Rational Design and Development of SARS-CoV-2 Serological Diagnostics by Immunoprecipitation-Targeted Proteomics
Bibliographic record
Abstract
Current design of serological tests utilizes conservative immunoassay approaches and is focused on fast and convenient assay development, throughput, straightforward measurements, and affordability. Limitations of common serological assays include semiquantitative measurements, cross-reactivity, lack of reference standards, and no differentiation between human immunoglobulin subclasses. In this study, we suggested that a combination of immunoaffinity enrichments with targeted proteomics would enable rational design and development of serological assays of infectious diseases, such as COVID-19. Immunoprecipitation-targeted proteomic assays allowed for sensitive and specific measurements of NCAP_SARS2 protein with a limit of detection of 313 pg/mL in serum and enabled differential quantification of anti-SARS-CoV-2 antibody isotypes (IgG, IgA, IgM, IgD, and IgE) and individual subclasses (IgG1-4 and IgA1-2) in plasma and saliva. Simultaneous evaluation of the numerous antigen-antibody subclass combinations revealed a receptor-binding domain (RBD)-IgG1 as a combination with the highest diagnostic performance. Further validation revealed that anti-RBD IgG1, IgG3, IgM, and IgA1 levels were significantly elevated in convalescent plasma, while IgG2, IgG4, and IgA2 were not informative. Anti-RBD IgG1 levels in convalescent (2138 ng/mL) vs negative (95 ng/mL) plasma revealed 385 ng/mL as a cutoff to detect COVID-19 convalescent plasma. Immunoprecipitation-targeted proteomic assays will facilitate improvement and standardization of the existing serological tests, enable rational design of novel tests, and offer tools for the comprehensive investigation of immunoglobulin subclass cooperation in immune response.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".