• Gravina S, Dong X, Yu B, Vijg J. Single-cell genome-wide bisulfite sequencing uncovers extensive heterogeneity in the mouse liver methylome. Genome Biol. 2016 Jul 5;17(1):150. PubMed: 27380908. Categories: OncoSENS

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

    Genome Biol. 2016 Jul 5;17(1):150.

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

    Gravina S, Dong X, Yu B, Vijg J.

    Abstract

    Abstract:

    BACKGROUND:

    Transmission 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.

    RESULTS:

    We 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.

    CONCLUSIONS:

    By 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.

     

  • Gravina S, Ganapathi S, Vijg J. Single-cell, locus-specific bisulfite sequencing (SLBS) for direct detection of epimutations in DNA methylation patterns. Nucleic Acids Res. 2015 Apr 19. pii: gkv366. PubMed: 25897117. Categories: OncoSENS

    Single-cell, locus-specific bisulfite sequencing (SLBS) for direct detection of epimutations in DNA methylation patterns.

    Nucleic Acids Res. 2015 Apr 19. pii: gkv366.

    Single-cell, locus-specific bisulfite sequencing (SLBS) for direct detection of epimutations in DNA methylation patterns.

    Gravina S, Ganapathi S, Vijg J.

    Abstract

    Abstract:

    Stochastic epigenetic changes drive biological processes, such as development, aging and disease. Yet, epigenetic information is typically collected from millions of cells, thereby precluding a more precise understanding of cell-to-cell variability and the pathogenic history of epimutations. Here we present a novel procedure for directly detecting epimutations in DNA methylation patterns using single-cell, locus-specific bisulfite sequencing (SLBS). We show that within gene promoter regions of mouse hepatocytes the epimutation rate is two orders of magnitude higher than the mutation rate.

  • Akman K, Haaf T, Gravina S, Vijg J, Tresch A. Genome-wide quantitative analysis of DNA methylation from bisulfite sequencing data. Bioinformatics. 2014 Jul 1;30(13):1933-4. doi: 10.1093/bioinformatics/btu142. Epub 2014 Mar 10. PubMed: 24618468. Categories: OncoSENS

    Genome-wide quantitative analysis of DNA methylation from bisulfite sequencing data.

    Bioinformatics. 2014 Jul 1;30(13):1933-4. doi: 10.1093/bioinformatics/btu142. Epub 2014 Mar 10.

    Genome-wide quantitative analysis of DNA methylation from bisulfite sequencing data.

    Akman K, Haaf T, Gravina S, Vijg J, Tresch A.

    Abstract

    Abstract:

    SUMMARY:

    Here we present the open-source R/Bioconductor software package BEAT (BS-Seq Epimutation Analysis Toolkit). It implements all bioinformatics steps required for the quantitative high-resolution analysis of DNA methylation patterns from bisulfite sequencing data, including the detection of regional epimutation events, i.e. loss or gain of DNA methylation at CG positions relative to a reference. Using a binomial mixture model, the BEAT package aggregates methylation counts per genomic position, thereby compensating for low coverage, incomplete conversion and sequencing errors.

    AVAILABILITY AND IMPLEMENTATION:

    BEAT is freely available as part of Bioconductor at www.bioconductor.org/packages/devel/bioc/html/BEAT.html. The package is distributed under the GNU Lesser General Public License 3.0.