Differentiation of Peritubular Myoid-Like Cells from Human Induced Pluripotent Stem Cells
Bibliographic record
Abstract
ABSTRACT Spermatogenesis is a complex process requiring intricate cellular interactions between multiple cell types to produce viable sperm. Peritubular myoid cells (PTMs) are smooth muscle cells that line the seminiferous tubules and play a critical role in sperm production by providing mechanical support and molecular signaling factors. In vitro investigation of their contribution to spermatogenesis and their dysfunction in infertility is currently limited by the rare accessibility of human testicular tissue for research. Therefore, this study set forth to generate an alternative source of PTMs using human induced pluripotent stem cells (hiPSCs) - adult cells that have been reprogrammed into a pluripotent state, making them capable of indefinite expansion and the regeneration of any cell type in the body. PTMs and Leydig cells arise from a common progenitor, so we hypothesized that PTMs could be derived by modifying an existing differentiation protocol for Leydig cell differentiation from hiPSCs. These hiPSC-derived cells, or hPTMs, were characterized and compared to hiPSC-derived Leydig cells (hLCs) and human primary Sertoli cells as a negative control. Our findings show that the substitution of the molecular patterning factor Platelet-Derived Growth Factor Subunit B (PDGF-BB) for Platelet-Derived Growth Factor Subunit A (PDGF-AA) in a molecule-based differentiation protocol for deriving Leydig-like cells, is sufficient to derive peritubular myoid-like cells. This study describes a method for generating PTM-like cells from hiPSCs. These cells will allow for ongoing understanding of the cellular interactions required for normal spermatogenesis in an in vitro setting.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".