A monitoring system for axonal growth dynamics using micropatterns of permissive and Semaphorin 3F chemorepulsive signals
Bibliographic record
Abstract
Neurons reach their correct targets by directional outgrowth of axons, which is mediated by attractive or repulsive cues. Growing axons occasionally cross a field of repulsive cues and stop at intermediate targets on the journey to their final destination. However, it is not well-understood how individual growth cones make decisions, and pass through repulsive territory to reach their permissive target regions. We developed a microcontact printing culture system that could trap individual axonal tips in a permissive dot area surrounded by the repulsive signal, semaphorin 3F (Sema3F). Axons of rat hippocampal neurons on the Sema3F/PLL dot array extended in the checkboard pattern with a significantly slow growth rate. The detailed analysis of the behaviors of axonal growth cones revealed the saccadic dynamics in the dot array system. The trapped axonal tips in the permissive area underwent growth cone enlargement with remarkably spiky filopodia, promoting their escape from the Sema3F constraints with straight extension of axons. This structured axonal growth on the dot pattern was disrupted by increased inter-dot distance, or perturbing intracellular signaling machineries. These data indicate that axons grow against repulsive signals by jumping over the repulsive cues, depending on contact signals and intracellular milieu. Our study suggests that our dot array culture system can be used as a screening system to easily and efficiently evaluate ECM or small molecule inhibitors interfering growth cone dynamics leading to controlling axonal growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".