Differentiation of Peritubular Myoid‐Like Cells from Human Induced Pluripotent Stem Cells
Bibliographic record
Abstract
Infertility affects 10-15% of couples, with half attributed to male factors. An improved understanding of the cell-type-specific dysfunction contributing to male infertility is needed to improve available therapies; however, human testicular tissues are difficult to obtain for research purposes. To overcome this, researchers have begun to use human induced pluripotent stem cells (hiPSCs) to generate various testis-specific cell types in vitro. Peritubular myoid cells (PTMs) are one such testicular cell type that serves a critical role in the human testis niche but, to date, have not been derived from hiPSCs. This study set forth to generate a molecular-based differentiation method for deriving PTMs from hiPSCs, mirroring in vivo patterning factors. Whole transcriptome profiling and quantitative polymerase chain reaction (qPCR) show that this differentiation method is sufficient to derive cells with PTM-like transcriptomes, including upregulation of hallmark PTM functional genes, secreted growth and matrix factors, smooth muscle, integrins, receptors, and antioxidants. Hierarchical clustering shows that they acquire transcriptomes similar to primary isolated PTMs, and immunostaining shows the acquisition of a smooth muscle phenotype. Overall, these hiPSC-PTMs will allow in vitro study of patient-specific PTM development and function in spermatogenesis and infertility.
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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) | 0.002 | 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".