Single-cell chromatin accessibility reveals principles of regulatory variation

By Jason D. Buenrostro, Beijing Wu, Ulrike Litzenburger, D. Ruff, Michael L. Gonzales, Michael Snyder, Howard Y. Chang, W.J. Greenleaf. Nature, 2015

2,079 citations recorded by the source corpus.

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’.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/90ed15803987231feddb8de2271942a9690a265e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

In Molecular Biology

Citations in the corpus

Defining cell types and states with single-cell genomics (2015, background): “Cusanovich et al. (2015) Genome Research 1493 www.genome.org sequencing library from accessible DNA elements (Buenrostro et al. 2013), for use in single cells (Buenrostro et al. 2015; Cusanovich et al. 2015).”
Mapping the Mouse Cell Atlas by Microwell-Seq. (2018, positive): “…methodologies, such as single-cell genome analysis (Hou et al., 2012; Navin et al., 2011; Wang et al., 2012; Xu et al., 2012), epigenome analysis (Buenrostro et al., 2015; Cusanovich et al., 2015; Guo et al., 2017; Jin et al., 2015; Lorthongpanich et al., 2013; Mooijman et al., 2016; Nagano et…”
Dictionary learning for integrative, multimodal, and massively scalable single-cell analysis (2022, neutral): “…single-cell measurements of chromatin accessibility (for example, single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq 13,14 )), DNA methylation (single-cell bisulfite sequencing 15 ), histone modifications (single-cell cleavage under targets and tagmentation…”
Comparison and evaluation of statistical error models for scRNA-seq (2021, background): “, scATAC-seq [57]), and DNA interaction maps (i.”
Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin (2020, background): “, 2014) and the underlying regulation of chromatin (Buenrostro et al., 2015), due to both intrinsic (e.”
Revealing the vectors of cellular identity with single-cell genomics (2016, methodology): “…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 state, as well as to determine the extent of variation in the activity of the associated factors between cells 26,28 .”