Purification and Characterization of Schwann Cells from Adult Human Skin and Nerve
Bibliographic record
Abstract
Abstract Despite its modest capacity for regeneration, peripheral nervous system injury often results in significant long-term disability. Supplementing peripheral nervous system injury with autologous Schwann cells (SCs) may serve to rejuvenate the postinjury environment to enhance regeneration and ultimately improve functional outcomes. However, human nerve-derived SC (hN-SC) collection procedures require invasive surgical resection. Here, we describe the characterization of SCs from adult human skin (hSk-SCs) of four male donors ranging between 27 and 46 years old. Within five weeks of isolating and culturing adherent mixed skin cells, we were able to obtain 3–5 million purified SCs. We found that hSk-SCs appeared transcriptionally indistinguishable from hN-SCs with both populations exhibiting expression of SC genes including:SOX10,SOX9,AP2A1,CDH19,EGR1,ETV5,PAX3,SOX2,CX32,DHH,NECL4,NFATC4,POU3F1,S100B, andYY1. Phenotypic analysis of hSk-SCs and hN-SCs cultures revealed highly enriched populations of SCs indicated by the high percentage of NES+ve, SOX10+ve, s100+veand p75+vecells, as well as the expression of a battery of other SC-associated proteins (PAX3, CDH19, ETV5, SOX2, POU3F1, S100B, EGR2, and YY1). We further show that both hSk-SCs and hN-SCs are capable of promoting axonal growth to similar degrees and that a subset of both associate with regenerating axons and form myelin following transplantation into the injured mouse sciatic nerve. Interestingly, although the majority of both hSk-SCs and hN-SCs maintained SOX10 immunoreactivity following transplant, only a subset of each activated the promyelinating factor, POU3F1, and were able to myelinate. Taken together, we demonstrate that adult hSk-SCs are genetically and phenotypically indistinguishable to hN-SCs.
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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) | 0.002 | 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".