Startle: A star homoplasy approach for CRISPR-Cas9 lineage tracing

Palash Sashittal, Henri Schmidt, Michelle M. W. Chan, Benjamin J. Raphael

Research output: Contribution to journalArticlepeer-review


CRISPR-Cas9-based genome editing combined with single-cell sequencing enables the tracing of the history of cell divisions, or cellular lineage, in tissues and whole organisms. Although standard phylogenetic approaches may be applied to reconstruct cellular lineage trees from this data, the unique features of the CRISPR-Cas9 editing process motivate the development of specialized models that describe the evolution of CRISPR-Cas9-induced mutations. Here, we introduce the “star homoplasy” evolutionary model that constrains a phylogenetic character to mutate at most once along a lineage, capturing the “non-modifiability” property of CRISPR-Cas9 mutations. We derive a combinatorial characterization of star homoplasy phylogenies and use this characterization to develop an algorithm, “Startle”, that computes a maximum parsimony star homoplasy phylogeny. We demonstrate that Startle infers more accurate phylogenies on simulated lineage tracing data compared with existing methods and finds parsimonious phylogenies with fewer metastatic migrations on lineage tracing data from mouse metastatic lung adenocarcinoma.

Original languageEnglish (US)
Pages (from-to)1113-1121.e9
JournalCell Systems
Issue number12
StatePublished - Dec 20 2023

All Science Journal Classification (ASJC) codes

  • Pathology and Forensic Medicine
  • Cell Biology
  • Histology


  • CRISPR-Cas9
  • cancer
  • combinatorial optimization
  • development
  • lineage tracing
  • mathematical modeling
  • phylogeny


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