Stirred suspension bioreactors as a novel method to enrich germ cells from pre-pubertal pig testis
Bibliographic record
Abstract
To study spermatogonial stem cells the heterogeneous testicular cell population first needs to be enriched for undifferentiated spermatogonia, which contain the stem cell population. When working with non-rodent models, this step requires working with large numbers of cells. Available cell separation methods rely on differential properties of testicular cell types such as expression of specific cell surface proteins, size, density, or differential adhesion to substrates to separate germ cells from somatic cells. The objective of this study was to develop an approach that allowed germ cell enrichment while providing efficiency of handling large cell numbers. Here, we report the use of stirred suspension bioreactors (SSB) to exploit the adhesion properties of Sertoli cells to enrich cells obtained from pre-pubertal porcine testes for undifferentiated spermatogonia. We also compared the bioreactor approach with an established differential plating method and the combination of both: SSB followed by differential plating. After 66 h of culture, germ cell enrichment in SSBs provided 7.3 ± 1.0-fold (n = 9), differential plating 9.8 ± 2.4-fold (n = 6) and combination of both methods resulted in 9.1 ± 0.3-fold enrichment of germ cells from the initial germ cell population (n = 3). To document functionality of cells recovered from the bioreactor, we demonstrated that cells retained their functional ability to reassemble seminiferous tubules de novo after grafting to mouse hosts and to support spermatogenesis. These results demonstrate that the SSB allows enrichment of germ cells in a controlled and scalable environment providing an efficient method when handling large cell numbers while reducing variability owing to handling.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".