“Product Ion Monitoring” Assay for Prostate-Specific Antigen in Serum Using a Linear Ion-Trap
Bibliographic record
Abstract
While numerous strategies exist for biomarker discovery, the bottleneck to product development and routine use at the clinic is in the verification phase of candidate biomarkers. The aim of this study was to establish a robust and high-throughput product ion monitoring (PIM) assay that is potentially capable of rapidly verifying candidates from discovery phase experiments. Using prostate-specific antigen (PSA), a model biomarker, and a routinely used mass spectrometer for discovery platforms, an ion trap (LTQ, Thermo), the utility of this instrument to perform PIM was explored. The proteotypic doubly charged intact peptide LSEPAELTDAVK ( m/ z 637) fragmenting to m/ z 943 (PAELTDAVK) was monitored. A limit of detection of 10 attomoles with a coefficient of variation (CV) of <20% was obtained for a purified recombinant PSA digest. Immunoextraction of endogenous PSA from serum using a monoclonal antibody on a 96-well microtiter plate, followed by PIM on the LTQ, enabled quantification of PSA down to less than 1 ng/mL with a limit of detection of 0.1 ng/mL and CVs < 20%. Mascot searching and ion ratio confirmation further supported the conclusion that the quantified moiety in serum was the PSA peptide. We conclude that this methodology could be adapted quickly and easily to other candidates, thus providing a much needed technology to bridge the gap between discovery and validation platforms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".