Hair Follicle and Sebaceous Gland De Novo Regeneration With Cultured Epidermal Stem Cells and Skin-Derived Precursors
Bibliographic record
Abstract
: Stem cell-based organ regeneration is purported to enable the replacement of impaired organs in the foreseeable future. Here, we demonstrated that a combination of cultured epidermal stem cells (Epi-SCs) derived from the epidermis and skin-derived precursors (SKPs) was capable of reconstituting functional hair follicles and sebaceous glands (SG). When Epi-SCs and SKPs were mixed in a hydrogel and implanted into an excisional wound in nude mice, the Epi-SCs formed de novo epidermis along with hair follicles, and SKPs contributed to dermal papilla in the neogenic hair follicles. Notably, a combination of culture-expanded Epi-SCs and SKPs derived from the adult human scalp were sufficient to generate hair follicles and hair. Bone morphogenetic protein 4, but not Wnts, sustained the expression of alkaline phosphatase in SKPs in vitro and the hair follicle-inductive property in vivo when SKPs were engrafted with neonatal epidermal cells into excisional wounds. In addition, Epi-SCs were capable of differentiating into sebocytes and formed de novo SGs, which excreted lipids as do normal SGs. Thus our results indicate that cultured Epi-SCs and SKPs are sufficient to generate de novo hair follicles and SGs, implying great potential to develop novel bioengineered skin substitutes with appendage genesis capacity. SIGNIFICANCE: In postpartum humans, skin appendages lost in injury are not regenerated, despite the considerable achievement made in skin bioengineering. In this study, transplantation of a combination of culture-expanded epidermal stem cells and skin-derived progenitors from mice and adult humans led to de novo regeneration of functional hair follicles and sebaceous glands. The data provide transferable knowledge for the development of novel bioengineered skin substitutes with epidermal appendage regeneration capacity.
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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) | 0.001 | 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 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".