Direct Genesis of Functional Rodent and Human Schwann Cells from Skin Mesenchymal Precursors
Bibliographic record
Abstract
Recent reports of directed reprogramming have raised questions about the stability of cell lineages. Here, we have addressed this issue, focusing upon skin-derived precursors (SKPs), a dermally derived precursor cell. We show by lineage tracing that murine SKPs from dorsal skin originate from mesenchymal and not neural crest-derived cells. These mesenchymally derived SKPs can, without genetic manipulation, generate functional Schwann cells, a neural crest cell type, and are highly similar at the transcriptional level to Schwann cells isolated from the peripheral nerve. This is not a mouse-specific phenomenon, since human SKPs that are highly similar at the transcriptome level can be made from neural crest-derived facial and mesodermally derived foreskin dermis and the foreskin SKPs can make myelinating Schwann cells. Thus, nonneural crest-derived mesenchymal precursors can differentiate into bona fide peripheral glia in the absence of genetic manipulation, suggesting that developmentally defined lineage boundaries are more flexible than widely thought.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 0.001 |
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".