Single-cell genome-wide bisulfite sequencing uncovers extensive heterogeneity in the mouse liver methylome

Abstract

BackgroundTransmission fidelity of CpG DNA methylation patterns is not foolproof, with error rates from less than 1 to well over 10 % per CpG site, dependent on preservation of the methylated or unmethylated state and the type of sequence. This suggests a fairly high chance of errors. However, the consequences of such errors in terms of cell-to-cell variation have never been demonstrated by experimentally measuring intra-tissue heterogeneity in an adult organism.ResultsWe employ single-cell DNA methylomics to analyze heterogeneity of genome-wide 5-methylcytosine (5mC) patterns within mouse liver. Our results indicate a surprisingly high level of heterogeneity, corresponding to an average epivariation frequency of approximately 3.3 %, with regions containing H3K4me1 being the most variable and promoters and CpG islands the most stable. Our data also indicate that the level of 5mC heterogeneity is dependent on genomic features. We find that non-functional sites such as repeat elements and introns are mostly unstable and potentially functional sites such as gene promoters are mostly stable.ConclusionsBy employing a protocol for whole-genome bisulfite sequencing of single cells, we show that the liver epigenome is highly unstable with an epivariation frequency in DNA methylation patterns of at least two orders of magnitude higher than somatic mutation frequencies.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1011-3) contains supplementary material, which is available to authorized users.

Allele-specific epigenome maps reveal sequence-dependent stochastic switching at regulatory loci

“…Our findings are consistent with the evolutionary advantages that may stem from the buffering of housekeeping genes against the effects of random mutations, while still retaining the potential for evolutionary innovation through changes in the regulation of less essential genes with tissue-specific expression patterns, and refine reports suggesting that all promoters show epigenetic buffering (50). Highest sensitivity to genetic variation at enhancers, and potentially perturbations in general, is consistent with observations of high cell-to-cell variability of their methylation (28, 51). Validated GWAS loci, and the enhancers within those loci, show enrichment for allelic imbalances, suggesting that sensitivity of those loci to genetic variation, and potentially also to environmental influences, may at least in part explain their role in common diseases.…”

Epigenetic aging signatures in mice livers are slowed by dwarfism, calorie restriction and rapamycin treatment

“…Toward this goal, we created a consolidated mouse liver methylome dataset combining two previous studies [19, 21] with data newly generated in this study (Additional file 2: Datasets used summary). This consolidated dataset consisted of 107 liver methylomes of mice aged 0.2 to 26.0 months old (Additional file 1: Figure S2A), covering 7628 CpG sites that were detected in nearly all samples (“Methods”).…”

“…In addition to the above data, public bisulfite sequencing data were downloaded from GEO [41] or the Sequence Read Archive (SRA) (accession numbers: [GEO: GSE60012] [19], [GEO: GSE52266] [42], [GEO: GSE67507] [43] and [SRA344045] [21]). Sequencing reads were trimmed using Trim Galore [37] with default parameters, aligned to bisulfite-converted Ensembl mmGRC38 version 84 [44] using bowtie2 [45] with parameters –N 1, and the methylation states were determined using Bismark v0.14.3 [40].…”

“…For construction of an epigenetic-aging model, we used [GEO: GSE60012] [19], [SRA344045] [21] and our own control mice, for a total of 124 mice liver/hepatocyte samples. Because RRBS is targeted towards CpG-rich regions of the genome, we included sites that were covered by ≥2 reads in 97% of mice, mapped to chromosomes 1–19 and had a standard deviation >0 and ≤20%.…”

“…Ages (in days) were transformed to log 2 scale, prior to normalization. The specific sequencing studies ([19, 21, 42], Ames and UM-HET3) were used to represent batch, and the model provided to ComBat included the covariates age, gender and treatment. After performing ComBat, we used PCA to verify that this normalization reduced the effects due to differences in sequencing technology or mouse strains (Additional file 1: Figure S2D,E).…”