Human-Specific Organization of Proliferation and Stemness in Squamous Epithelia: A Comparative Study to Elucidate Differences in Stem Cell Organization
Bibliographic record
Abstract
The mechanisms that influence human longevity are complex and operate on cellular, tissue, and organismal levels. To better understand the tissue-level mechanisms, we compared the organization of cell proliferation, differentiation, and cytoprotective protein expression in the squamous epithelium of the esophagus between mammals with varying lifespans. Humans are the only species with a quiescent basal stem cell layer that is distinctly physically separated from parabasal transit-amplifying cells. In addition to these stark differences in the organization of proliferation, human squamous epithelial stem cells express DNA repair-related markers, such as MECP2 and XPC, which are absent or low in mouse basal cells. Furthermore, we investigated whether the transition from basal to suprabasal is different between species. In humans, the parabasal cells seem to originate from cells detaching from the basement membrane, and these can already begin to proliferate while delaminating. In most other species, delaminating cells have been rare or their proliferation rate is different from that of their human counterparts, indicating an alternative mode of how stem cells maintain the tissue. In humans, the combination of an elevated cytoprotective signature and novel tissue organization may enhance resistance to aging and prevent cancer. Our results point to enhanced cellular cytoprotection and a tissue architecture which separates stemness and proliferation. These are both potential factors contributing to the increased fitness of human squamous epithelia to support longevity by suppressing tumorigenesis. However, the organization of canine oral mucosa shows some similarities to that of human tissue and may provide a useful model to understand the relationship between tissue architecture, gene expression regulation, tumor suppression, and longevity.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".