Detailed Method for Performing the ExSTA Approach in Quantitative Bottom-Up Plasma Proteomics
Bibliographic record
Abstract
The use of stable isotope-labeled standards (SIS) is an analytically valid means of quantifying proteins in biological samples. The nature of the labeled standards and their point of insertion in a bottom-up proteomic workflow can vary, with quantification methods utilizing curves in analytically sound practices. A promising quantification strategy for low sample amounts is external standard addition (ExSTA). In ExSTA, multipoint calibration curves are generated in buffer using serially diluted natural (NAT) peptides and a fixed concentration of SIS peptides. Equal concentrations of SIS peptides are spiked into experimental sample digests, with all digests (control and experimental) subjected to solid-phase extraction prior to liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis. Endogenous peptide concentrations are then determined using the regression equation of the standard curves. Given the benefits of ExSTA in large-scale analysis, a detailed protocol is provided herein for quantifying a multiplexed panel of 125 high-to-moderate abundance proteins in undepleted and non-enriched human plasma samples. The procedural details and recommendations for successfully executing all phases of this quantification approach are described. As the proteins have been putatively correlated with various noncommunicable diseases, quantifying these by ExSTA in large-scale studies should help rapidly and precisely assess their true biomarker efficacy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.035 |
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".