Electric field–guided collective motility initiation of large epidermal cell groups
Bibliographic record
Abstract
Recent research has elucidated mechanochemical pathways of single cell polarization, but much less is known about collective motility initiation in adhesive cell groups. We used galvanotactic assays of zebrafish keratocyte cell groups, pharmacological perturbations, electric field switches, particle imaging velocimetry, and cell tracking to show that large cell groups initiate motility in minutes toward the cathode. Interestingly, while PI3K-inhibited single cells are biased toward the anode, inhibiting PI3K does not affect the cathode-directed cell group migration. We observed that control groups had the fastest cathode-migrating cell at the front, while the front cells in PI3K-inhibited groups were the slowest. Both control and PI3K-inhibited groups rapidly repolarized when the electric field direction was reversed, and the group migration continued after the electric field was switched off. Inhibiting myosin disrupted the cohesiveness of keratocyte groups and abolished the collective directionality and ability to switch direction when the electric field is reversed. Our data are consistent with a model according to which cells in the group sense the electric field individually and mechanical integration of the cells results in coherent group motility.
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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) | 0.000 | 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".