Identification of a molecular marker for type A spermatogonia by microarray analysis using gonadal cells from p<i>vasa</i>‐<i>GFP</i> transgenic rainbow trout (<i>Oncorhynchus mykiss</i>)
Bibliographic record
Abstract
The spermatogonia of fish can be classified as being either undifferentiated type A spermatogonia or differentiated type B spermatogonia. Although type A spermatogonia, which contain spermatogonial stem cells, have been demonstrated to be a suitable material for germ cell transplantation, no molecular markers for distinguishing between type A and type B spermatogonia in fish have been developed to date. We therefore sought to develop a molecular marker for type A spermatogonia in rainbow trout. Using GFP-dependent flow cytometry (FCM), enriched fractions of type A and type B spermatogonia, testicular somatic cells, and primordial germ cells were prepared from rainbow trout possessing the green fluorescent protein (GFP) gene driven by trout vasa regulatory regions (pvasa-GFP rainbow trout). The gene-expression profiles of each cell fraction were then compared with a microarray containing cDNAs representing 16,006 genes from several salmonid species. Genes exhibiting high expression for type A spermatogonia relative to above-mentioned other types of gonadal cells were identified and subjected to RT-PCR and quatitative PCR analysis. Since only the rainbow trout notch1 homologue showed significantly high expression in the type A spermatogonia-enriched fraction, we propose that notch1 may be a useful molecular marker for type A spermatogonia. The combination of GFP-dependent FCM and microarray analysis of pvasa-GFP rainbow trout can therefore be applied to the identification of potentially useful molecular markers of germ cells in fish.
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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".