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Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics.

ABSTRACT: Methods for single-cell genome and transcriptome sequencing have contributed to our understanding of cellular heterogeneity, whereas methods for single-cell epigenomics are much less established. Here, we describe a whole-genome bisulfite sequencing (WGBS) assay that enables DNA methylation mapping in very small cell populations (?WGBS) and single cells (scWGBS). Our assay is optimized for profiling many samples at low coverage, and we describe a bioinformatic method that analyzes collections of single-cell methylomes to infer cell-state dynamics. Using these technological advances, we studied epigenomic cell-state dynamics in three in vitro models of cellular differentiation and pluripotency, where we observed characteristic patterns of epigenome remodeling and cell-to-cell heterogeneity. The described method enables single-cell analysis of DNA methylation in a broad range of biological systems, including embryonic development, stem cell differentiation, and cancer. It can also be used to establish composite methylomes that account for cell-to-cell heterogeneity in complex tissue samples.

SUBMITTER: Farlik M

PROVIDER: S-EPMC4542311 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics.

Farlik Matthias M Sheffield Nathan C NC Nuzzo Angelo A Datlinger Paul P Schönegger Andreas A Klughammer Johanna J Bock Christoph C

Cell reports 20150226 8

Methods for single-cell genome and transcriptome sequencing have contributed to our understanding of cellular heterogeneity, whereas methods for single-cell epigenomics are much less established. Here, we describe a whole-genome bisulfite sequencing (WGBS) assay that enables DNA methylation mapping in very small cell populations (μWGBS) and single cells (scWGBS). Our assay is optimized for profiling many samples at low coverage, and we describe a bioinformatic method that analyzes collections of ...[more]

PMID: 25732828

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Project description:Here we describe a method for whole genome bisulfite sequencing in very small cell populations (µWGBS) and in single cells (scWGBS). Our method is optimized for profiling DNA methylation in many samples at low coverage, and we developed a bioinformatic method that uses collections of single-cell methylomes to infer cell state dynamics. Using these technological advances, we studied epigenetic cell state dynamics in three in vitro models of cellular differentiation and pluripotency and identified patterns of epigenetic remodeling and cell-to-cell heterogeneity. 1-cell or 4-cell samples were collected from a variety of human and mouse cell lines (KBM7, CCE, K562, 32D, HL60). We also included negative controls (no cells), and cross controls (cells of one species aligned to the other). K562 cells were treated with 5-azacytidine in a final concentration of 1µM to induce demethylation. HL60 cells were treated with 1α,25-dihydroxyvitamin D3 in a final concentration of 10nM to induce differentiation. Naïve pluripotency of CCE ES cells was induced by culturing the cells in ESGRO-2i medium with the addition of 1% FCS. Differentiation of CCE ES cells was induced by treating the cells with all-trans retinoic acid (ATRA) in a final concentration of 0,3µM every third day along with complete LIF-free medium exchange. Embryoid body formation from CCE ES cells was induced by hanging-drop culture for 5 days, followed by 9 days on gelatinized dishes (see treatment annotation). Each experiment, with treated and untreated cells, was performed in multiple biological replicates. We then measured DNA methylation in the individual 1-cell or 4-cell experiments with bisulfite treatment and high-throughput sequencing. For sequencing, most experiments pooled a 4-cell experiment from one species with a 1-cell experiment from the other, but some replicates were sequenced as a single cell (indicated with SC in the replicate annotation). Two samples (marked as “deep”) were sequenced much deeper than the others. See Farlik et al. (2015) Cell Reports for complete study details.

Project description:BackgroundDetection of DNA methylome at single-base resolution is a significant challenge but promises to shed considerable light on human disease etiology. Current technologies could not detect DNA methylation genome-wide at single-base resolution with small amount of sequencing data and could not avoid detecting the methylation of repetitive elements which are considered as "junk DNA".MethodsIn this study, we have developed a novel DNA methylome profiling technology named MB-seq with its ability to identify genome-wide 5mC and quantify DNA methylation levels by introduced an assistant adapter AluI-linker This linker can be ligated to sonicated DNA and then be digested after the bisulfite treatment and amplification, which has no effect of MeDIP enrichment. Because many researchers are interested in investigating the methylation of functional regions such as promoters and gene bodies, we have also developed a novel alternative method named MRB-seq, which can be used to investigate the DNA methylation of functional regions by removing the repeats with Cot-1 DNA.ResultsIn this study, we have developed MB-seq, a novel DNA methylome profiling technology combining MeDIP-seq with bisulfite conversion, which can precisely detect the 5mC sites and determine their DNA methylation level at single-base resolution in a cost-effective way. In addition, we have developed a new alternative method, MRB-seq (MeDIP-repetitive elements removal-bisulfite sequencing), which interrogates 5mCs in functional regions by depleting nearly half of repeat fragments enriched by MeDIP. Comparing MB-seq and MRB-seq to whole-genome BS-seq using the same batch of DNA from YH peripheral blood mononuclear cells. We found that the sequencing data of MB-seq and MRB-seq almost reaches saturation after generating 7-8 Gbp data, whereas BS-seq requires about 100 Gbp data to achieve the same effect. In comparison to MeDIP-seq and BS-seq, MB-seq offers several key advantages, including single-base resolution, discriminating the methylated sites within a CpG and non-CpG pattern and overcoming the false positive of MeDIP-seq due to the non-specific binding of 5-methylcytidine antibody to genomic fragments.ConclusionOur novel developed method MB-seq can accelerate the decoding process of DNA methylation mechanism in human diseases because it requires 7-8 Gbp data to measure human methylome with enough coverage and sequencing depth, affording it a direct and practical application in the study of multiple samples. In addition, we have also provided a novel alternative MRB-seq method, which removes most repetitive sequences and allows researchers to genome-wide characterize DNA methylation of functional regions.

Dataset Information

Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics.

Publications

Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics.

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