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

Menée à l'aide d'une lignée de cellules épithéliales mammaires, d'une technique de séquençage de l'ARN à l'échelle d'une seule cellule et d'un modèle mathématique, cette étude identifie plusieurs mécanismes d'activation de la transition épithélio-mésenchymateuse

Résumé en anglais

The epithelial-to-mesenchymal transition (EMT) is a critical cell biological process that occurs during normal embryonic development and cancer progression. Our study combines single-cell RNA-sequencing analysis and mathematical modeling to identify critical regulators of EMT. Detailed analyses of TGF-β1–induced EMT by single-cell RNA-sequencing data revealed simultaneous activation of EMT signaling pathways. We created mathematical approaches to identify the master regulatory pathway of EMT and key downstream mediators of this process. This study sheds light on the signaling architecture that governs EMT and informs ongoing efforts to delineate drivers of cancer initiation, progression, and metastasis.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. All the raw sequencing data have been deposited at National Center for Biotechnology Information Sequence Read Archive (BioProject ID: PRJNA698642). All other study data are included in the article and supporting information.