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评估市售的全自动和基于 ELISA 的检测抗 SARS-CoV-2 中和抗体的方法。

Evaluation of commercially available fully automated and ELISA-based assays for detecting anti-SARS-CoV-2 neutralizing antibodies.

机构信息

Department of Biomedical Science, College of Health Sciences, QU Health, Qatar University, P.O. Box 2713, Doha, Qatar.

Biomedical Research Center, Qatar University, P.O. Box 2713, Doha, Qatar.

出版信息

Sci Rep. 2022 Nov 8;12(1):19020. doi: 10.1038/s41598-022-21317-x.

Abstract

Rapid and accurate measurement of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2)-specific neutralizing antibodies (nAbs) is paramount for monitoring immunity in infected and vaccinated subjects. The current gold standard relies on pseudovirus neutralization tests which require sophisticated skills and facilities. Alternatively, recent competitive immunoassays measuring anti-SARS-CoV-2 nAbs are proposed as a quick and commercially available surrogate virus neutralization test (sVNT). Here, we report the performance evaluation of three sVNTs, including two ELISA-based assays and an automated bead-based immunoassay for detecting nAbs against SARS-CoV-2. The performance of three sVNTs, including GenScript cPass, Dynamiker, and Mindray NTAb was assessed in samples collected from SARS-CoV-2 infected patients (n = 160), COVID-19 vaccinated individuals (n = 163), and pre-pandemic controls (n = 70). Samples were collected from infected patients and vaccinated individuals 2-24 weeks after symptoms onset or second dose administration. Correlation analysis with pseudovirus neutralization test (pVNT) and immunoassays detecting anti-SARS-CoV-2 binding antibodies was performed. Receiver operating characteristic (ROC) curve analysis was generated to assess the optimal threshold for detecting nAbs by each assay. All three sVNTs showed an excellent performance in terms of specificity (100%) and sensitivity (100%, 97.0%, and 97.1% for GenScript, Dynamiker, and Mindray, respectively) in samples collected from vaccinated subjects. GenScript demonstrated the strongest correlation with pVNT (r = 0.743, R = 0.552), followed by Mindray (r = 0.718, R = 0.515) and Dynamiker (r = 0.608, R = 0.369). Correlation with anti-SARS-CoV-2 binding antibodies was variable, but the strongest correlations were observed between anti-RBD IgG antibodies and Mindray (r = 0.952, R = 0.907). ROC curve analyses demonstrated excellent performance for all three sVNT assays in both groups, with an AUC ranging between 0.99 and 1.0 (p < 0.0001). Also, it was shown that the manufacturer's recommended cutoff values could be modified based on the tested cohort without significantly affecting the sVNT performance. The sVNT provides a rapid, low-cost, and scalable alternative to conventional neutralization assays for measuring and expanding nAbs testing across various research and clinical settings. Also, it could aid in evaluating actual protective immunity at the population level and assessing vaccine effectiveness to lay a foundation for boosters' requirements.

摘要

快速准确地测量严重急性呼吸综合征冠状病毒 2(SARS-CoV2)特异性中和抗体(nAbs)对于监测感染和接种疫苗的受试者的免疫至关重要。目前的金标准依赖于假病毒中和试验,该试验需要复杂的技能和设备。或者,最近提出的用于测量抗 SARS-CoV-2 nAbs 的竞争性免疫测定法可以作为一种快速且可商购的替代病毒中和试验(sVNT)。在这里,我们报告了三种 sVNTs 的性能评估,包括两种基于 ELISA 的测定法和一种用于检测针对 SARS-CoV-2 的 nAbs 的自动化珠免疫测定法。在 SARS-CoV-2 感染患者(n=160)、COVID-19 接种个体(n=163)和大流行前对照(n=70)采集的样本中评估了三种 sVNTs(包括 GenScript cPass、Dynamiker 和 Mindray NTAb)的性能。从感染患者和接种个体中采集了症状发作或第二剂给药后 2-24 周的样本。对与假病毒中和试验(pVNT)和检测抗 SARS-CoV-2 结合抗体的免疫测定法进行了相关性分析。生成了接收器工作特征(ROC)曲线分析,以评估每种测定法检测 nAbs 的最佳阈值。在接种疫苗的受试者样本中,所有三种 sVNTs 在特异性(100%)和敏感性(GenScript 为 100%、97.0%和 97.1%,Dynamiker 和 Mindray)方面均表现出出色的性能。GenScript 与 pVNT 的相关性最强(r=0.743,R=0.552),其次是 Mindray(r=0.718,R=0.515)和 Dynamiker(r=0.608,R=0.369)。与抗 SARS-CoV-2 结合抗体的相关性各不相同,但观察到抗 RBD IgG 抗体与 Mindray 的相关性最强(r=0.952,R=0.907)。ROC 曲线分析表明,所有三种 sVNT 测定法在两组中的性能均出色,AUC 范围在 0.99 到 1.0 之间(p<0.0001)。此外,结果表明,可以根据测试队列修改制造商推荐的截断值,而不会显著影响 sVNT 的性能。sVNT 为在各种研究和临床环境中测量和扩展 nAbs 测试提供了一种快速、低成本且可扩展的替代传统中和测定法。此外,它可以帮助评估人群水平的实际保护免疫,并评估疫苗效力,为加强针的需求奠定基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be74/9643483/f11564124a72/41598_2022_21317_Fig1_HTML.jpg

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