Comparison of human skin‐ and nerve‐derived Schwann cells reveals many similarities and subtle genomic and functional differences
Bibliographic record
Abstract
Skin is an easily accessible tissue and a rich source of Schwann cells (SCs). Toward potential clinical application of autologous SC therapies, we aim to improve the reliability and specificity of our protocol to obtain SCs from small skin samples. As well, to explore potential functional distinctions between skin-derived SCs (Sk-SCs) and nerve-derived SCs (N-SCs), we used single-cell RNA-sequencing and a series of in vitro and in vivo assays. Our results showed that Sk-SCs expressed typical SC markers. Single-cell sequencing of Sk- and N-SCs revealed an overwhelming overlap in gene expression with the exception of HLA genes which were preferentially up-regulated in Sk-SCs. In vitro, both cell types exhibited similar levels of proliferation, migration, uptake of myelin debris and readily associated with neurites when co-cultured with human iPSC-induced motor neurons. Both exhibited ensheathment of multiple neurites and early phase of myelination, especially in N-SCs. Interestingly, dorsal root ganglion (DRG) neurite outgrowth assay showed substantially more complexed neurite outgrowth in DRGs exposed to Sk-SC conditioned media compared to those from N-SCs. Multiplex ELISA array revealed shared growth factor profiles, but Sk-SCs expressed a higher level of VEGF. Transplantation of Sk- and N-SCs into injured peripheral nerve in nude rats and NOD-SCID mice showed close association of both SCs to regenerating axons. Myelination of rodent axons was observed infrequently by N-SCs, but absent in Sk-SC xenografts. Overall, our results showed that Sk-SCs share near-identical properties to N-SCs but with subtle differences that could potentially enhance their therapeutic utility.
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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 | 0.001 | 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".