The proteomic toolbox for identification, quantification, and characterization of polyclonal antibodies
Bibliographic record
Abstract
SUMMARY Recent advances in proteomics and mass spectrometry facilitated the in-depth characterization of monoclonal antibodies and enabled innovative approaches for the quantification of polyclonal antibodies generated against numerous antigens. Human respiratory syncytial virus (RSV) is a contagious respiratory pathogen often manifested as a common cold infection in adults and more serious symptoms in infants and the elderly population. Here, we used a reference IgG1κ monoclonal antibody NISTmAb 8671 and its affinity interaction with an RSV fusion glycoprotein F as a model to develop the proteomic toolbox for identification, quantification, and characterization of polyclonal antibodies. Our toolbox integrated a variety of proteomic and mass spectrometry approaches for accurate mass measurements of antibody fragments, antibody digestion with the complimentary proteases (trypsin, asparaginase, and proalanase), immunoaffinity enrichments, and bottom-up or middle-down proteomics. We measured absolute concentrations of anti-RSV antibody isotypes and subclasses in 69 serum samples of healthy individuals and revealed IgG1 (2,580 ng/mL), IgA1 (280 ng/mL), and IgM (180 ng/mL) as the most abundant isotypes. Interestingly, we also identified the presence of IgG2 (74 ng/ml), IgG4 (4.9 ng/mL) and IgA2 (5.5 ng/mL) antibodies. Interactome measurements detected the consistent co-precipitation of C1q complement complexes. Repertoire profiling of the variable regions of polyclonal antibodies revealed the frequent use of IGHV3 subgroup genes, while IGHV5-51 was the most abundant single gene of the anti-RSV polyclonal antibody response. The presented toolbox will facilitate the in-depth characterization of polyclonal antibodies and pave the way to quantitative approaches in serological studies and precision immunology.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".