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

Abstract

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 two lineage tracing datasets for which we estimate time-scaled trees together with cell proliferation rates. Using simulated and real lineage tracing data obtained from a monoclonal culture of HEK293T cells, we compare SciPhy to other lineage reconstruction methods, and find that SciPhy consistently constructs distinct, and more accurate lineage trees. In particular, for HEK293T cells, 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. Our second example applies SciPhy to the study of murine gastruloids, and showcases the use of complex models of time-varying population growth to capture realistic aspects of this in-vitro model of early mammalian development. Together, these examples showcase 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. SciPhys codebase is publicly available at https://github.com/azwaans/SciPhy.