Deep learning-based serological test for point-of-care analysis of COVID-19 immunity with a paper-based multiplexed sensor

摘要：2020年3月12日，由SARS-CoV-2病毒引起的COVID-19被世界卫生组织（WHO）宣布为大流行。诊断测试一直是应对措施的关键焦点，迫切需要快速开发、扩展和分发新的测试。尽管开发了所有用于直接检测SARS-CoV-2遗传物质的成功检测方法，但仍然迫切需要创建能够检测病毒特异性抗体的新的血清学检测方法，因为它们可以通过指示以前的暴露和潜在的免疫来确定直接检测方法的补充信息，由于各种新变体，这一点尤为重要。此外，随着针对新变体的疫苗的推出，这些血清学测试可用于评估疫苗接种运动的有效性，包括在接种疫苗和未接种疫苗的人群中引发SARS-CoV-2和变体抗原特异性抗体的能力。与目前的直接检测方法相比，检测抗体的血清学检测成本低，有利于在护理点（POC）环境中开展广泛的筛查工作，如广泛的免疫检测，以表明需要疫苗增强剂的个人，使个人有资格旅行，重返工作岗位，和/或确定恢复期血浆捐献者。为了满足这一迫切需求，该项目将创建一个基于智能手机的、具有成本效益的平台，该平台可以感知和测量一个人可能产生的许多不同的SARS-CoV-2特异性抗体，其测试格式易于使用，并且可以在15分钟内使用廉价的纸质测试完成。研究小组将开发一种多重POC免疫测定和血清诊断算法，该算法将从多达10种独特的免疫反应中推断疫苗接种/免疫状态，以区分一系列SARS-CoV-2抗体。为此，研究小组将创建一种多重垂直流动试验（xVFA），同时检测S蛋白的IgA、IgM和IgG抗体（以及S蛋白的变体，如delta、lambda和其他新出现的变体），在SARS-CoV-2病毒及其最新变体中，S蛋白的S-1、S-2和受体结合域（RBD）具有单独的免疫反应位点。研究小组将利用现有的和去鉴定的人血清样本，利用xVFA平台，筛选covid -19阳性样本，包括由常见变异（通过逆转录-聚合酶链反应和测序确认）产生的样本，以及接种疫苗样本和大流行前未接种疫苗的阴性对照样本。然后，神经网络将使用来自多重免疫反应的定量信息和一组残余人类血清样本的基本真实临床状态进行训练。该培训阶段将(1)创建一种血清诊断算法，以识别对SARS-CoV-2感染（包括常见变体）的阳性免疫反应或使用多重抗体测量的疫苗接种状态，以及(2)确定抗体-抗原相互作用的关键子集，该子集最准确地代表和量化对SARS-CoV-2感染的免疫反应或通过疫苗接种的保护。盲法测试阶段将对多路复用和数据驱动方法的性能增强进行基准测试，以严格验证训练后的推理网络的泛化性。通过验证一种新的用于COVID-19免疫保护的多路垂直流检测和血清诊断算法，研究小组的目标是确定通过多次测量和计算分析获得的灵敏度和特异性的显着提高，这些测量和计算分析几乎不需要增加成本或操作步骤，也不需要样本量。该项目还将建立一个补充的教育推广计划，包括：(1)在新闻媒体和互联网上进行公众采访和科普文章；(2)涉及代表性不足学生的本科生研究机会；(3)通过组织讲习班、研讨会和会议来培养研究生。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Deep learning-based serological test for point-of-care analysis of COVID-19 immunity with a paper-based multiplexed sensorAbstract: COVID-19, caused by the virus SARS-CoV-2, was declared a pandemic by the World Health Organization (WHO) on March 12, 2020. Diagnostic testing has been a critical focus of the response, with an urgent need to rapidly develop, scale, and distribute new tests. Despite all the successful testing methods developed for the direct detection of SARS-CoV-2 genetic material, there is still an urgent need to create new serological assays that can detect virus-specific antibodies as they can ascertain complementary information to direct detection methods by indicating previous exposure and potential immunity, especially important due to various emerging variants. In addition, as vaccines against new variants roll out, these serological tests can be used to evaluate the efficacy of vaccination campaigns, including the ability to elicit SARS-CoV-2 and variant antigen-specific antibodies across vaccinated and unvaccinated populations. In contrast to the current direct detection methods, serology tests that detect antibodies can be low-cost and conducive to a point-of-care (POC) setting, enabling broad screening efforts like widespread immunity testing to indicate individuals in need of vaccine boosters, qualify individuals for travel, return to work, and/or identify convalescent plasma donors. To serve this urgent need, this project will create a smartphone-based, cost-effective platform that can sense and measure the many different antibodies specific to SARS-CoV-2 a person may develop, in a testing format that is easy to use and can be completed within 15 min using an inexpensive paper-based test. The team of researchers will develop a multiplexed POC immunoassay and serodiagnostic algorithm that will infer the vaccination/immunity status from up to 10 unique immunoreactions to distinguish an array of SARS-CoV-2 antibodies. For this, the research team will create a multiplexed vertical flow assay (xVFA) to simultaneously detect IgA, IgM, and IgG antibodies to the S protein (as well as variants of the S protein, such as delta, lambda, and other emerging variants), with separate immunoreaction sites dedicated to S-1, S-2, and the receptor-binding domain (RBD) of the S-protein in the SARS-CoV-2 virus and its most recent variants. Using existing and de-identified human serum samples, with the xVFA platform, the research team will screen COVID-19-positive samples, including those resulting from common variants (confirmed through reverse transcriptase-Polymerase Chain Reaction and sequencing) along with vaccinated samples and pre-pandemic un-vaccinated negative control samples. A neural network will then be trained using quantitative information from the multiplexed immunoreactions and the ground-truth clinical state over a set of remnant human serum samples. This training phase will (1) create a serodiagnostic algorithm to identify a positive immune response to SARS-CoV-2 infection (including common variants) or vaccination status using the multiplexed antibody measurements, and (2) identify the key subset of antibody-antigen interactions that most accurately represent and quantify an immune response to SARS-CoV-2 infection or protection via vaccination. A blinded testing phase will benchmark the performance enhancement of the multiplexed and data-driven approach to rigorously validate the trained inference network's generalization. By validating a new multiplexed vertical flow assay and serodiagnosis algorithm for COVID-19 immune protection, the research team aims to determine the significant improvements in sensitivity and specificity gained through the multiple measurements and computational analysis, which come with little added cost or operational steps, or required sample volume. This project will also establish a complementary educational outreach program that will involve (1) public interviews and popular science articles in news media and the internet; (2) undergraduate research opportunities involving underrepresented students; and (3) graduate student training through the organization of workshops, seminars and conferences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.