Malaria: Parasites and Hosts Unit, Department of Parasites and Insect Vectors, Institut Pasteur, Paris, France.
ED 393, Sorbonne Université, Paris, France.
Lancet Microbe. 2021 Feb;2(2):e60-e69. doi: 10.1016/S2666-5247(20)30197-X. Epub 2020 Dec 21.
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.
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.
IgG antibody responses to trimeric spike protein (S) 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-S 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.
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.
European Research Council. Fondation pour la Recherche Médicale. Institut Pasteur Task Force COVID-19.
严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2) 的感染会引发针对多种抗原的抗体反应，这些反应会随时间而变化。本研究旨在利用这种复杂性开发更准确的血清学诊断方法。
开发了一种多重血清学检测方法，用于测量 215 名在法国四家医院（53 名患者和 162 名医护人员）因 SARS-CoV-2 感染而定量 RT-PCR 确诊的成年人在症状出现后 39 天内的 IgG 和 IgM 抗体对七种 SARS-CoV-2 刺突或核蛋白抗原、两种 229E 和 NL63 季节性冠状病毒核蛋白抗原以及三种非冠状病毒抗原的反应。使用来自法国、泰国和秘鲁的健康成年献血者在 SARS-CoV-2 流行前采集的阴性对照血清样本（335 份样本）。使用包含之前感染过 SARS-CoV-2 的个体的多元数据训练机器学习分类器，随机森林算法显示出最佳的分类性能。使用基于其他冠状病毒先验信息的抗体动力学贝叶斯数学模型来估计随时间变化的抗体反应，并评估在症状出现后第一年血清学诊断的敏感性和分类性能。提出了一种统计估计器，可用于在低传播环境中提供血清流行率的估计值。
对三聚体刺突蛋白（S）的 IgG 抗体反应可识别出 91.6%（95%CI 87.5-94.5）的敏感性和 99.1%（97.4-99.7）的特异性的既往 SARS-CoV-2 感染者。使用针对多种抗原的 IgG 和 IgM 血清学特征，可以以 98.8%（96.5-99.6）的敏感性和 99.3%（97.6-99.8）的特异性识别感染个体。根据其他冠状病毒的现有数据，我们估计感染后 1 年，最佳抗-S IgG 截止值的单克隆检测的敏感性为 88.7%（95%可信区间 63.4-97.4），而四抗原多重检测可将敏感性提高到 96.4%（80.9-100.0）。当应用于人群水平的血清学调查时，对多元数据的统计分析允许估计低于 2%的血清流行率水平，低于许多其他检测方法的假阳性率。
基于针对多种抗原的抗体反应的血清学特征可以对以前感染过 SARS-CoV-2 的个体进行准确和稳健的血清学分类。这为 SARS-CoV-2 血清学监测的两个紧迫挑战提供了潜在的解决方案：对 6 个月前感染的个体进行分类和在低传播环境中的血清学调查中测量血清流行率。
欧洲研究理事会。法国医学研究基金会。巴斯德研究所 COVID-19 特别工作组。
