Assessing the inference of single-cell phylogenies and population dynamics from genetic lineage tracing data
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Background
Multicellular organisms develop from a single cell by repeated rounds of cell division, differentiation and apoptosis, which can be displayed in a single-cell phylogenetic tree. Genetic lineage tracing allows us to investigate this development by tracking the ancestry of individual cells as populations grow and change over time. However, accurate reconstruction of the cell phylogeny and quantification of the corresponding phylodynamic parameters - division, differentiation and apoptosis rate - from this tracking data remains challenging and needs to be systematically evaluated.
Results
We perform simulations and assess, using the Bayesian framework, the joint inference of time-scaled cell phylogenies and phylodynamic parameters from CRISPR lineage recordings with random or sequential edits. Principally, we characterize the inference improvement as the recorder capacity increases. Further, we characterize the increase of accuracy of phylogenetic reconstruction when using sequential compared to random recordings, i.e. when using the additional information contained in the order of edits. Moreover, we find that CRISPR lineage recordings carry a strong signal on the rates of cell division. When the phylodynamic parameters are inferred under models that match the true dynamics and when sufficiently many cells of each type are sampled, also cell death and differentiation rates can be estimated from the data.
Conclusion
In this study, we evaluate how much information on cellular development can be extracted from genetic lineage tracing data using phylogenetic and phylodynamic methodology. We identify important experimental, conceptual, and computational limitations for the inference which can guide future advancements in the field.
