Identification of EMT signaling cross-talk and gene regulatory networks by single-cell RNA sequencing

Proc Natl Acad Sci U S A. 2021 May 11;118(19):e2102050118. doi: 10.1073/pnas.2102050118.

Abstract

The epithelial-to-mesenchymal transition (EMT) plays a critical role during normal development and in cancer progression. EMT is induced by various signaling pathways, including TGF-β, BMP, Wnt-β-catenin, NOTCH, Shh, and receptor tyrosine kinases. In this study, we performed single-cell RNA sequencing on MCF10A cells undergoing EMT by TGF-β1 stimulation. Our comprehensive analysis revealed that cells progress through EMT at different paces. Using pseudotime clustering reconstruction of gene-expression profiles during EMT, we found sequential and parallel activation of EMT signaling pathways. We also observed various transitional cellular states during EMT. We identified regulatory signaling nodes that drive EMT with the expression of important microRNAs and transcription factors. Using a random circuit perturbation methodology, we demonstrate that the NOTCH signaling pathway acts as a key driver of TGF-β-induced EMT. Furthermore, we demonstrate that the gene signatures of pseudotime clusters corresponding to the intermediate hybrid EMT state are associated with poor patient outcome. Overall, this study provides insight into context-specific drivers of cancer progression and highlights the complexities of the EMT process.

Keywords: EMT; NOTCH; RACIPE; scRNA-seq; signaling cascade.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cell Line
  • Epithelial Cells / drug effects
  • Epithelial Cells / metabolism
  • Epithelial-Mesenchymal Transition / drug effects
  • Epithelial-Mesenchymal Transition / genetics*
  • Gene Expression Profiling / methods
  • Gene Expression Profiling / statistics & numerical data
  • Gene Regulatory Networks*
  • Humans
  • Kaplan-Meier Estimate
  • MicroRNAs / genetics
  • Neoplasms / classification
  • Neoplasms / genetics
  • Prognosis
  • Proportional Hazards Models
  • RNA-Seq / methods*
  • Signal Transduction / drug effects
  • Signal Transduction / genetics*
  • Single-Cell Analysis / methods*
  • Transforming Growth Factor beta1 / metabolism
  • Transforming Growth Factor beta1 / pharmacology

Substances

  • MicroRNAs
  • Transforming Growth Factor beta1