Dissecting cell identity via network inference and in silico gene perturbation

. Here we use gene-regulatory networks inferred from single-cell multi-omics data to perform in silico transcription factor perturbations, simulating the consequent changes in cell identity using only unperturbed wild-type data. We apply this machine-learning-based approach, CellOracle, to well-established paradigms-mouse and human haematopoiesis, and zebrafish embryogenesis-and we correctly model reported changes in phenotype that occur as a result of transcription factor perturbation. Through systematic in silico transcription factor perturbation in the developing zebrafish, we simulate and experimentally validate a previously unreported phenotype that results from the loss of noto, an established notochord…

• NS
  National Science Foundation

  Awards: 1745038, DGE-1745038, 2139839, DGE-2139839 and DGE-1745038
• SV
  Silicon Valley Community Foundation

  Awards: DAF2021-238797, HCA2-A-1708-02799
• WU
  Washington University in St. Louis
• PG
  Paul G. Allen Frontiers Group
• JS
  Japan Society for the Promotion of Science
• NI
  National Institute of General Medical Sciences

  Awards: R35 GM118179, GM126112, R01 GM126112