bioRxivCancer Biology
Comparing transcriptional dynamics of the epithelial-mesenchymal transition
Abstract
Epithelial-mesenchymal (E/M) heterogeneity is ubiquitous within all epithelial tissues and the reversible transition between these two states provides cells with plasticity that contributes to organogenesis in the developing embryo, tissue homeostasis in adults, and tumour progression1. While the epithelial-mesenchymal transition (EMT) has been extensively studied, no common, EMT-defining gene expression program has been identified2. Here, we leverage highly multiplexed single-cell RNA sequencing (scRNA-seq) to compare the transcriptional program associated with the EMT across a variety of contexts, assessing 103,999 cells from 960 samples, comprising 12 EMT time course experiments and 16 independent kinase inhibitor screens. We demonstrate that the EMT is not simply a linear transition between E/M states, and transcriptional dynamics are widely variable across contexts, regardless of the cell type and cytokine used to induce the transition. While many canonical EMT genes were poor markers of the transition in our models, we identified 86 conserved mesenchymal-associated genes also coexpressed in a variety of mouse and human epithelial and carcinoma tissues. Despite the heterogeneous transcriptional responses, we identified a core set of largely conserved transcription factors coordinating these dynamics, including RELB and SOX4. Finally, we found that the EMT is associated with a broad increase in expression of secreted factors. Kinase inhibitor screens revealed multiple paracrine dependencies of the EMT, including a novel association between TGFB1 and the TNF-associated kinase RIPK1. Together, these results comprehensively highlight the complexity and diversity of the EMT, but also reveal dynamics conserved across contexts. This work will provide the foundation for understanding the nature of E/M heterogeneity and its functional consequences, which could elucidate various physiological processes and be leveraged for cancer treatments.
- organism: mouse
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
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