Comparing transcriptional dynamics of the epithelial-mesenchymal transition
Bibliographic record
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 progression 1 . While the epithelial-mesenchymal transition (EMT) has been extensively studied, no common, EMT-defining gene expression program has been identified 2 . 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".