Transcriptional heterogeneity of stemness phenotypes in the ovarian epithelium
Bibliographic record
Abstract
Abstract The ovarian surface epithelium (OSE) is a monolayer of epithelial cells covering the surface of the ovary. During ovulation, the OSE is ruptured to allow release of the oocyte. This wound is quickly repaired, but mechanisms of this repair are poorly understood. The contribution of tissue-resident stem cells in the homeostasis of several epithelial tissues is widely accepted, such as the intestinal epithelium, airway epithelium, and skin, but their involvement in OSE maintenance is unclear. While putative stem cell populations in the OSE have been described, how they are regulated is poorly defined. We show that traits associated with stem cells (stemness) can be increased in OSE following exposure to the cytokine TGFB1, overexpression of the transcription factor Snai1 , or deletion of Brca1 . By assessing the gene expression profiles of these populations, we show that stemness is often linked to mesenchymal-associated gene expression and higher activation of ERK signalling, but it is not consistently dependent on their activation. Expression profiles of these populations are extremely context specific, suggesting that stemness may not correspond to a single, distinct population, but rather is a heterogenous state that can possibly emerge from diverse environmental cues. Together, these findings support that the OSE may not require distinct stem cell populations for long-term maintenance, and may achieve this through transient dedifferentiation into a stem-like state.
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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) | 0.002 | 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".