Immuno-Mass Spectrometry: Quantification of Low-Abundance Proteins in Biological Fluids
Bibliographic record
Abstract
Mass spectrometry is emerging as one of the most promising analytical techniques to examine simultaneously hundreds of analytes quickly, precisely, and accurately, using minute sample volumes. Currently, a major bottleneck in the verification phase of putative biomarkers is the lack of methods/reagents to quantify low levels of analytes in biological fluids. A major objective is to establish a high-throughput multiple reaction monitoring (MRM) assay capable of quantifying low-abundance proteins or peptides in biological fluids (low μg/L range) using mass spectrometry. The experimental procedure we propose, called immuno-mass spectrometry, consists of immuno-capturing analytes of interest from relevant biological fluids in 96-well microtiter plates and performing in-well tryptic digestion, with subsequent MRM of digested peptides on a triple quadrupole mass spectrometer. With such a strategy, limits of detection of 0.1-1 μg/L proteins in serum with a coefficient of variation of <20% can be obtained. This methodology could be adapted quickly and easily to potential candidates of interest, thus providing a much needed technology to bridge the gap between discovery and validation platforms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".