Stochastic single cell migration leads to robust horizontal cell layer formation in the vertebrate retina
Bibliographic record
Abstract
ABSTRACT Developmental programs that arrange cells and tissues into patterned organs are remarkably robust. In the developing vertebrate retina, for example, neurons reproducibly assemble into distinct layers giving the mature organ its overall structured appearance. This stereotypic neuronal arrangement, termed lamination, is important for efficient neuronal connectivity. Although retinal lamination is conserved in many vertebrates, including humans, how it emerges from single cell behaviour is not fully understood. To shed light on this issue, we here investigated the formation of the retinal horizontal cell layer. Using in vivo light sheet imaging of the developing zebrafish retina, we generated a comprehensive quantitative analysis of horizontal single cell behaviour from birth to final positioning. Interestingly, we find that all parameters analysed, including cell cycle dynamics, migration paths and kinetics, as well as sister cell dispersal, are very heterogeneous. Thus, horizontal cells show individual non-stereotypic behaviour before final positioning. Yet these initially variable cell dynamics always generate the correct laminar pattern. Consequently, our data show that the extent of single cell stochasticity in the lamination of the vertebrate retina is underexplored.
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 | 0.000 |
| 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".