Highly sensitive and specific multiplex antibody assays to quantify immunoglobulins M, A and G against SARS-CoV-2 antigens
Bibliographic record
Abstract
ABSTRACT Reliable serological tests are required to determine the prevalence of antibodies against SARS-CoV-2 antigens and to characterise immunity to the disease in order to address key knowledge gaps in the context of the COVID-19 pandemic. Quantitative suspension array technology (qSAT) assays based on the xMAP Luminex platform overcome the limitations of rapid diagnostic tests and ELISA with their higher precision, dynamic range, throughput, miniaturization, cost-efficacy and multiplexing capacity. We developed three qSAT assays to detect IgM, IgA and IgG to a panel of eight SARS-CoV-2 antigens including spike (S), nucleoprotein (N) and membrane (M) protein constructs. The assays were optimized to minimize processing time and maximize signal to noise ratio. We evaluated the performance of the assays using 128 plasmas obtained before the COVID-19 pandemic (negative controls) and 115 plasmas from individuals with SARS-CoV-2 diagnosis (positive controls), of whom 8 were asymptomatic, 58 had mild symptoms and 49 were hospitalized. Pre-existing IgG antibodies recognizing N, M and S2 proteins were detected in negative controls suggestive of cross-reactive to common cold coronaviruses. The best performing antibody isotype/antigen signatures had specificities of 100% and sensitivities of 94.94% at ≥14 days since the onset of symptoms and 96.08% at ≥21 days since the onset of symptoms, with AUC of 0.992 and 0.999, respectively. Combining multiple antibody markers as assessed by qSAT assays has the highest efficiency, breadth and versatility to accurately detect low-level antibody responses for obtaining reliable data on prevalence of exposure to novel pathogens in a population. Our assays will allow gaining insights into antibody correlates of immunity required for vaccine development to combat pandemics like the COVID-19.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".