A novel sorting method for the enrichment of early human spermatocytes from clinical biopsies
Bibliographic record
Abstract
Objective To determine if early spermatocytes can be enriched from a human testis biopsy using fluorescent-activated cell sorting (FACS). Design Potential surface markers for early spermatocytes were identified using bioinformatics analysis of single cell RNA sequenced (scRNAseq) human testis tissue. Testicular sperm extraction (TESE) samples from 3 participants with normal spermatogenesis were digested into single cell suspensions and cryopreserved. 2-4 million cells were obtained from each and sorted by FACS as separate biological replicates using antibodies for the identified surface markers. A portion from each biopsy remained unsorted to serve as controls. The sorted cells were then characterized for enrichment of early spermatocytes. Setting A laboratory study. Patients Three males diagnosed with obstructive azoospermia (age range 30-40 years old). Intervention None. Main outcome measures Sorted cells were characterized for RNA expression of markers encompassing the stages of spermatogenesis. Sorting markers were validated by their reactivity on human testis formalin-fixed paraffin-embedded (FFPE) tissue. Results Serine Protease 50 (TSP50) and SWI5 Dependent Homologous Recombination Repair Protein 1 (SFR1) were identified as potential surface proteins specific for early spermatocytes. After FACS sorting, the TSP50-sorted populations accounted for 1.6-8.9% of total populations and exhibited the greatest average fold increases in RNA expression for the pre-meiotic marker Stimulated by Retinoic Acid (STRA8), by 23-fold. Immunohistochemistry showed the staining pattern for TSP50 to be strong in pre-meiotic Undifferentiated Embryonic Cell Transcription Factor 1 (UTF1)-/ Doublesex And Mab-3 Related Transcription Factor 1 (DMRT1)-/STRA8+ spermatogonia as well as SYCP3+/Protamine 2 (PRM2)- spermatocytes. Conclusion This work shows that TSP50 can be used to enrich for early STRA8-expressing spermatocytes from human testicular biopsies, providing a means for targeted scRNAseq analysis and in vitro functional interrogation of germ cells during the onset of meiosis. This could enable investigation into details of the regulatory pathways underlying this critical stage of spermatogenesis previously difficult to enrich from whole tissue samples.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".