Combinatorial expression of glial transcription factors induces Schwann cell‐specific gene expression in mouse embryonic fibroblasts
Bibliographic record
Abstract
Abstract Background Schwann cells provide peripheral nerve trophic support, myelinate axons, and assist in repair. However, Schwann cell repair capacity is limited by chronic injury, disease, and aging. Schwann cell reprogramming is a cellular conversion strategy that could provide a renewable cell supply to repair injured nerves. Here, we developed a plasmid‐based approach to test the Schwann cell conversion potential of four glial transcription factors. Results We employed four transcription factors implicated in Schwann cell differentiation and repair: Sox10, Sox2, Jun, and Pax3. Expression vectors were generated for Sox10 alone and two triple transcription factor combinations: Jun‐Pax3‐Sox2 (triple 1, T1) and Sox10‐Jun‐Sox2 (triple 2, T2). Mouse embryonic fibroblasts (MEFs) were transfected with these vectors, transferred to glial inductive media, and Schwann cell‐marker expression was in assessed by immunostaining, flow cytometry, and qPCR. All expression vectors repressed fibroblast‐specific gene expression. However, T2 was most efficient at generating O4 + Schwann cell‐like cells, which had some capacity to myelinate denervated axons from explanted dorsal root ganglia. In comparison, T1 more efficiently induced repair Schwann cell‐marker expression in converted O4 + cells. Conclusions T1 and T2 convert MEFs to Schwann cells with different efficacies and gene expression profiles, and may provide cell‐based therapies for peripheral nerve repair.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".