Evaluation of in vitro spermatogenesis system effectiveness to study genes behavior: monitoring the expression of the testis specific 10 (Tsga10) gene as a model.
Bibliographic record
Abstract
BACKGROUND: In vitro generation of germ cells introduces a novel approach to male infertility and provides an effective system in gene tracking studies, however many aspects of this process have remained unclear. We aimed to promote mouse embryonic stem cells (mESC) differentiation into germ cells and evaluate its effectiveness with tracking the expression of the Tsga10 during this process. METHODS: mESCs were differentiated into germ cells in the presence of Retinoic Acid. Based on developmental schedule of the postnatal testis, samples were taken on the 7th, 12th, and 25th days of the culture and were subjected to expression analysis of a panel of germ cell specific genes. Expression of Tsga10 in RNA and protein levels was then analyzed. RESULTS: Transition from mitosis to meiosis occurred between 7th and 12th days of mESC culture and post-meiotic gene expression did not occur until the 25th day of the culture. Results showed low level of Tsga10expression in undifferentiated stem cells. During transition from meiotic to post-meiotic phase, Tsga10 expression increased in 6.6 folds. This finding is in concordance with in vivo changes during transition from pre-pubertal to pubertal stage. Localization of processed and unprocessed forms of the related protein was similar to those in vivo as well. CONCLUSIONS: Expression pattern of Tsga10, as a gene with critical function in spermatogenesis, is similar during in vitro and in vivo germ cell generation. The results suggest that in vitro derived germ cells could be a trusted model to study genes behavior during spermatogenesis.
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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".