigenomic methods14–16; however, these approaches remain relatively low throughput and have high costs.”
From Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility (2019) · cites this paper as [16]
Single-cell characterization of histone post-translational modifications has not been achieved for highly complex adult organs such as the brain.
“However, for highly complex adult organs such as the brain, this single-cell characterization has not been achieved.”
From Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues (2021) · cites this paper as [7]
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Single-cell epigenomic variability reveals functional cancer heterogeneity
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Droplet-based single-cell joint profiling of histone modifications and transcriptomes
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“Single cell sequencing technologies in particular have uncovered heterogeneity at both the transcriptional and epigenetic level [8, 9].”
BackgroundAnalyzing Circulating Tumor Cells One at a Time
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Evolution of regulatory signatures in primate cortical neurons at cell-type resolution
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- Authors
- Rotem, A., Ram, O., Shoresh, N., Sperling, R. A., Goren, A., Weitz, D. A., & Bernstein, B. E.
- Affiliations
- Broad Institute (US)
- Harvard University (US)
- Corresponding author
- Weitz, David A
- Bernstein, Bradley E
- Venue
- Nature Biotechnology
- Published
- 2015-10-12
- Volume
- 33
- Issue
- 11
- Pages
- 1165-1172
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC4636926
- OpenAlex
- W2198371822
- Cited by
- 854
- References
- 46
- Citations by year
- 2026: 24 · 2025: 45 · 2024: 65 · 2023: 74 · 2022: 89 · 2021: 102 · 2020: 87 · 2019: 114 · 2018: 89 · 2017: 97 · 2016: 65 · 2015: 2 · 2014: 1
- FWCI
- 36.64
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- National Heart, Lung, and Blood Institute (U01HL100395)
- National Human Genome Research Institute (U54HG006991, P50HG006193)
- 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 › Epigenetics and DNA Methylation
- Keywords
- chromatin state, cellular heterogeneity, epigenetic heterogeneity, cell subpopulations, pluripotency, embryonic stem cells, fibroblasts, hematopoietic progenitor cells, microfluidics, DNA barcoding, single-cell epigenomics
- MeSH terms
- Animals, Chromatin, Humans, Sequence Analysis, DNA, Computational Biology, Microfluidic Analytical Techniques, Chromatin Immunoprecipitation, Mice, Embryonic Stem Cells, DNA Barcoding, Taxonomic, Single-Cell Analysis
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