Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing
Open accessAbstract
Chromatin state and the single cell: Identifying the chromatin state of any single cell, which may or may not have a different function or represent different stages relative to others collected within any single culture, experiment, or tissue, has been challenging. Cusanovitch et al. skirted previously identified technological limitations to identify regions of accessible chromatin at single-cell resolution. Combinatorial cellular indexing, a strategy for multiplex barcoding of thousands of single cells per experiment, was successfully used to investigate the genome-wide chromatin accessibility landscape in each of over 15,000 single cells. Science , this issue p. 910
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Two 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 [112]
The paper’s Abstract describes integrating combinatorial cellular indexing with ATAC-seq to measure chromatin accessibility in single cells. It supports this 2015 method, but does not establish the claim that two methods were developed in 2015. (LLM, conf=0.86)
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 [82]
The Abstract describes ATAC-seq-based chromatin accessibility profiling in more than 15,000 single cells. The experiments described in our copy use mixtures of cultured human and mouse cell lines, not in vivo profiling across a whole organism; the paper discusses broader tissue characterization only as a future possibility. (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 [82]
Other 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 [184]
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 [59]
Cusanovich et al. adapted latent semantic indexing to unravel different cell types based on accessibility profile patterns and to identify sets of co-accessible regions.
“For example, Cusanovich et al. [59] adapted a previously existing text clustering method called latent semantic indexing to unravel different cell types based on their accessibility profile patterns and to identify sets of co-accessible regions.”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [59]
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 [59]
The number of reads per single cell reported for combinatorial indexing (median 2503 reads per cell) is considerably lower than those obtained via the microfluidic approach (73 000 reads per cell).
“but the number of reads per single cell reported by Cusanovich et al. [59] for combinatorial indexing (median of 2503 reads per cell) is considerably lower than those obtained via the microfluidic approach (73 000 reads per cell)”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [59]
By grouping coordinated combinations of accessible regions, Cusanovich et al. identified subtypes in the lymphoblastic cell line GM12878, of which the variability is driven by activity of the nuclear factor-kappa B pathway.
“For instance, by grouping coordinated combinations of accessible regions, they identify subtypes in the lymphoblastic cell line GM12878, of which the variability is driven by activity of the nuclear factor-kappa B pathway.”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [59]
Combinatorial indexing has a higher throughput of 500-1500 single cells per run, compared with 96 cells with the microfluidics-based platform.
“combinatorial indexing has a higher throughput of 500–1500 single cells per run, as compared with 96 cells with the microfluidics-based platform”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [59]
sciATAC-seq is able to uncover different combinations of transcription factor activity between cell lines and between cells within an apparently homogeneous population.
“Cusanovich et al. illustrated that sciATAC-seq is not only able to uncover different combinations of TF activity between cell lines but also between cells within an apparently homogeneous population.”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [59]
Removing the main axes of technical variation entirely may eliminate important aspects of the biological signal.
“While several studies have opted to remove these axes of variation entirely27,65, this may result in eliminating important aspects of the biological signal.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [27]
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 [27]
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 [27]
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 [133]
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 [19]
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 [19]
Transposed nuclei and DNA remain intact after transposition.
“Importantly, previous studies have described that transposed nuclei and DNA remain intact after transposition19,23.”
From Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility (2019) · cites this paper as [19]
The estimated collision rate below 2% is considerably lower than the over 5% rate of other previously described high-throughput sciATAC-seq methods.
“Notably, this estimated collision rate (<2%) is considerably lower than that of other previously described high-throughput single-cell combinatorial indexing for ATAC-seq (sciATAC-seq) methods19,24 (>5%) (Supplementary Fig. 3a).”
From Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility (2019) · cites this paper as [19]
The combinatorial barcoding method's utility has been demonstrated for DNA sequencing, RNA-seq and chromatin accessibility assays.
“The method's utility has been demonstrated for DNA sequencing (Vitak et al, 2017), RNA‐seq (Cao et al, 2017) and chromatin accessability assays (Cusanovich et al, 2015).”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Cusanovich et al, 2015
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- Authors
- Cusanovich, D. A., Daza, R., Adey, A., Pliner, H. A., Christiansen, L., Gunderson, K. L., Steemers, F. J., Trapnell, C., & Shendure, J.
- Affiliations
- University of Washington (US)
- Oregon Health & Science University (US)
- Illumina (United States) (US)
- Corresponding author
- Shendure, Jay
- ORCID
- Venue
- Science
- Published
- 2015-05-22
- Volume
- 348
- Issue
- 6237
- Pages
- 910-914
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC4836442
- OpenAlex
- W1492083415
- Cited by
- 1,248
- References
- 38
- Citations by year
- 2026: 82 · 2025: 106 · 2024: 124 · 2023: 140 · 2022: 145 · 2021: 169 · 2020: 132 · 2019: 102 · 2018: 98 · 2017: 75 · 2016: 49 · 2015: 22 · 2014: 2
- FWCI
- 35.30
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH (1DP1HG007811)
- Paul G. Allen Family Foundation
- Damon Runyon Cancer Research Foundation (DFS-#10-14)
- 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 accessibility, single-cell epigenomics, cell clustering, Human Cell Atlas
- MeSH terms
- Chromatin, Humans, HL-60 Cells, Epigenesis, Genetic, HEK293 Cells, Single-Cell Analysis
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