Spatiotemporal control of genome engineering in cone photoreceptors
Bibliographic record
Abstract
Abstract Background Cones are essential for color recognition, high resolution, and central vision; therefore cone death causes blindness. Understanding the pathophysiology of each cell type in the retina is key to developing therapies for retinal diseases. However, studying the biology of cone cells in the rod-dominant mammalian retina is particularly challenging. In this study, we used a bacterial artificial chromosome (BAC) recombineering method to knock in the “CreERT2” sequence into the Gnat2 and Arr3 genes, respectively and generated three novel inducible CreERT2 mice with different cone cell specificities. Results These models (Gnat2CreERT2, Arr3T2ACreERT2, and Arr3P2ACreERT2) express temporally controllable Cre recombinase that achieves conditional alleles in cone photoreceptors. Cre-LoxP recombination can be induced as early as postnatal day (PD) two upon tamoxifen injection at varying efficiencies, ranging from 10 to 15% in Gnat2CreERT2, 40% in Arr3T2ACreERT2, and 100% in Arr3P2ACreERT2. Notably, knocking in the P2A-CreERT2 cassette does not affect cone cell morphology and functionality. Most cone-phototransduction enzymes, including Opsins, CNGA3, etc. are not altered except for a reduction in the Arr3 transcript. Conclusions The Arr3P2ACreERT2 mouse, an inducible cone-specific Cre driver, is a valuable line in studying cone cell biology, function, as well as its relationship with rod and other retinal cells. Moreover, the Cre activity can be induced by delivering tamoxifen intragastrically as early as PD2, which will be useful for studying retinal development or in rapid degenerative mouse models.
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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) | 0.001 | 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".