The complex microscopic dynamics of cells in high-density epithelial tissues
Bibliographic record
Abstract
Abstract Epithelial tissues line the surfaces of vital organs and are often densely packed in a disordered, mechanically arrested state, referred to as ‘jammed’ or ‘glassy state. While collective migration at low-density regimes has been extensively studied, the microscopic dynamics within such jammed epithelia remains poorly understood. Here, we reveal that contrary to expectations from thermal systems with glassy dynamics, jammed epithelial monolayers do not exhibit cage effects that reflect the temporary spatial trapping of the particles. Instead, cells display sub-diffusive creep and Fickian yet non-Gaussian dynamics, accompanied by compressed exponential relaxation, features that reveal stress-driven fluidity. We show that cell divisions and extrusions transiently do enhance local motion, they are insufficient to fluidize the tissue globally. Fast-moving cells form collective, anisotropic clusters, and these dynamic heterogeneities correlate with local structural entropy and low-frequency vibrational modes. These findings challenge the conventional view that jammed tissues are static and inert structures, uncovering a hidden fluidity that can be expected to play a critical role in morphogenesis, wound healing, and early tumor progression.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".