Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state

By Assaf Rotem, Oren Ram, N. Shoresh, R. Sperling, Amir Goren, David A. Weitz, Bradley E. Bernstein. Nature Biotechnology, 2015

790 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/adab7a8c8364775c4db8636d3b456f8acd2a303e

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Citations in the corpus

Revealing the vectors of cellular identity with single-cell genomics (2016, methodology): “…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 coverage 26 .”
Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain (2017, neutral): “To identify corresponding regulatory signatures, we generated scTHS-seq data from 32,869 single nuclei, and we resolved 27,906 of those nuclei from the visual (13,232) and frontal (4,753) cortices and the cerebellum (9,921) into neuronal or non-neuronal cell types after clustering each region indepe”