Vibrissa hair bulge houses two populations of skin epithelial stem cells distinct by their keratin profile
Bibliographic record
Abstract
Defining the properties of postnatal stem cells is of interest given their relevance for tissue homeostasis and therapeutic applications, such as skin tissue engineering for burn patients. In hair follicles, the bulge region of the outer root sheath houses stem cells. We show that explants from the prominent bulge area, but not the bulb, in rodent vibrissa follicles can produce epidermis in a skin model of tissue engineering. Using morphological criteria and keratin expression, we typified epithelial stem cells of vibrissa bulge. Two types of slow-cycling cells (Bb, Bs1) featuring a high colony-forming capacity occur in the bulge. Bb cells are located in the outermost basal layer, express K5, K15, K17, and K19, and feature a loosely organized keratin network. Bs1 cells localize to the suprabasal layers proximal to Bb cells and express K5/K17, correlating with a network of densely bundled filaments. These prominent bundles are missing in K17-null mice, which lack vibrissa. Atypically, both the Bb and Bs1 keratinocytes lack K14 expression. These findings show heterogeneity within the hair follicle stem cell repository, establish that a subset of slow-cycling cells are suprabasal in location, and point to a special role for K5/K17 filaments in a newly defined subset of stem cells. Our results are discussed in the context of long-term survival of engineered tissues after grafting that requires the presence of stem cells.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".