Immunocapture-Selected Reaction Monitoring Screening Facilitates the Development of ELISA for the Measurement of Native TEX101 in Biological Fluids*
Bibliographic record
Abstract
Monoclonal antibodies that bind the native conformation of proteins are indispensable reagents for the development of immunoassays, production of therapeutic antibodies and delineating protein interaction networks by affinity purification-mass spectrometry. Antibodies generated against short peptides, protein fragments, or even full length recombinant proteins may not bind the native protein form in biological fluids, thus limiting their utility. Here, we report the application of immunocapture coupled with selected reaction monitoring measurements (immunocapture-SRM), in the rapid screening of hybridoma culture supernatants for monoclonal antibodies that bind the native protein conformation. We produced mouse monoclonal antibodies, which detect in human serum or seminal plasma the native form of the human testis-expressed sequence 101 (TEX101) protein-a recently proposed biomarker of male infertility. Pairing of two monoclonal antibodies against unique TEX101 epitopes led to the development of an ELISA for the measurement of TEX101 in seminal plasma (limit of detection: 20 pg/ml) and serum (limit of detection: 40 pg/ml). Measurements of matched seminal plasma samples, obtained from men pre- and post-vasectomy, confirmed the absolute diagnostic specificity and sensitivity of TEX101 for noninvasive identification of physical obstructions in the male reproductive tract. Measurement of male and female serum samples revealed undetectable levels of TEX101 in the systemic circulation of healthy individuals. Immunocapture-SRM screening may facilitate development of monoclonal antibodies and immunoassays against native forms of challenging protein targets.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".