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

基于深度学习的纸基复合传感器新冠肺炎免疫点分析血清学试验摘要：新冠肺炎由SARS-CoV-2病毒引起，于2020年3月12日被世界卫生组织宣布为大流行。诊断性检测一直是应对措施的关键重点，迫切需要迅速开发、扩大和分发新的检测。尽管开发了所有成功的检测方法来直接检测SARS-CoV-2遗传物质，但仍然迫切需要创造新的血清学检测方法来检测病毒特异性抗体，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，特别是由于各种新出现的变种，这一点尤其重要。此外，随着针对新变种的疫苗的推出，这些血清学测试可用于评估疫苗接种活动的效果，包括在接种和未接种疫苗的人群中诱导SARS-CoV-2和变种抗原特异性抗体的能力。与目前的直接检测方法不同，检测抗体的血清学测试成本低，有利于医疗保健(POC)设置，使广泛的筛查工作得以进行，如广泛的免疫测试，以指示需要疫苗强化接种的个人，使个人有资格旅行，返回工作岗位，和/或识别恢复期的血浆捐赠者。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抗体，测试格式易于使用，使用廉价的纸质测试可以在15分钟内完成。研究小组将开发一种多路POC免疫分析和血清诊断算法，该算法将从多达10个独特的免疫反应中推断疫苗/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分析(XVFA)，以同时检测S蛋白(以及S蛋白的变体，如Delta、Lambda和其他新兴变体)的IgA、IgM和Ig G抗体，并在SARS-CoV-2病毒及其最新变体中具有专门针对S-1、S-2和S蛋白的受体结合域的单独免疫反应位点。利用现有的和未识别的人血清样本，通过xVFA平台，研究团队将筛选新冠肺炎阳性样本，包括由常见变异引起的样本(通过逆转录-聚合酶链式反应和测序确认)以及接种疫苗的样本和大流行前未接种疫苗的阴性对照样本。然后，将使用来自多路免疫反应的定量信息和一组剩余的人类血清样本的地面真实临床状态来训练神经网络。这一培训阶段将(1)创建一种血清诊断算法，以使用多重抗体测量来确定对SARS-CoV-2感染的阳性免疫反应(包括常见变种)或疫苗接种状态，以及(2)确定最准确地代表和量化对SARS-CoV-2感染的免疫反应或通过接种疫苗进行保护的抗体-抗原相互作用的关键子集。盲测试阶段将对多路复用和数据驱动方法的性能提高进行基准测试，以严格验证训练后的推理网络的泛化。通过验证用于新冠肺炎免疫保护的新的多重垂直流动分析和血清诊断算法，研究团队旨在确定通过多次测量和计算分析获得的灵敏度和特异度的显著提高，这些测量和计算分析几乎不需要增加成本或操作步骤，也不需要多少样本量。该项目还将建立一个补充的教育推广计划，包括(1)新闻媒体和互联网上的公开采访和科普文章；(2)涉及未被充分代表的学生的本科生研究机会；以及(3)通过组织研讨会、研讨会和会议进行研究生培训。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.