Control of cellular cortical tension and shape by RhoGTPase signalling
Bibliographic record
Abstract
Abstract Shape changes are ubiquitous in biology, from cytokinesis at the single cell scale to tissue-scale morphogenesis involving coordinated changes in hundreds of cells. In all cases, morphogenesis is powered by gradients in mechanical tension that arise downstream of signalling. Many pathways converge on RhoGTPases that modulate the cytoskeleton and cell contractility to control cell mechanics and, subsequently, shape. Despite their physiological importance, we lack a quantitative understanding of how changes in signalling alter cortical mechanics to drive cell shape change. Here, we use optogenetics to quantitatively characterise the relationship between the amount of RhoGEF localised to the membrane, the downstream myosin recruitment, and the subsequent mechanical changes. We then show that cortical myosin amount and cortical tension increase linearly with the amount of membranous RhoGEF signalling. Based on these data, we develop a predictive model of the temporal evolution of RhoGEF membrane localisation, cortical myosin enrichment, and cortical tension in response to a pulse of light. Using this model together with an active surface model of the cell cortex, we show that the cellular shape changes induced by localised optogenetic recruitment of RhoGEF signalling can be predicted, directly linking gradients in signalling to shape change. Significance statement Shape changes are ubiquitous in biology, during division in single cells and in tissue during embryogenesis. In all cases, shape change is powered by gradients in mechanical tension that arise downstream of changes in biochemical signals. Despite their importance, we lack a quantitative understanding of how changes in signals alter cell mechanics to drive cell shape change. Here, we control the location and amount of biochemical signal using light to quantitatively characterise the relationship between signals and their resulting biological and mechanical changes. We show that mechanical change scales linearly with the amount of biochemical signal. Based on this, we develop a mathematical model that predicts cell mechanical and shape changes from the location and amount of biochemical signals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".