SciPhy: A Bayesian phylogenetic framework using sequential genetic lineage tracing data

Abstract

Abstract CRISPR-based lineage tracing offers a promising avenue to decipher single-cell lineage trees, especially in organisms not amenable to microscopy. Sequential genome editing records not only genetic edits but also the order in which they occur. To leverage this enriched information, we introduce SciPhy, a simulation and inference tool implemented in BEAST 2. SciPhy utilizes a Bayesian phylogenetic approach to jointly estimate time-scaled phylogenies and cell population parameters. After validation on simulated data, we use simulated and real data from a monoclonal cell culture to benchmark SciPhy against existing methods and find that it consistently reconstructs more accurate phylogenies. Compared to UPGMA, SciPhy additionally reports uncertainty and proliferation rates. Our second example applies SciPhy to murine gastruloids, demonstrating its ability to model time-varying population dynamics in early development. Together, these results establish a phylodynamic framework for the quantitative analysis of lineage tracing data. SciPhy’s codebase is publicly available at https://github.com/azwaans/SciPhy .