Amoeboid-like neuronal migration ensures correct horizontal cell layer formation in the developing vertebrate retina
Bibliographic record
Abstract
ABSTRACT As neurons are often born at positions different than where they ultimately function, neuronal migration is key to ensure successful nervous system development. Radial migration during which neurons featuring unipolar and bipolar morphology, employ pre-existing processes or underlying cells for directional guidance, is the most well explored neuronal migration mode. However, how neurons that display multipolar morphology, without such processes, move through highly crowded tissue environments towards their final positions remains elusive. To understand this, we here investigated multipolar migration of horizontal cells in the zebrafish retina. We found that horizontal cells tailor their movements to the environmental spatial constraints of the crowded retina, by featuring several characteristics of amoeboid migration. These include cell and nucleus shape changes, and persistent rearward polarization of stable F-actin, which enable horizontal cells to successfully move through the crowded retina. Interference with the organization of the developing retina by changing nuclear properties or overall tissue architecture, hampers efficient horizontal cell migration and layer formation. Thus, cell-tissue interplay is crucial for efficient migration of horizontal cells in the retina. In view of high proportion of multipolar neurons, the here uncovered ameboid-like neuronal migration mode might also be crucial in other areas of the developing brain.
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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".