<i>NRL</i> −/− gene edited human embryonic stem cells generate rod-deficient retinal organoids enriched in S-cone-like photoreceptors
Bibliographic record
Abstract
Abstract Organoid cultures represent a unique tool to investigate the developmental complexity of tissues like the human retina. NRL is a transcription factor required for the specification and homeostasis of mammalian rod photoreceptors. In Nrl-deficient mice, photoreceptor precursor cells do not differentiate into rods, and instead follow a default photoreceptor specification pathway to generate S-cone-like cells. To investigate whether this genetic switch mechanism is conserved in humans, we used CRISPR/Cas9 gene editing to engineer an NRL-deficient embryonic stem cell (ESC) line (NRL−/−), and differentiated it into retinal organoids. Retinal organoids self-organize and resemble embryonic optic vesicles (OVs) that recapitulate the natural histogenesis of rods and cone photoreceptors. NRL−/− OVs develop comparably to controls, and exhibit a laminated, organized retinal structure with markers of photoreceptor synaptogenesis. Using immunohistochemistry and quantitative polymerase chain reaction (qPCR), we observed that NRL−/− OVs do not express NRL, or other rod photoreceptor markers directly or indirectly regulated by NRL. On the contrary, they show an abnormal number of photoreceptors positive for S-OPSIN, which define a primordial subtype of cone, and overexpress other cone genes indicating a conserved molecular switch in mammals. This study represents the first evidence in a human in vitro ESC-derived organoid system that NRL is required to define rod identity, and that in its absence S-cone-like cells develop as the default photoreceptor cell type. It shows how gene edited retinal organoids provide a useful system to investigate human photoreceptor specification, relevant for efforts to generate cells for transplantation in retinal degenerative diseases.
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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 | 0.000 |
| 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".