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

CRISPR-based lineage tracing offers a promising avenue to decipher single cell lineage trees, especially in organisms that are challenging for microscopy. A recent advancement in this domain is lineage tracing based on sequential genome editing, which not only records genetic edits but also the order in which they occur. To capitalize on this enriched data, we introduce SciPhy, a simulation and inference tool integrated within the BEAST 2 framework. SciPhy utilizes a Bayesian phylogenetic approach to estimate time-scaled phylogenies and cell population parameters. After validating SciPhy using simulations, we apply it to lineage tracing data obtained from a monoclonal culture of HEK293T cells for which we estimate time-scaled trees together with cell proliferation rates. We compare SciPhy to the lineage reconstruction based on a widely used clustering method, UPGMA, and find that the UPGMA-reconstructed lineage trees differ from SciPhy trees in some key aspects of tree structure; in particular, SciPhy trees stand out for their later estimated cell division times. In addition, SciPhy reports uncertainty as well as proliferation rates, neither of which are available within a UPGMA analysis. This study showcases the application of advanced phylogenetic and phylodynamic tools to explore and quantify cell lineage trees, laying the groundwork for enhanced and confident analyses to decode the complexities of biological development in multicellular organisms. SciPhy’s codebase is publicly available at https://github.com/azwaans/SciPhy .