Evidence for a multipotent mammary progenitor with pregnancy-specific activity
Bibliographic record
Abstract
INTRODUCTION: The mouse mammary gland provides a powerful model system for studying processes involved in epithelial tissue development. Although markers that enrich for mammary stem cells and progenitors have been identified, our understanding of the mammary developmental hierarchy remains incomplete. METHODS: We used the MMTV promoter linked to the reverse tetracycline transactivator to induce H2BGFP expression in the mouse mammary gland. Mammary epithelial cells (MECs) from virgin mice were sorted by flow cytometry for expression of the mammary stem cell/progenitor markers CD24 and CD29, and H2BGFP. Sorted populations were analyzed for in vivo repopulation ability, expression of mammary lineage markers, and differential gene expression. RESULTS: The reconstituting activity of CD24⁺/CD29⁺ cells in cleared fat pad transplantation assays was not distinguished in GFP⁺ compared to GFP⁻ subpopulations. However, within the CD24⁺/CD29(lo) luminal progenitor-enriched population, H2BGFP⁺, but not H2BGFP⁻, MECs formed mammary structures in transplantation assays; moreover, this activity was dramatically enhanced in pregnant recipients. These outgrowths contained luminal and myoepithelial mammary lineages and produced milk, but lacked the capacity for serial transplantation. Transcriptional microarray analysis revealed that H2BGFP⁺/CD24⁺/CD29(lo) MECs are distinct from H2BGFP⁻/CD24⁺/CD29(lo) MECs and enriched for gene expression signatures with both the stem cell (CD24⁺/CD29⁺) and luminal progenitor (CD24⁺/CD29(lo)/CD61⁺) compartments. CONCLUSIONS: We have identified a population of MECs containing pregnancy-activated multipotent progenitors that are present in the virgin mammary gland and contribute to the expansion of the mammary gland during pregnancy.
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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 | 0.001 |
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".