Cell lineage inference from SNP and scRNA-Seq data

Nucleic Acids Res. 2019 Jun 4;47(10):e56. doi: 10.1093/nar/gkz146.


Several recent studies focus on the inference of developmental and response trajectories from single cell RNA-Seq (scRNA-Seq) data. A number of computational methods, often referred to as pseudo-time ordering, have been developed for this task. Recently, CRISPR has also been used to reconstruct lineage trees by inserting random mutations. However, both approaches suffer from drawbacks that limit their use. Here, we develop a method to detect significant, cell type specific, sequence mutations from scRNA-Seq data. We show that only a few mutations are enough for reconstructing good branching models. Integrating these mutations with expression data further improves the accuracy of the reconstructed models. As we show, the majority of mutations we identify are likely RNA editing events indicating that such information can be used to distinguish cell types.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Cell Lineage*
  • Cells, Cultured
  • Cluster Analysis
  • Computational Biology / methods
  • Gene Expression Profiling / methods
  • High-Throughput Nucleotide Sequencing / methods*
  • Humans
  • Mutation
  • RNA Editing
  • Sequence Analysis, RNA / methods*
  • Single-Cell Analysis / methods*