Temporal Progression of Retinal Progenitor Cell Identity: Implications in Cell Replacement Therapies
Bibliographic record
Abstract
Retinal degenerative diseases, which lead to the death of rod and cone photoreceptor cells, are the leading cause of inherited vision loss worldwide. Induced pluripotent or embryonic stem cells (iPSCs/ESCs) have been proposed as a possible source of new photoreceptors to restore vision in these conditions. The proof of concept studies carried out in mouse models of retinal degeneration over the past decade have highlighted several limitations for cell replacement in the retina, such as the low efficiency of cone photoreceptor production from stem cell cultures and the poor integration of grafted cells in the host retina. Current protocols to generate photoreceptors from stem cells are largely based on the use of extracellular factors. Although these factors are essential to induce the retinal progenitor cell (RPC) fate from iPSCs/ESCs, developmental studies have shown that RPCs alter fate output as a function of time (i.e., their temporal identity) to generate the seven major classes of retinal cell types, rather than spatial position. Surprisingly, current stem cell differentiation protocols largely ignore the intrinsic temporal identity of dividing RPCs, which we argue likely explains the low efficiency of cone production in such cultures. In this article, we briefly review the mechanisms regulating temporal identity in RPCs and discuss how they could be exploited to improve cone photoreceptor production for cell replacement therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".