Interlaboratory Evaluation of Automated, Multiplexed Peptide Immunoaffinity Enrichment Coupled to Multiple Reaction Monitoring Mass Spectrometry for Quantifying Proteins in Plasma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The inability to quantify large numbers of proteins in tissues and biofluids with high precision, sensitivity, and throughput is a major bottleneck in biomarker studies. We previously demonstrated that coupling immunoaffinity enrichment using anti-peptide antibodies (SISCAPA) to multiple reaction monitoring mass spectrometry (MRM-MS) produces Immunoprecipitation MRM-MS (immuno-MRM-MS) assays that can be multiplexed to quantify proteins in plasma with high sensitivity, specificity, and precision. Here we report the first systematic evaluation of the interlaboratory performance of multiplexed (8-plex) immuno-MRM-MS in three independent labs. A staged study was carried out in which the effect of each processing and analysis step on assay coefficient of variance, limit of detection, limit of quantification, and recovery was evaluated. Limits of detection were at or below 1 ng/ml for the assayed proteins in 30 μl of plasma. Assay reproducibility was acceptable for verification studies, with median intra- and interlaboratory coefficients of variance above the limit of quantification of 11% and <14%, respectively, for the entire immuno-MRM-MS assay process, including enzymatic digestion of plasma. Trypsin digestion and its requisite sample handling contributed the most to assay variability and reduced the recovery of target peptides from digested proteins. Using a stable isotope-labeled protein as an internal standard instead of stable isotope-labeled peptides to account for losses in the digestion process nearly doubled assay accuracy for this while improving assay precision 5%. Our results demonstrate that multiplexed immuno-MRM-MS can be made reproducible across independent laboratories and has the potential to be adopted widely for assaying proteins in matrices as complex as plasma.
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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 | 0.000 |
| 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 it