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Inference of Single-Cell Phylogenies from Lineage Tracing Data

By Matthew G Jones, Alex Khodaverdian, Jeffrey J Quinn, Michelle M. Chan, Jeffrey A Hussmann, Robert Wang, Chenling Xu, Jonathan S. Weissman, Nir Yosef

Posted 10 Oct 2019
bioRxiv DOI: 10.1101/800078 (published DOI: 10.1186/s13059-020-02000-8)

The pairing of CRISPR/Cas9-based gene editing with massively parallel single-cell readouts now enables large-scale lineage tracing. However, the rapid growth in complexity of data from these assays has outpaced our ability to accurately infer phylogenetic relationships. To address this, we provide three resources. First, we introduce Cassiopeia - a suite of scalable and theoretically grounded maximum parsimony approaches for tree reconstruction. Second, we provide a simulation framework for evaluating algorithms and exploring lineage tracer design principles. Finally, we generate the most complex experimental lineage tracing dataset to date - consisting of 34,557 human cells continuously traced over 15 generations, 71% of which are uniquely marked - and use it for benchmarking phylogenetic inference approaches. We show that Cassiopeia outperforms traditional methods by several metrics and under a wide variety of parameter regimes, and provide insight into the principles for the design of improved Cas9-enabled recorders. Together these should broadly enable large-scale mammalian lineage tracing efforts. Cassiopeia and its benchmarking resources are publicly available at www.github.com/YosefLab/Cassiopeia.

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