Laser-Based Single-Axon Transection for High-Content Axon Injury and Regeneration Studies
Bibliographic record
Abstract
The investigation of the regenerative response of the neurons to axonal injury is essential to the development of new axoprotective therapies. Here we study the retinal neuronal RGC-5 cell line after laser transection, demonstrating that the ability of these cells to initiate a regenerative response correlates with axon length and cell motility after injury. We show that low energy picosecond laser pulses can achieve transection of unlabeled single axons in vitro and precisely induce damage with micron precision. We established the conditions to achieve axon transection, and characterized RGC-5 axon regeneration and cell body response using time-lapse microscopy. We developed an algorithm to analyze cell trajectories and established correlations between cell motility after injury, axon length, and the initiation of the regeneration response. The characterization of the motile response of axotomized RGC-5 cells showed that cells that were capable of repair or regrowth of damaged axons migrated more slowly than cells that could not. Moreover, we established that RGC-5 cells with long axons could not recover their injured axons, and such cells were much more motile. The platform we describe allows highly controlled axonal damage with subcellular resolution and the performance of high-content screening in cell cultures.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".