Development of Automated SISCAPA Assays for High-Throughput Quantitation of Protein Biomarkers
Bibliographic record
Abstract
Quantitation of proteotypic peptides in digests of plasma by SRM-MS allows specific, internally-standardized measurement of protein biomarkers and can achieve sub-nanogram/mL detection levels when specific anti-peptide antibodies are used to enrich target peptides from the plasma digests (SISCAPA). For this study, proteotypic tryptic peptides (initially 5 peptides per protein) were selected representing known protein biomarkers: PAI3 (protein C inhibitor), LPS binding protein, transferrin receptor, osteopontin, ferritin light chain, mesothelin, alpha-fetoprotein, HER2/neu, CA-125 and thyroglobulin. Affinity-purified polyclonal antibodies against the two peptides for each protein showing highest titers were characterized in SISCAPA assays, after which rabbit monoclonal antibodies (RabMAbs) were prepared (Epitomics, Inc.) against the best performing peptide for each target, except for Tg, for which mAbs were made against two peptides. The SISCAPA assay has been automated allowing processing of 96 samples in less than 30 minutes. The eluted peptides are delivered in a volume (20 μL) and solvent (5% acetic acid) suitable for subsequent injection into a reversed-phase LC system. Parameters for each of the 11 target peptides and cognate labeled standards have been optimized, permitting use of retention-time scheduled MRM data collection and rapid (3 min) analysis times. Results will be presented demonstrating the performance of the workflow for high-throughput quantitation of protein biomarkers.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".