A probabilistic framework for cellular lineage reconstruction using single-cell 5-hydroxymethylcytosine 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 that prevent the reconstruction of lineage trees at an individual cell division resolution. Moreover, these methods require a transgene, which poses a significant barrier in the study of human tissues. To overcome these limitations, we report scPECLR, a probabilistic algorithm to endogenously infer lineage trees at a single cell-division resolution using 5-hydroxymethylcytosine. When applied to 8-cell preimplantation mouse embryos, scPECLR predicts the full lineage tree with greater than 95% accuracy. Further, scPECLR can accurately extract lineage information for a majority of cells when reconstructing larger trees. Finally, we show that scPECLR can also be used to map chromosome strand segregation patterns during cell division, thereby providing a strategy to test the “immortal strand” hypothesis in stem cell biology. Thus, scPECLR provides a generalized method to endogenously reconstruct lineage trees at an individual cell-division resolution.

The immortal strand hypothesis: still non-randomly segregating opinions

Cairns first suggested a mechanism for protecting the genomes of stem cells (SCs) from replicative errors some 40 years ago when he proposed the immortal strand hypothesis, which argued for the inheritance of a so-called immortal strand by an SC following asymmetric SC divisions. To date, the existence of immortal strands remains contentious with published evidence arguing in favour of and against the retention of an immortal strand by asymmetrically dividing SCs. The conflicting evidence is derived from a diverse array of studies on adult SC types and is predominantly based on following the fate of labelled DNA strands during asymmetric cell division events. Here, we review current data, highlighting limitations of such labelling techniques, and suggest how interpretation of such data may be improved in the future.