Year-end Sale % OFF 
Abstract
Understanding cell-to-cell variability in cytosine methylation is essential for understanding cellular perturbation and its molecular machinery. However, conventional methylation studies have focused on the differences in the average levels between cell types while overlooking methylation heterogeneity within cell types. Little information has been uncovered using recent single-cell methods because of either technical limitations or the great labor required to process many single cells. Here, we report the highly efficient detection of cell-to-cell DNA methylation variability in liver tissue, based on comparing the methylation status of adjacent CpG sites on long sequencing reads. This method provides abundant methylation linkage information and enables genome-wide estimation of cell-to-cell variability. We observed repressed methylation variability in hypomethylated regions compared with the variability in hypomethylated regions across the genome, which we confirmed using public human sperm data. A gradual change in methylation status at the boundaries of hypomethylated regions was observed for the first time. This approach allows the concise, comprehensive assessment of cell-to-cell DNA methylation variability.
Highlights
The DNA in a given cell population is identical, the epigenetic information in different cells, such as DNA methylation or histone modifications, is thought to differ
Using public HiSeq (100 bp reads) human sperm data[7], we confirmed some of our findings, the shorter read length limited the observation of linked methylation status
We present an efficient, informative method for analyzing cell-to-cell DNA methylation patterns in liver tissues by mining adjacent CpG sites on individual long reads
Summary
The DNA in a given cell population is identical, the epigenetic information in different cells, such as DNA methylation or histone modifications, is thought to differ. Compared with single-cell methylome studies, this approach cannot compare methylomes at the resolution of a single cell, but could provide the characteristics of cell-to-cell variability Combining this with HiSeq sequencing, Landan et al conducted a genome-wide epigenetic polymorphism study using the methylation patterns of four adjacent CpG sites on individual reads[6]. The genome coverage was extremely poor; only 0.3% of all CpG sites were covered and only CpG-rich regions were detectable due to the short length of the reads (36 bp) We address these difficulties and limitations by using long MiSeq sequencing reads (2 × 300 bp paired-end reads), which can effectively and comprehensively observe the methylation status of 3 to 21 adjacent CpG sites on individual reads (Fig. 1A). The sufficiency of genome coverage using longer reads allows a deeper understanding of cell-to-cell DNA methylation variability
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
