Transplanted human photoreceptors transfer cytoplasmic material but not to the recipient mouse retina
Bibliographic record
Abstract
BACKGROUND: The discovery of material transfer between transplanted and host mouse photoreceptors has expanded the possibilities for utilizing transplanted photoreceptors as potential vehicles for delivering therapeutic cargo. However, previous research has not directly explored the capacity for human photoreceptors to engage in material transfer, as human photoreceptor transplantation has primarily been investigated in rodent models of late-stage retinal disease, which lack host photoreceptors. METHODS: In this study, we transplanted human stem-cell derived photoreceptors purified from human retinal organoids at different ontological ages (weeks 10, 14, or 20) into mouse models with intact photoreceptors and assessed transfer of human proteins and organelles to mouse photoreceptors. RESULTS: Unexpectedly, regardless of donor age or mouse recipient background, human photoreceptors did not transfer material in the mouse retina, though a rare subset of donor cells (< 5%) integrated into the mouse photoreceptor cell layer. To investigate the possibility that a species barrier impeded transfer, we used a flow cytometric assay to examine material transfer in vitro. Interestingly, dissociated human photoreceptors transferred fluorescent protein with each other in vitro, yet no transfer was detected in co-cultures of human and mouse photoreceptors, suggesting that material transfer is species specific. CONCLUSIONS: While xenograft models are not a tractable system to study material transfer of human photoreceptors, these findings demonstrate that human retinal organoid-derived photoreceptors are competent donors for material transfer and thus may be useful to treat retinal degenerative disease.
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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 | 0.001 | 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) | 0.003 | 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".