Dissecting cell identity via network inference and in silico gene perturbation
By Kenji Kamimoto, Blerta Stringa, Christy M. Hoffmann, Kunal Jindal, L. Solnica-Krezel, Samantha A. Morris. Nature, 2023
597 citations recorded by the source corpus.
A machine-learning-based strategy called CellOracle combines computational perturbation with modelling of gene-regulatory networks to analyse how cell identity is regulated by transcription factors, and correctly predicts phenotypic changes after transcription factor perturbation in the developing zebrafish. Cell identity is governed by the complex regulation of gene expression, represented as gene-regulatory networks^ 1 . 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.
Subfields: Cell Biology, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/9672a8fe4712abd9e74c40d8e906c9a82f33ef39
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Citations in the corpus
- Predicting transcriptional outcomes of novel multigene perturbations with GEARS (2023, unknown): “GEARS functionalizes this graph-based inductive bias using a graph neural network (GNN) architecture 33 .”
- Gene regulatory network inference in the era of single-cell multi-omics (2023, methodology): “Using the CellOracle 76 framework for in silico predictions, followed by experimental validation, they characterized a role of Brachyury in priming cis -regulatory elements (CREs) for differentiation.”