Stirred suspension bioreactors as a novel method to enrich germ cells from pre-pubertal pig testis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 it