<i>In Vivo</i>Response-Based Identification of Direct Hormone Target Cell Populations Using High-Density Tissue Arrays
Bibliographic record
Abstract
To identify cell populations directly responsive to prolactin (PRL), GH, erythropoietin, or granulocyte-colony stimulating factor within the physiological setting of an intact mammal, we combined in situ detection of hormone-activated signal transducer and activator of transcription (Stat)-5 in rats with high-throughput tissue array analysis using cutting-edge matrix assembly (CEMA). Inducible activation of Stat5a/b, as judged by levels of nuclear-localized, phosphoTyr694/699-Stat5a/b, served as an immediate and sensitive in situ marker of receptor signaling in rat tissues after injection into male and female rats of a single, receptor-saturating dose of hormone for maximal receptor activation. CEMA tissue arrays facilitated analysis of most tissues, including architecturally complex, thin-walled, and stratified tissues such as gut and skin. In 40 tissues analyzed, 35 PRL-responsive and 32 GH-responsive cell types were detected, of which 22 cell types were responsive to both hormones. Interestingly, PRL but not GH activated Stat5 in nearly all of the endocrine glands. In mammary glands, PRL activated Stat5 in a majority of luminal epithelial cells but not myoepithelial cells, stromal fibroblasts, or adipocytes, whereas GH activated Stat5 in a significant fraction of myoepithelial cells, fibroblasts, and adipocytes but only in a minority of luminal cells. Finally, the organism-wide screening revealed a yet-to-be identified erythropoietin-responsive cell type in connective tissue. CEMA tissue arrays provide cost-effective in situ analysis of large numbers of tissues. Biomarker-based identification of cell populations responsive to individual hormones may shed new light on endocrine disease as well as improve understanding of effects and side effects of hormones and drugs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".