Müller Glia maintain their regenerative potential despite degeneration in the aged zebrafish retina
Bibliographic record
Abstract
ABSTRACT Ageing is a significant risk factor for degeneration of the retina. Harnessing the regenerative potential of Müller glia cells (MG) in the retina offers great promise for the treatment of blinding conditions. Yet, the impact of ageing on MG regenerative capacity has not yet been considered. Here we show that the zebrafish retina undergoes telomerase-independent age-related neurodegeneration. Yet, this progressive neuronal loss in the ageing retina is insufficient to stimulate the MG regenerative response. Instead, age-related neurodegeneration leads to MG gliosis and loss of vision, similarly to humans. Nevertheless, gliotic MG cells retain Yap expression and the ability to regenerate neurons after acute light damage. Therefore, we identify key differences in the MG response to acute versus chronic damage in the zebrafish retina and show that aged gliotic MG can be stimulated to repair damaged neurons in old age. SUMMARY Our data suggest there are key differences between mechanisms driving regeneration in response to acute damage versus age-related chronic damage. It may be that either the number of cells dying in natural ageing is not enough to stimulate MG to proliferate, or the low number of microglia and respective signals released are not sufficient to trigger MG proliferation. Importantly, we show that gliotic MG cells can be stimulated to repair damaged neurons in old zebrafish retina.
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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 | 0.001 |
| 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".