The Mouse Epididymal Transcriptome: Transcriptional Profiling of Segmental Gene Expression in the Epididymis1
Bibliographic record
Abstract
Maturation of spermatozoa, including the acquisition of motility and the ability to undergo capacitation, occurs during transit through the dynamic environment of the epididymis. The microenvironments created along the length of the epididymal tubule are essential to the molecular modifications of spermatozoa that result in fertile gametes. The secretory and resorptive processes of the epithelial cells that line this tubule generate these microenvironments. In the current study, 10 morphologically distinct segments of the mouse epididymis were identified by microdissection. We hypothesized that the changing environments of the epididymal lumen are established by differential gene expression among these segments. RNA isolated from each of the 10 segments was analyzed by microarray analysis. More than 17,000 genes are expressed in the mouse epididymis, compared with about 12,000 genes identified from whole epididymal samples. Screening a panel of normal mouse tissues identified both epididymal-selective and epididymal-specific transcripts. In addition, this study identified 2168 genes that are up-regulated or down-regulated by greater than 4-fold between at least two different segments. The expression patterns of these genes identify distinct patterns of segmental regulation. Using principal component analysis, we determined that the 10 segments form 6 different transcriptional units. These analyses elucidate the changes in gene expression along the length of the epididymis for 17,000 expressed transcripts and provide a powerful resource for the research community in future studies of the biological factors that mediate epididymal sperm maturation.
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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 | 0.001 | 0.001 |
| 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".