Summary
Cell identity is governed by Gene Regulatory Networks (GRNs), a complex system of molecular interactions resulting in the precise spatial and temporal regulation of gene expression. Here, we present CellOracle, a computational tool that integrates single-cell transcriptome and epigenome profiles, integrating prior biological knowledge via regulatory sequence analysis to infer GRNs. Application of these inferred GRNs to the simulation of gene expression changes in response to transcription factor (TF) perturbation enables network configurations to be interrogated in silico, facilitating their interpretation. Here, we benchmark CellOracle against ground-truth TF-gene interactions and demonstrate its efficacy to recapitulate known regulatory changes across hematopoiesis, correctly predicting well-characterized phenotypic changes in response to TF perturbations. Application of CellOracle to direct lineage reprogramming reveals distinct network configurations underlying different modes of reprogramming failure. Furthermore, analysis of GRN reconfiguration along successful cell fate conversion trajectories identifies new factors to enhance target cell yield. We validate these CellOracle predictions experimentally and highlight a putative role for AP-1-Yap1 in reprogramming. Together, these results demonstrate the efficacy of CellOracle to infer and interpret cell-type-specific GRN configurations, at high-resolution, enabling new mechanistic insights into the regulation and reprogramming of cell identity. CellOracle code and documentation are available at https://github.com/morris-lab/CellOracle.
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