Evidence from the stop‐EGFP mouse supports a niche‐sharing model of epidermal proliferative units
Bibliographic record
Abstract
The classical model of epidermal proliferative units (EPUs) postulates that each EPU is composed of a single column of corneocytes plus epidermal cells directly below the column and is maintained by a single stem cell within the unit. Using the stop-enhanced green fluorescent protein (stop-EGFP) transgenic mouse system, we previously showed epidermal stem cell clonal lineages could produce multiple adjacent corneocytes (i.e. epidermal cells belonging to multiple adjacent EPUs), contradicting the classical EPU model. One possible problem with our earlier study was that N-ethyl-N-nitrosourea (ENU) was used to generate mutations for clonal analysis. This could alter the normal environment of the epidermal tissue and might lead to an artificial expansion of stem cell clonal lineages. In this study, we replicate our earlier findings using untreated stop-EGFP mice and relying on spontaneous mutations to generate clonal cell lineages. We propose an alternative to the classical EPU model to explain the dynamic nature of epidermal proliferation. Our niche-sharing model of EPUs allows epidermal cells to horizontally migrate among EPUs, so that multiple stem cells cooperatively maintain a larger proliferative compartment.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".