(505e) 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 (5hmC). When applied to 8-cell preimplantation mouse embryos, scPECLR predicts the full lineage tree with greater than 95% accuracy. Furthermore, scPECLR can accurately extract lineage information for a majority of cells when reconstructing larger trees. In addition, we developed a new single-cell technology to simultaneously quantify both 5hmC and genomic DNA from the same cell. Copy-number variations and single-nucleotide polymorphisms inferred from sequencing genomic DNA enabled us to significantly improve the prediction accuracy of larger trees. We find that for 32-cell trees with more than 10²⁶ possible topologies, our integrated single-cell technology improves the prediction accuracy by 31%. 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.