HeteroMeth: A Database of Cell-to-Cell Heterogeneity in DNA Methylation

Abstract

DNA methylation is an important epigenetic mark that plays a vital role in gene expression and cell differentiation. The average DNA methylation level among a group of cells has been extensively documented. However, the cell-to-cell heterogeneity in DNA methylation, which reflects the differentiation of epigenetic status among cells, remains less investigated. Here we established a gold standard of the cell-to-cell heterogeneity in DNA methylation based on single-cell bisulfite sequencing (BS-seq) data. With that, we optimized a computational pipeline for estimating the heterogeneity in DNA methylation from bulk BS-seq data. We further built HeteroMeth, a database for searching, browsing, visualizing, and downloading the data for heterogeneity in DNA methylation for a total of 141 samples in humans, mice, Arabidopsis, and rice. Three genes are used as examples to illustrate the power of HeteroMeth in the identification of unique features in DNA methylation. The optimization of the computational strategy and the construction of the database in this study complement the recent experimental attempts on single-cell DNA methylomes and will facilitate the understanding of epigenetic mechanisms underlying cell differentiation and embryonic development. HeteroMeth is publicly available at http://qianlab.genetics.ac.cn/HeteroMeth.

Single-cell transcriptome analyses recapitulate the cellular and developmental responses to abiotic stresses in rice

“…This is consistent with the previous observations in heat-shock treated Arabidopsis roots that cell-type-specific expression changes often took place in differentiated cells (Jean-Baptiste et al, 2019), although we could not rule out the possibility that the detection of DEGs was statistically less sensitive due to the relatively smaller number of primordium cells. In addition, single-cell epigenetic or transcription diversity has been reported as a hallmark of developmental potential (Gulati et al, 2020;Huan et al, 2018). It would be of interest in the future to test this idea with plant single-cell transcriptome data.…”

“…In the future, scRNA-seq analyses reported in this study, together with single-cell epigenetic approaches (Buenrostro et al, 2015;Hainer et al, 2019;Huan et al, 2018;Lai et al, 2018) and methods with spatial information using in situ reverse transcription, can help characterize the spatiotemporal transcriptome atlas of plants. Such studies will not only promote the understanding of plant cellular and developmental biology at single-cell resolution but also help breed better crops against environmental stresses (Rhee et al, 2019).…”

Contiguous and stochastic CHH methylation patterns of plant DRM2 and CMT2 revealed by single-read methylome analysis

“…As a consequence, most currently available methylome data is not single cell; analyses that can decode additional dimensions of information from this type of data are of high potential value in producing more insights from new and existing data. One such analysis was recently proposed to produce a heterogeneity signal from CG methylation patterns among cells [26]. This tool calculates the Shannon entropy of reads overlapping a set of CG sites and identifies unique patterns of methylation within this subsample, as relating to heterogeneity within the sample population of cells.…”

Processive and stochastic CHH methylation by plant DRM2 and CMT2 revealed by single-read methylome analysis

“…As a consequence, most currently available methylome data is not singlecell; analyses that can decode additional dimensions of information from this type of data are of high potential value in producing more insights from new and existing data. One such analysis was recently proposed to produce a heterogeneity signal from CG methylation patterns among cells [26]. This tool calculates the Shannon entropy of reads overlapping a set of CG sites, and identifies unique patterns of methylation within this subsample, as relating to heterogeneity within the sample population of cells.…”