Human retinal ganglion cell neurons generated by synchronous BMP inhibition and transcription factor mediated reprogramming
Bibliographic record
Abstract
In optic neuropathies, including glaucoma, retinal ganglion cells (RGCs) die. Cell transplantation and endogenous regeneration offer strategies for retinal repair, however, developmental programs required for this to succeed are incompletely understood. To address this, we explored cellular reprogramming with transcription factor (TF) regulators of RGC development which were integrated into human pluripotent stem cells (PSCs) as inducible gene cassettes. When the pioneer factor NEUROG2 was combined with RGC-expressed TFs (ATOH7, ISL1, and POU4F2) some conversion was observed and when pre-patterned by BMP inhibition, RGC-like induced neurons (RGC-iNs) were generated with high efficiency in just under a week. These exhibited transcriptional profiles that were reminiscent of RGCs and exhibited electrophysiological properties, including AMPA-mediated synaptic transmission. Additionally, we demonstrated that small molecule inhibitors of DLK/LZK and GCK-IV can block neuronal death in two pharmacological axon injury models. Combining developmental patterning with RGC-specific TFs thus provided valuable insight into strategies for cell replacement and neuroprotection.
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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".