Targeted and multiplexed quantitation of CSF proteins by MRM and labeled peptide standards (981.5)
Bibliographic record
Abstract
Precise and accurate protein quantitation is essential for screening biomarkers for risk stratification, disease prognostication, and therapeutic monitoring. The most promising analytical strategy for quantitating unverified biomarkers in pre‐clinical biofluids relies on targeted MRM/MS with isotopically labeled standards. Through this approach, considerable effort has been extended toward verifying protein biomarkers of non‐communicable disease in the systematic circulation, but little in the way of accelerating the discovered markers of central nervous system‐related diseases in cerebrospinal fluid (CSF). We herein aimed to develop a rapid and robust, antibody‐free method to quantify a large panel of proteins in human CSF for disease biomarker verification and validation studies. Using pooled CSF and a complex isotopically coded peptide mixture, various denaturation/digestion approaches were evaluated within a bottom‐up proteomic workflow. For enhanced reproducibility and multiplexing, peptides were separated at standard‐flow rates and detected by dynamic MRM in a triple quadrupole mass spectrometer. The final method demonstrated excellent reproducibility (average retention times of 0.06% and signal of 5.8% CV) and enabled the quantitation of 155 protein biomarker candidates (inferred from 337 interference‐free peptides) in a 43 min run. The quantitative analyses of >100 CSF samples from spinal cord injury patients is currently being performed and will additionally be presented. Grant Funding Source : Supported by Genome BC, Genome Canada, and the Western Economic Diversification of Canada.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".