GSE131678
A probabilistic framework for cellular lineage reconstruction using integrated single-cell 5-hydroxymethylcytosine and genomic DNA sequencing
Lineage reconstruction is central to understanding tissue development and maintenance. While powerful tools to infer cellular relationships have been developed, these methods typically have a clonal resolution and require a transgene. Here, we report scPECLR, a probabilistic algorithm to endogenously infer lineage trees at a single cell-division resolution using 5-hydroxymethylcytosine (5hmC). When applied to 8-cell mouse embryos, scPECLR predicts the full lineage tree with greater than 95% accuracy, with the ability to infer larger lineage trees depending on the distribution of 5hmC patterns. Finally, we show that scPECLR can also be used to test the "immportal strand" hypothesis in stem cell biology. Thus, scPECLR provides a generalized method to endogenously reconstruct lineage trees at an individual cell-division resolution. Overall design: In this study, strand-specific 5-hydroxymethylcytosine (5hmC) was detected on a genome-wide scale in single cells from 8-cell mouse embryos. The distribution of 5hmC in single cells was used to reconstruct cellular lineages using a new probabilistic framework called scPECLR. Moreover, scH&G that can detect 5hmC and gDNA from the same single cell was developed to increase the reconstruction power of scPECLR.