The epigenetic DNA modification 5-hydroxymethylcytosine (5hmC) has crucial roles in development and gene regulation. Quantifying the abundance of this epigenetic mark at the single-cell level could enable us to understand its roles. We present a single-cell, genome-wide and strand-specific 5hmC sequencing technology, based on 5hmC glucosylation and glucosylation-dependent digestion of DNA, that reveals pronounced cell-to-cell variability in the abundance of 5hmC on the two DNA strands of a given chromosome. We develop a mathematical model that reproduces the strand bias and use this model to make two predictions. First, the variation in strand bias should decrease when 5hmC turnover increases. Second, the strand bias of two sister cells should be strongly anti-correlated. We validate these predictions experimentally, and use our model to reconstruct lineages of two- and four-cell mouse embryos, showing that single-cell 5hmC sequencing can be used as a lineage reconstruction tool.

Protein-DNA interactions are critical to the regulation of gene expression, but it remains challenging to define how cell-to-cell heterogeneity in protein-DNA binding influences gene expression variability. Here we report a method for the simultaneous quantification of protein-DNA contacts by combining single-cell DNA adenine methyltransferase identification (DamID) with mRNA sequencing of the same cell (scDam&T-seq). We apply scDam&T-seq to reveal how genome-lamina contacts or chromatin accessibility correlate with gene expression in individual cells. Furthermore, we provide single-cell genome-wide interaction data on a Polycomb-group protein, RING1B, and the associated transcriptome. Our results show that scDam&T-seq is sensitive enough to distinguish mouse embryonic stem cells cultured under different conditions and their different chromatin landscapes. Our method will enable analysis of protein-mediated mechanisms that regulate cell type-specific transcriptional programs in heterogeneous tissues.