DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning.

Recent technological advances have enabled DNA methylation to be assayed at single-cell resolution. However, current protocols are limited by incomplete CpG coverage and hence methods to predict missing methylation states are critical to enable genome-wide analyses. We report DeepCpG, a computational approach based on deep neural networks to predict methylation states in single cells. We evaluate DeepCpG on single-cell methylation data from five cell types generated using alternative sequencing protocols. DeepCpG yields substantially more accurate predictions than previous methods. Additionally, we show that the model parameters can be interpreted, thereby providing insights into how sequence composition affects methylation variability

Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity.

We report a single-cell bisulfite sequencing (scBS-seq) method that can be used to accurately measure DNA methylation at up to 48.4% of CpG sites. Embryonic stem cells grown in serum or in 2i medium displayed epigenetic heterogeneity, with '2i-like' cells present in serum culture. Integration of 12 individual mouse oocyte datasets largely recapitulated the whole DNA methylome, which makes scBS-seq a versatile tool to explore DNA methylation in rare cells and heterogeneous populations.This work was supported by the UK Biotechnology and Biological Sciences Research Council grant BB/J004499/1, UK Medical Research Council grant MR/K011332/1, Wellcome Trust award 095645/Z/11/Z and EU FP7 EpiGeneSys and BLUEPRINT

Genome-Scale Oscillations in DNA Methylation during Exit from Pluripotency.

Pluripotency is accompanied by the erasure of parental epigenetic memory, with naïve pluripotent cells exhibiting global DNA hypomethylation both in vitro and in vivo. Exit from pluripotency and priming for differentiation into somatic lineages is associated with genome-wide de novo DNA methylation. We show that during this phase, co-expression of enzymes required for DNA methylation turnover, DNMT3s and TETs, promotes cell-to-cell variability in this epigenetic mark. Using a combination of single-cell sequencing and quantitative biophysical modeling, we show that this variability is associated with coherent, genome-scale oscillations in DNA methylation with an amplitude dependent on CpG density. Analysis of parallel single-cell transcriptional and epigenetic profiling provides evidence for oscillatory dynamics both in vitro and in vivo. These observations provide insights into the emergence of epigenetic heterogeneity during early embryo development, indicating that dynamic changes in DNA methylation might influence early cell fate decisions.Wellcome Trus