Inferring active and passive mechanical drivers of epithelial convergent extension
Bibliographic record
Abstract
What can we learn about the mechanical processes that shape tissues by simply watching? Several schemes suggest that static cell morphology or junctional connectivity can reveal where chains of cells transmit force or where force asymmetries drive cellular rearrangements. We hypothesize that dynamic cell shape changes from time lapse sequences can be used to distinguish specific mechanisms of tissue morphogenesis. We refer to this as 'mechanism inference'. Convergent extension (CE) is a crucial developmental motif wherein a planar tissue narrows in one direction and lengthens in the other. It is tempting to assume that forces driving CE reside within cells of the deforming tissue, but CE may reflect a variety of active processes or passive responses to forces generated by adjacent tissues. In this work, we first construct a simple model of epithelial cells capable of passive CE in response to external forces. We adapt this framework to simulate CE from active anisotropic processes in three different modes: crawling, contraction, and capture. We develop an image analysis pipeline for analysis of morphogenetic changes in both live cells and simulated cells using a panel of mechanical and statistical approaches. Our results allow us to identify how each simulated mechanism uniquely contributes to tissue morphology and provide insight into how force transmission is coordinated. We construct a MEchanism Index (MEI) to quantify how similar live CE is to simulated CE modes. Applying these analyses to timelapse data of Xenopus neural CE reveals features of both passive motion and active forces. Furthermore, we find spatial variation across the neural plate. We compare the inferred mechanisms in the frog midline to tissues undergoing CE in both the mouse and fly. We find that distinct active modes may have different prevalences depending on the model system. Our modeling framework allows us to gain insight from tissue timelapse images and assess the relative contribution of specific cellular mechanisms to observed tissue phenotypes. This approach can be used to guide further experimental inquiry into how mechanics influences the shaping of tissues and organs during development.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".