Multiplex assays for the identification of serological signatures of SARS-CoV-2 infection: an antibody-based diagnostic and machine learning study - Sorbonne Université Access content directly
Journal Articles The Lancet Microbe Year : 2021

Multiplex assays for the identification of serological signatures of SARS-CoV-2 infection: an antibody-based diagnostic and machine learning study

1 Malaria : parasites et hôtes - Malaria : parasites and hosts
2 ED 393 - Santé publique : épidémiologie & sciences de l'information biomédicale
3 Régulation spatiale des Génomes - Spatial Regulation of Genomes
4 IVI - Interactions Virus-Insectes - Insect-Virus Interactions
5 GMV-ARN (UMR_3569 / U-Pasteur_2) - Génétique Moléculaire des Virus à ARN - Molecular Genetics of RNA Viruses
6 Virologie Structurale - Structural Virology
7 EMAE - Epidémiologie et modélisation de la résistance aux antimicrobiens - Epidemiology and modelling of bacterial escape to antimicrobials
8 Equipe Mobile d'Infectiologie [APHP Centre, Paris]
9 AP-HP - Hôpital Cochin Broca Hôtel Dieu [Paris]
10 CHU Pitié-Salpêtrière [AP-HP]
11 Laboratoire de Virologie [Strasbourg]
12 IRM - Immuno-Rhumatologie Moléculaire
13 Centre d’Investigation Clinique Plurithématique (CIC - P) - CIC Strasbourg
14 CNRS-UMR3569 - Virus et Immunité - Virus and immunity
15 VRI - Vaccine Research Institute [Créteil, France]
16 C2RT - Centre de Ressources et de Recherche Technologique - Center for Technological Resources and Research
17 PPR - Plateforme technologique Production et purification de protéines recombinantes – Production and Purification of Recombinant Proteins Technological Platform
18 University of Melbourne
19 WEHI - The Walter and Eliza Hall Institute of Medical Research
20 Epidémiologie des Maladies Emergentes - Emerging Diseases Epidemiology
21 CNAM - Conservatoire National des Arts et Métiers [CNAM]
Jerome de Seze
  • Function : Author

Abstract

Background Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces an antibody response targeting multiple antigens that changes over time. This study aims to take advantage of this complexity to develop more accurate serological diagnostics. Methods A multiplex serological assay was developed to measure IgG and IgM antibody responses to seven SARS-CoV-2 spike or nucleoprotein antigens, two antigens for the nucleoproteins of the 229E and NL63 seasonal coronaviruses, and three non-coronavirus antigens. Antibodies were measured in serum samples collected up to 39 days after symptom onset from 215 adults in four French hospitals (53 patients and 162 health-care workers) with quantitative RT-PCR-confirmed SARS-CoV-2 infection, and negative control serum samples collected from healthy adult blood donors before the start of the SARS-CoV-2 epidemic (335 samples from France, Thailand, and Peru). Machine learning classifiers were trained with the multiplex data to classify individuals with previous SARS-CoV-2 infection, with the best classification performance displayed by a random forests algorithm. A Bayesian mathematical model of antibody kinetics informed by prior information from other coronaviruses was used to estimate time-varying antibody responses and assess the sensitivity and classification performance of serological diagnostics during the first year following symptom onset. A statistical estimator is presented that can provide estimates of seroprevalence in very low-transmission settings. Findings IgG antibody responses to trimeric spike protein (Stri) identified individuals with previous SARS-CoV-2 infection with 91·6% (95% CI 87·5–94·5) sensitivity and 99·1% (97·4–99·7) specificity. Using a serological signature of IgG and IgM to multiple antigens, it was possible to identify infected individuals with 98·8% (96·5–99·6) sensitivity and 99·3% (97·6–99·8) specificity. Informed by existing data from other coronaviruses, we estimate that 1 year after infection, a monoplex assay with optimal anti-Stri IgG cutoff has 88·7% (95% credible interval 63·4–97·4) sensitivity and that a four-antigen multiplex assay can increase sensitivity to 96·4% (80·9–100·0). When applied to population-level serological surveys, statistical analysis of multiplex data allows estimation of seroprevalence levels less than 2%, below the false-positivity rate of many other assays. Interpretation Serological signatures based on antibody responses to multiple antigens can provide accurate and robust serological classification of individuals with previous SARS-CoV-2 infection. This provides potential solutions to two pressing challenges for SARS-CoV-2 serological surveillance: classifying individuals who were infected more than 6 months ago and measuring seroprevalence in serological surveys in very low-transmission settings. Funding European Research Council. Fondation pour la Recherche Médicale. Institut Pasteur Task Force COVID-19.
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hal-03128905 , version 1 (02-02-2021)

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Attribution - NonCommercial - NoDerivatives

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Jason Rosado, Stéphane Pelleau, Charlotte A. Cockram, Sarah Hélène Merkling, Narimane Nekkab, et al.. Multiplex assays for the identification of serological signatures of SARS-CoV-2 infection: an antibody-based diagnostic and machine learning study. The Lancet Microbe, 2021, 2 (2), pp.e60-e69. ⟨10.1016/S2666-5247(20)30197-X⟩. ⟨hal-03128905⟩
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