Multiplexed MRM-Based Protein Quantitation Using Two Different Stable Isotope-Labeled Peptide Isotopologues for Calibration
Bibliographic record
Abstract
When quantifying endogenous plasma proteins for fundamental and biomedical research - as well as for clinical applications - precise, reproducible, and robust assays are required. Targeted detection of peptides in a bottom-up strategy is the most common and precise mass spectrometry-based quantitation approach when combined with the use of stable isotope-labeled peptides. However, when measuring protein in plasma, the unknown endogenous levels prevent the implementation of the best calibration strategies, since no blank matrix is available. Consequently, several alternative calibration strategies are employed by different laboratories. In this study, these methods were compared to a new approach using two different stable isotope-labeled standard (SIS) peptide isotopologues for each endogenous peptide to be quantified, enabling an external calibration curve as well as the quality control samples to be prepared in pooled human plasma without interference from endogenous peptides. This strategy improves the analytical performance of the assay and enables the accuracy of the assay to be monitored, which can also facilitate method development and validation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".