Human skin and nerve derived Schwann cells exhibit subtle transcriptomic and functional differences
Bibliographic record
Abstract
Schwann cells (SCs) support peripheral nerve regeneration, but the efficacy of this regeneration is limited. Introducing exogenous SCs to the site of peripheral nerve injury is an ongoing avenue of investigation as a cellular therapy to enhance regeneration. Approaches that utilize an accessible source of a patient's own cells would greatly facilitate clinical translation. Previous work demonstrates that skin-derived SCs are able to promote axonal growth and remyelination in murine models of nerve injury, however, it is not clear whether skin-derived and nerve-derived SC are functionally equivalent. To this end, we isolated SCs from small skin samples and then subjected them to high resolution single-cell mRNA sequencing (scRNA-seq) followed by battery of in vitro and in vivo assays. Our genomic analyses revealed close to 95% similarity between skin and nerve SCs at differential gene level while gene network analysis showed mostly overlapping profiles between the two cell types with the exception of immune regulatory family (eg. IRF) upregulated in skin SCs. In vitro assays revealed similarity in proliferation, migration and expression of epidermal growth factor between the two cell types, which is in alignment with our genomic analysis. However, we observed subtle difference such as higher expression of VEGF and collagen content in skin SCs and higher expression of TGF-alpha in nerve SC. Overall, our results showed that skin and nerve Schwann cells share mostly identical properties with subtle differences, suggesting that skin may be a viable source of Schwann cells to improve nerve repair.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".