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Record W3157693110 · doi:10.1038/s42003-021-02045-w

Transcriptional heterogeneity of stemness phenotypes in the ovarian epithelium

2021· article· en· W3157693110 on OpenAlexafffund
Lauren E. Carter, David P. Cook, Curtis W. McCloskey, Mélanie Grondin, David A. Landry, Tiffany Dang, Olga Collins, Lisa F. Gamwell, Holly A. Dempster, Barbara C. Vanderhyden

Bibliographic record

VenueCommunications Biology · 2021
Typearticle
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersCanadian Institutes of Health ResearchGénome QuébecMcGill University
KeywordsBiologyStem cellCell biologySNAI1Mesenchymal stem cellPopulationTranscription factorEpitheliumPhenotypeEpithelial–mesenchymal transitionGeneticsDownregulation and upregulationGeneMedicine

Abstract

fetched live from OpenAlex

The ovarian surface epithelium (OSE) is a monolayer of epithelial cells surrounding the ovary that ruptures during each ovulation to allow release of the oocyte. This wound is quickly repaired, but mechanisms promoting repair are poorly understood. The contribution of tissue-resident stem cells in the homeostasis of several epithelial tissues is widely accepted, but their involvement in OSE is unclear. We show that traits associated with stem cells can be increased following exposure to the cytokine TGFB1, overexpression of the transcription factor Snai1, or deletion of Brca1. We find that stemness is often linked to mesenchymal-associated gene expression and higher activation of ERK signalling, but is not consistently dependent on their activation. Expression profiles of these populations are extremely context specific, suggesting that stemness may not be associated with a single, distinct population, but rather is a heterogeneous cell state that may emerge from diverse environmental cues. These findings support that the OSE may not require distinct stem cells for long-term maintenance, and may instead achieve this through transient dedifferentiation into a stem-like state.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.179

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.092
GPT teacher head0.359
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2021
Admission routes2
Has abstractyes

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