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Single cell multiomics unravel the transcription networks controlling the different EMT tumor states

Jul 2026 · Nature Communications · Vol 17 · 1 citation · 52 references
Medicine

TL;DR

The authors use single-cell multiomics and CRISPR/Cas9-mediated loss-of-function studies to identify key transcription factors controlling EMT states and metastasis and defines the transcriptional and chromatin landscape controlling EMT progression in mouse skin SCC.

Abstract

Epithelial-to-mesenchymal transition (EMT) is a dynamic process during which cells lose their epithelial characteristics and acquire mesenchymal traits. In cancer, EMT is closely associated with tumor initiation, progression, invasion, metastasis, and therapy resistance. Rather than being a binary state switch, EMT encompasses a spectrum of tumor states with distinct functional properties. However, the transcription factors (TFs) that govern transitions between these EMT states remain poorly defined. Here, using multi-omic approaches combining single-cell RNA-seq and single-cell ATAC-seq, we delineate the transcriptomic and chromatin landscapes of distinct EMT states in a mouse model of skin squamous cell carcinoma (SCC). Through CRISPR/Cas9-mediated loss-of-function studies coupled with in vitro and in vivo functional assays, we identify TFs regulating specific EMT states. Klf5 and Pitx1 control the early stages of EMT and are essential for metastasis formation. In contrast, Nfatc1 and Creb3l1 act at later stages of EMT. Similar EMT states and regulatory patterns are found in mouse pancreatic adenocarcinoma and human cancers. Altogether, our study defines the transcriptional and chromatin landscape controlling EMT progression in mouse skin SCC, identifies EMT state-specific TFs and highlights their essential roles in regulating metastasis. Epithelial-to-mesenchymal transition (EMT) involves cancer cells shifting between different states linked to tumor progression. Here, the authors use single-cell multiomics and CRISPR/Cas9-mediated loss-of-function studies to identify key transcription factors controlling EMT states and metastasis.

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