Interlaboratory Evaluation of Automated, Multiplexed Peptide Immunoaffinity Enrichment Coupled to Multiple Reaction Monitoring Mass Spectrometry for Quantifying Proteins in Plasma
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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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 | 0.026 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".