Inference of single-cell phylogenies from lineage tracing data using Cassiopeia

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

Research output: Contribution to journalArticlepeer-review

47 Scopus citations


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. First, we introduce Cassiopeia-a suite of scalable 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, 34,557 human cells continuously traced over 15 generations, 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

Original languageEnglish (US)
Article number92
JournalGenome biology
Issue number1
StatePublished - Apr 14 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Genetics
  • Ecology, Evolution, Behavior and Systematics
  • Cell Biology


  • Lineage tracing
  • Single cell
  • scRNA-seq


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