In vivo imaging of adeno-associated viral vector labelled retinal ganglion cells
Bibliographic record
Abstract
A defining characteristic of optic neuropathies, such as glaucoma, is progressive loss of retinal ganglion cells (RGCs). Current clinical tests only provide weak surrogates of RGC loss, but the possibility of optically visualizing RGCs and quantifying their rate of loss could represent a radical advance in the management of optic neuropathies. In this study we injected two different adeno-associated viral (AAV) vector serotypes in the vitreous to enable green fluorescent protein (GFP) labelling of RGCs in wild-type mice for in vivo and non-invasive imaging. GFP-labelled cells were detected by confocal scanning laser ophthalmoscopy 1-week post-injection and plateaued in density at 4 weeks. Immunohistochemical analysis 5-weeks post-injection revealed labelling specificity to RGCs to be significantly higher with the AAV2-DCX-GFP vector compared to the AAV2-CAG-GFP vector. There were no adverse functional or structural effects of the labelling method as determined with electroretinography and optical coherence tomography, respectively. The RGC-specific positive and negative scotopic threshold responses had similar amplitudes between control and experimental eyes, while inner retinal thickness was also unchanged after injection. As a positive control experiment, optic nerve transection resulted in a progressive loss of labelled RGCs. AAV vectors provide strong and long-lasting GFP labelling of RGCs without detectable adverse effects.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".