Multiplexed MRM with Internal Standards for Cerebrospinal Fluid Candidate Protein Biomarker Quantitation
Bibliographic record
Abstract
Multiplexed quantitation is essential for discovering, verifying, and validating biomarkers for risk stratification, disease prognostication, and therapeutic monitoring. The most promising strategy for quantifying unverified protein biomarkers in biofluids relies on selected/multiple reaction monitoring (SRM or MRM) technology with isotopically labeled standards employed within a bottom-up proteomic workflow. Since cerebrospinal fluid (CSF) is an important fluid for studying central nervous system (CNS) related diseases, we sought to develop a rapid, antibody- and fractionation-free MRM-based approach with a complex mixture of peptide standards to quantify a highly multiplexed panel of candidate protein biomarkers in human CSF. Development involved peptide transition optimization, denaturation/digestion protocol evaluation, transition interference screening, and protein quantitation via peptide standard curves. The final method exhibited excellent reproducibility (average coefficient of variation of <1% for retention time and <6% for signal) and breadth of quantitation (130 proteins from 311 interference-free peptides) in a single 43-min run. These proteins are of high-to-low abundance with determined concentrations from 118 μg/mL (serum albumin) to 550 pg/mL (apolipoprotein C-I). Overall, the method consists of the most highly multiplexed and broadest panel of candidate protein biomarkers in human CSF reported thus far and is well suited for subsequent verification studies on patient samples.
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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 | 0.008 | 0.007 |
| 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 | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".