Literature DB >> 28126923

Bisulfite-independent analysis of CpG island methylation enables genome-scale stratification of single cells.

Lin Han1, Hua-Jun Wu2,3, Haiying Zhu4,5, Kun-Yong Kim6, Sadie L Marjani4, Markus Riester2,3, Ghia Euskirchen7, Xiaoyuan Zi4,1,5, Jennifer Yang4, Jasper Han1, Michael Snyder7, In-Hyun Park6, Rafael Irizarry2,3, Sherman M Weissman4, Franziska Michor2,3, Rong Fan1, Xinghua Pan4,8,9.   

Abstract

Conventional DNA bisulfite sequencing has been extended to single cell level, but the coverage consistency is insufficient for parallel comparison. Here we report a novel method for genome-wide CpG island (CGI) methylation sequencing for single cells (scCGI-seq), combining methylation-sensitive restriction enzyme digestion and multiple displacement amplification for selective detection of methylated CGIs. We applied this method to analyzing single cells from two types of hematopoietic cells, K562 and GM12878 and small populations of fibroblasts and induced pluripotent stem cells. The method detected 21 798 CGIs (76% of all CGIs) per cell, and the number of CGIs consistently detected from all 16 profiled single cells was 20 864 (72.7%), with 12 961 promoters covered. This coverage represents a substantial improvement over results obtained using single cell reduced representation bisulfite sequencing, with a 66-fold increase in the fraction of consistently profiled CGIs across individual cells. Single cells of the same type were more similar to each other than to other types, but also displayed epigenetic heterogeneity. The method was further validated by comparing the CpG methylation pattern, methylation profile of CGIs/promoters and repeat regions and 41 classes of known regulatory markers to the ENCODE data. Although not every minor methylation differences between cells are detectable, scCGI-seq provides a solid tool for unsupervised stratification of a heterogeneous cell population.
© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

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Year:  2017        PMID: 28126923      PMCID: PMC5605247          DOI: 10.1093/nar/gkx026

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   16.971


  53 in total

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Review 7.  Using single-cell multiple omics approaches to resolve tumor heterogeneity.

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Review 8.  Single-Cell Analysis of Circulating Tumor Cells: How Far Have We Come in the -Omics Era?

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9.  Bisulfite-free epigenomics and genomics of single cells through methylation-sensitive restriction.

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