Identification of genes expressed in a mesenchymal subset regulating prostate organogenesis using tissue and single cell transcriptomics
Bibliographic record
Abstract
Prostate organogenesis involves epithelial growth controlled by inductive signalling from specialised mesenchymal subsets. To identify pathways active in mesenchyme we used tissue and single cell transcriptomics to define mesenchymal subsets and subset-specific transcript expression. We documented transcript expression using Tag-seq and RNA-seq in female rat Ventral Mesenchymal Pad (VMP) as well as adjacent urethra comprised of smooth muscle and peri-urethral mesenchyme. Transcripts enriched in female VMP were identified with Tag-seq of microdissected tissue, RNA-seq of cell populations, and single cells. We identified 400 transcripts as enriched in the VMP using bio-informatic comparisons of Tag-seq and RNA-seq data, and 44 were confirmed by single cell RNA-seq. Cell subset analysis showed that VMP and adjacent mesenchyme were composed of distinct cell types and that each tissue contained two subgroups. Markers for these subgroups were highly subset specific. Thirteen transcripts were validated by qPCR to confirm cell specific expression in microdissected tissues, as well as expression in neonatal prostate. Immunohistochemical staining demonstrated that Ebf3 and Meis2 showed a restricted expression pattern in female VMP and prostate mesenchyme. We conclude that prostate inductive mesenchyme shows limited cellular heterogeneity and that transcriptomic analysis identified new mesenchymal subset transcripts associated with prostate organogenesis.
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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) | 0.001 | 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".