PUBLICATION

Dissecting cell identity via network inference and in silico gene perturbation

Authors
Kamimoto, K., Stringa, B., Hoffmann, C.M., Jindal, K., Solnica-Krezel, L., Morris, S.A.
ID
ZDB-PUB-230210-20
Date
2023
Source
Nature   614(7949): 742-751 (Journal)
Registered Authors
Solnica-Krezel, Lilianna
Keywords
none
Datasets
GEO:GSE145298
MeSH Terms
  • Animals
  • Cell Differentiation*/genetics
  • Computer Simulation*
  • Embryonic Development/genetics
  • Gene Regulatory Networks*
  • Hematopoiesis/genetics
  • Humans
  • Mesoderm/enzymology
  • Mesoderm/metabolism
  • Mice
  • Phenotype
  • Transcription Factors*/metabolism
  • Zebrafish/embryology
  • Zebrafish/genetics
PubMed
36755098 Full text @ Nature
Abstract
Cell identity is governed by the complex regulation of gene expression, represented as gene-regulatory networks1. 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 regulator. Furthermore, we identify an axial mesoderm regulator, lhx1a. Together, these results show that CellOracle can be used to analyse the regulation of cell identity by transcription factors, and can provide mechanistic insights into development and differentiation.
Genes / Markers
Figures
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Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Antibodies
Orthology
Engineered Foreign Genes
Mapping