Establishment of a Short-Term In Vitro Assay for Mouse Spermatogonial Stem Cells1
Bibliographic record
Abstract
Spermatogonial stem cells (SSCs) are responsible for life-long, daily production of male gametes and for the transmission of genetic information to the next generation. Unequivocal detection of SSCs has relied on spermatogonial transplantation, in which functional SSCs are analyzed qualitatively and quantitatively based on their regenerative capacity. However, this technique has some significant limitations. For example, it is a time-consuming procedure, as data acquisition requires at least 8 weeks after transplantation. It is also laborious, requiring microinjection of target cells into the seminiferous tubules of individual testes. Donor-recipient immunocompatibility for successful transplantation and large variations in data obtained represent further limitations of this technique. In the present study, we provide evidence that a recently developed SSC culture system can be employed as a reliable, short-term in vitro assay for SSCs. In this system, donor cells generate three-dimensional structures of aggregated germ cells (clusters) in vitro within 6 days. We show that each cluster originates from a single cell. Thus, by counting the clusters, cluster-forming cells can be quantified. We observed a strong linear correlation between the numbers of clusters and SSCs over extended culture periods. Therefore, cluster numbers faithfully reflect SSC numbers. These results indicate that by simply counting the number of clusters, functional SSCs can be readily detected within 1 week in a semi-quantitative manner. The faithfulness of this in vitro assay to the transplantation assay was further confirmed under two experimental situations. This in vitro cluster formation assay provides a reliable short-term technique to detect SSCs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".