Coupling immunoaffinity techniques with MS for quantitative analysis of low-abundance protein biomarkers
Bibliographic record
Abstract
The field of proteomics is rapidly turning towards targeted mass spectrometry (MS) methods to quantify putative markers or known proteins of biological interest. Historically, the enzyme-linked immunosorbent assay (ELISA) has been used for targeted protein analysis, but, unfortunately, it is limited by the excessive time required for antibody preparation, as well as concerns over selectivity. Despite the ability of proteomics to deliver increasingly quantitative measurements, owing to limited sensitivity, the leads generated are in the microgram per milliliter range. This stands in stark contrast to ELISA, which is capable of quantifying proteins at low picogram per milliliter levels. To bridge this gap, targeted liquid chromatography (LC) tandem MS (MS/MS) analysis of tryptic peptide surrogates using selected reaction monitoring detection has emerged as a viable option for rapid quantification of target proteins. The precision of this approach has been enhanced by the use of stable isotope-labeled peptide internal standards to compensate for variation in recovery and the influence of differential matrix effects. Unfortunately, the complexity of proteinaceous matrices, such as plasma, limits the usefulness of this approach to quantification in the mid-nanogram per milliliter range (medium-abundance proteins). This article reviews the current status of LC/MS/MS using selected reaction monitoring for protein quantification, and specifically considers the use of a single antibody to achieve superior enrichment of either the protein target or the released tryptic peptide. Examples of immunoaffinity-assisted LC/MS/MS are reviewed that demonstrate quantitative analysis of low-abundance proteins (subnanogram per milliliter range). A strategy based on this technology is proposed for the expedited evaluation of novel protein biomarkers, which relies on the synergy created from the complementary nature of MS and ELISA.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".