Abstract 17763: Multiple Reaction Monitoring-Based, Multiplexed, Absolute Quantitation of 82 Putative Biomarkers of Cardiovascular Disease in Human Plasma
Bibliographic record
Abstract
Multiplexed biomarker expression profiling of clinical samples can be achieved in a rapid and targeted fashion using mass spectrometry-based multiple reaction monitoring (MRM) quantitation. For this project, a mixture of ~230 peptides standards was created to permit absolute quantitation of 82 putative biomarkers of cardiovascular disease, in human plasma trypsin digests. All experiments were performed on simple tryptic digests of human EDTA-plasma without prior affinity depletion or enrichment. Synthetic purified stable isotope-labeled standard peptides (SIS peptides) were added to the samples after tryptic digestion. For maximum specificity, a high-flow system using UPLC and an Agilent 6490 triple quadrupole mass spectrometer, equipped with an ion-funnel sprayer, were used. Instrumental parameters were empirically determined in order to generate the most abundant precursor ions and ion fragments. The narrow UPLC peak shapes and reproducible retention times assisted in the development of the highly-multiplexed system by increasing the chromatographic “space”. In addition, the larger id column, with a larger amount of packing material, also provided increased “robustness” of the overall system, as demonstrated by 110 analyses of the same sample with no loss of sensitivity or retention time accuracy. Linear responses and lowest limits of quantitation were obtained for all proteins. Sensitivities using the new system were in the low attomole range demonstrating that the high-flow system, which allowed loading more protein digest onto the column, more than compensated for any reduction in electrospray ionization efficiency caused by the higher flow rate. Concentrations of individual peptide standards in the mixture were optimized to approximate endogenous concentrations of analytes for the highest quantitation accuracy. The analytical precision was assessed for each protein assay from LC-MRM/MS analyses performed on 3 different days on different batches of plasma tryptic digests, and was found to be within 10%. Concentrations of proteins were compared to reported literature values. This method, with the same mixture of internal standards, is now being used for the plasma protein expression profiling of cardiovascular disease patients.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".