Notch signaling is a driver of glandular stem cell activity and regenerative migration after damage
Bibliographic record
Abstract
Organoid models have significantly enhanced our understanding of adult stem cell function, however, uncovering regulatory mechanisms governing rare and often quiescent stem cells in glandular organs remains challenging. Here, we employ an integrative multi-omics approach, combining single-cell RNA sequencing, bulk ATAC and RNA sequencing, to profile the cellular populations and signaling pathways characterizing a mouse salivary gland organoid model across different temporal stages and after radiation-induced damage. Our findings identify Sox9- and Itgb1/Cd44-expressing cells as primitive adult stem/progenitor populations with a critical migratory role in tissue repair. Notch signaling is a key driver of self-renewal and migration in response to irradiation. Additionally, scRNA-seq analysis of irradiated salivary gland tissue confirms these findings in an in vivo setting. Extending these findings to murine and patient-derived salivary, mammary and thyroid gland organoids, we reveal the conserved role of Notch signaling in coordinating stem/progenitor cell-mediated regeneration across glandular tissues. These insights position Notch signaling as a central regulator of glandular stem cell-like populations and as a promising therapeutic target for enhancing glandular tissue regeneration following cancer therapies.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".