Immunoprecipitation-targeted proteomics assays facilitate rational development of SARS-CoV-2 serological diagnostics
Bibliographic record
Abstract
ABSTRACT Current design of serological tests employs conservative immunoassay approaches and is often focused on convenience, speed of manufacturing, and affordability. Limitations of such serological tests include semi-quantitative measurements, lack of standardization, potential cross-reactivity, and inability to distinguish between antibody subclasses. As a result of cross- reactivity, diagnostic specificity of serological antibody tests may not be sufficiently high to enable screening of the general asymptomatic populations for the acquired immunity against low-prevalence infectious diseases, such as COVID-19. Likewise, lack of a single standard for assay calibration limits inter-laboratory and international standardization of serological tests. In this study, we hypothesize that combination of immunoaffinity enrichments with targeted mass spectrometry measurements would enable rational design of serology diagnostics of infectious diseases, such as COVID-19. The same instrumental platform allows for sensitive and specific measurements of viral protein antigens, as wells as anti-viral antibodies circulating in human serum. Our proof-of-concept immunoprecipitation - parallel reaction monitoring (IP-PRM) assays quantified NCAP_SARS2 protein with a limit of detection of 313 pg/mL in serum. In addition, a multiplex IP-selected reaction monitoring (IP-SRM) assay facilitated differential quantification of anti-SARS-CoV-2 antibody isotypes and subclasses in patient sera. Simultaneous evaluation of numerous antigen-antibody subclass combinations revealed a receptor-binding domain (RBD)-IgG1 as a combination with the highest diagnostic specificity and sensitivity. Anti-RBD IgG1, IgG3, IgM and IgA1 subclasses, but not IgG2, IgG4 and IgA2, were found elevated in COVID-19-positive sera. Synthetic heavy isotope-labeled peptide internal standards as calibrators revealed elevated anti-RBD IgG1 in positive (510-6700 ng/mL; 0.02-0.22% of total serum IgG1) versus negative sera (60 [interquartile range 41-81] ng/mL). Likewise, anti-RBD IgM was elevated in positive (190-510 ng/mL; 0.06-0.16% of total serum IgM) versus negative sera (76 [31-108] ng/mL). Further validation of immunoprecipitation-targeted proteomics assays as a platform for serological assays will facilitate standardization and improvement of the existing serological tests, enable rational design of novel tests, and offer tools for comprehensive investigation of antibody isotype and subclass cooperation in immunity 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".