Additional file 6 of Folate regulates RNA m5C modification and translation in neural stem cells

Additional file 6: Table 5. List of high-confidence m5C sites in mouse NSCs

Additional file 2 of Folate regulates RNA m5C modification and translation in neural stem cells

Additional file 2: Table 1. Library statistics

Additional file 9 of Folate regulates RNA m5C modification and translation in neural stem cells

Additional file 9. Source codes used to generate figures

Integrative single-cell omics analyses reveal epigenetic heterogeneity in mouse embryonic stem cells

<div><p>Embryonic stem cells (ESCs) consist of a population of self-renewing cells displaying extensive phenotypic and functional heterogeneity. Research towards the understanding of the epigenetic mechanisms underlying the heterogeneity among ESCs is still in its initial stage. Key issues, such as how to identify cell-subset specifically methylated loci and how to interpret the biological meanings of methylation variations remain largely unexplored. To fill in the research gap, we implemented a computational pipeline to analyze single-cell methylome and to perform an integrative analysis with single-cell transcriptome data. According to the origins of variation in DNA methylation, we determined the genomic loci associated with allelic-specific methylation or asymmetric DNA methylation, and explored a beta mixture model to infer the genomic loci exhibiting cell-subset specific methylation (CSM). We observed that the putative CSM loci in ESCs are significantly enriched in CpG island (CGI) shelves and regions with histone marks for promoter and enhancer, and the genes hosting putative CSM loci show wide-ranging expression among ESCs. More interestingly, the putative CSM loci may be clustered into co-methylated modules enriching the binding motifs of distinct sets of transcription factors. Taken together, our study provided a novel tool to explore single-cell methylome and transcriptome to reveal the underlying transcriptional regulatory networks associated with epigenetic heterogeneity of ESCs.</p></div

An overview of analysis pipeline to infer CSM with single-cell BS-seq dataset.

<p>Top panel illustrates the procedure of detecting putative CSM loci in mouse ES cells. Bottom panel illustrates the procedure of exploring the regulation mechanisms of putative CSM loci.</p

Co-methylation and co-regulation of putative CSM loci.

<p>(A) Heatmap of pair-wise Pearson’s correlations of putative CSM loci according to their methylation levels in 17 mouse tissues, with top five co-methylated modules marked. (B) The methylation profiles of the top five modules in the 17 mouse tissues, with circle showing the average methylation level, and the error bar showing the standard deviation. Tissues deriving from different germ layers are marked. (C) The significance of GO terms enriched for each module. P values were reported using NCBI DAVID annotation tool and scaled to–log10 based. (D) Top three TF motifs enriched in each module. P values were determined using Homer software.</p

The methylation profile of ASM loci.

<p>(A) Heatmap of the methylation level of 47 ASM loci in 19 cells. The methylation levels were represented by color gradient from blue (unmethylation) to yellow (partial methylation) until to red (full methylation), with white color representing missing data of the locus in that cell. (B) Boxplot of the methylation level of 47 ASM loci across single cells, with germline and somatic ASM loci marked separately.</p