Skin-Derived Precursors Differentiate Into Skeletogenic Cell Types and Contribute to Bone Repair
Bibliographic record
Abstract
Skin-derived precursors (SKPs) are multipotent dermal precursors that share similarities with neural crest stem cells and that can give rise to peripheral neural and some mesodermal cell types, such as adipocytes. Here, we have asked whether rodent or human SKPs can generate other mesenchymally derived cell types, with a particular focus on osteocytes and chondrocytes. In culture, rodent and human foreskin-derived SKPs differentiated into alkaline-positive, collagen type-1-positive, mineralizing osteocytes, and into collagen type-II-positive chondrocytes that secreted chondrocyte-specific proteoglycans. Clonal analysis demonstrated that SKPs efficiently generated these skeletogenic cell types, and that they were multipotent with regard to the osteogenic and chondrogenic lineages. To ask if SKPs could generate these same lineages in vivo, genetically tagged, undifferentiated rat SKPs were transplanted into a tibial bone fracture model. Over the ensuing 6 weeks, many of the transplanted cells survived within the bone callus, where they were morphologically and phenotypically similar to the endogenous mesenchymal/osteogenic cells. Moreover, some transplanted cells adopted a mature osteocyte phenotype and integrated into the newly formed bone. Some transplanted cells also differentiated into chondrocytes and into smooth muscle cells and/or pericytes that were associated with blood vessels. Thus, both rodent and human SKPs generate skeletogenic cell types in culture, and the injured bone environment is sufficient to instruct SKPs to differentiate down an osteogenic lineage, in a fashion similar to the endogenous mesenchymal precursors.
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) | 0.002 | 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".