Multiomics approach to profiling Sertoli cell maturation during development of the spermatogonial stem cell niche
Bibliographic record
Abstract
Spermatogonial stem cells (SSCs) are the basis of spermatogenesis, a complex process supported by a specialized microenvironment, called the SSC niche. Postnatal development of SSCs is characterized by distinct metabolic transitions from prepubertal to adult stages. An understanding of the niche factors that regulate these maturational events is critical for the clinical application of SSCs in fertility preservation. To investigate the niche maturation events that take place during SSC maturation, we combined different '-omics' technologies. Serial single cell RNA sequencing analysis revealed changes in the transcriptomes indicative of niche maturation that was initiated at 11 years of age in humans and at 8 weeks of age in pigs, as evident by Monocle analysis of Sertoli cells and peritubular myoid cell (PMC) development in humans and Sertoli cell analysis in pigs. Morphological niche maturation was associated with lipid droplet accumulation, a characteristic that was conserved between species. Lipidomic profiling revealed an increase in triglycerides and a decrease in sphingolipids with Sertoli cell maturation in the pig model. Quantitative (phospho-) proteomics analysis detected the activation of distinct pathways with porcine Sertoli cell maturation. We show here that the main aspects of niche maturation coincide with the morphological maturation of SSCs, which is followed by their metabolic maturation. The main aspects are also conserved between the species and can be predicted by changes in the niche lipidome. Overall, this knowledge is pivotal to establishing cell/tissue-based biomarkers that could gauge stem cell maturation to facilitate laboratory techniques that allow for SSC transplantation for restoration of fertility.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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".