Abstract

In direct lineage conversion, transcription factor (TF) overexpression reconfigures gene regulatory networks (GRNs) to reprogram cell identity. We previously developed CellOracle, a computational method to infer GRNs from single-cell transcriptome and epigenome data. Using inferred GRNs, CellOracle simulates gene expression changes in response to TF perturbation, enabling in silico interrogation of network reconfiguration. Here, we combine CellOracle analysis with lineage tracing of fibroblast to induced endoderm progenitor (iEP) conversion, a prototypical direct reprogramming paradigm. By linking early network state to reprogramming outcome, we reveal distinct network configurations underlying successful and failed fate conversion. Via in silico simulation of TF perturbation, we identify new factors to coax cells into successfully converting their identity, uncovering a central role for the AP-1 subunit Fos with the Hippo signaling effector, Yap1. Together, these results demonstrate the efficacy of CellOracle to infer and interpret cell-type-specific GRN configurations, providing new mechanistic insights into lineage reprogramming.

Original languageEnglish
Pages (from-to)97-112
Number of pages16
JournalStem Cell Reports
Volume18
Issue number1
DOIs
StatePublished - Jan 10 2023

Keywords

  • cell fate prediction
  • direct lineage reprogramming
  • gene perturbation simulation
  • gene regulatory networks
  • machine learning
  • single-cell analysis

Fingerprint

Dive into the research topics of 'Gene regulatory network reconfiguration in direct lineage reprogramming'. Together they form a unique fingerprint.

Cite this