Postnatal development of mouse spermatogonial stem cells as determined by immunophenotype, regenerative capacity, and long-term culture-initiating ability: a model for practical applications
Bibliographic record
Abstract
Spermatogonial stem cells (SSCs) are the foundation of life-long spermatogenesis. While SSC research has advanced greatly over the past two decades, characterization of SSCs during postnatal development has not been well documented. Using the mouse as a model, in this study, we defined the immunophenotypic profiles of testis cells during the course of postnatal development using multi-parameter flow cytometry with up to five cell-surface antigens. We found that the profiles progress over time in a manner specific to developmental stages. We then isolated multiple cell fractions at different developmental stages using fluorescent-activated cell sorting (FACS) and identified specific cell populations with prominent capacities to regenerate spermatogenesis upon transplantation and to initiate long-term SSC culture. The data indicated that the cell fraction with the highest level of regeneration capacity exhibited the most prominent potential to initiate SSC culture, regardless of age. Interestingly, refinement of cell fractionation using GFRA1 and KIT did not lead to further enrichment of regenerative and culture-initiating stem cells, suggesting that when a high degree of SSC enrichment is achieved, standard markers of SSC self-renewal or commitment may lose their effectiveness to distinguish cells at the stem cell state from committed progenitors. This study provides a significant information resource for future studies and practical applications of mammalian 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".