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

基于深度学习的血清学测试，使用纸基多重传感器对 COVID-19 免疫力进行即时分析

• 批准号: 2149551
• 负责人: Aydogan Ozcan
• 金额: $ 39.32万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2149551&HistoricalAwards=false
• 关键词: Deep; learning; based; serological; test

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.

基于深度学习的血清学检测用于使用基于纸的多路复用传感器的COVID-19免疫的床旁分析摘要：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）宣布为大流行病。诊断测试一直是应对措施的一个关键重点，迫切需要快速开发、扩展和分发新的测试。尽管所有成功的测试方法开发的SARS-CoV-2遗传物质的直接检测，仍然迫切需要创建新的血清学检测，可以检测病毒特异性抗体，因为它们可以确定补充信息，直接检测方法，通过指示以前的暴露和潜在的免疫力，特别是重要的，由于各种新出现的变种。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有效性，包括在接种和未接种人群中引发SARS-CoV-2和变异抗原特异性抗体的能力。与目前的直接检测方法相比，检测抗体的血清学测试可以是低成本的，并且有助于护理点（POC）设置，使得能够进行广泛的筛查工作，如广泛的免疫测试，以指示需要疫苗加强剂的个体，使个体有资格旅行，返回工作岗位，和/或识别恢复期血浆供体。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，该平台可以检测和测量一个人可能产生的多种不同的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免疫测定和血清学诊断算法，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SARS-CoV-2抗体阵列。为此，研究团队将创建一种多重垂直流检测（xVFA），以同时检测S蛋白的伊加、IgM和IgG抗体（以及S蛋白的变体，如δ、λ和其他新出现的变体），具有专用于S-1，S-2，以及SARS-CoV-2病毒及其最新变体中S蛋白的受体结合域（RBD）。利用现有的和去识别的人类血清样本，通过xVFA平台，研究团队将筛查COVID-19阳性样本，包括由常见变体产生的样本（通过逆转录-聚合酶链反应和测序确认）沿着接种疫苗的样本和大流行前未接种疫苗的阴性对照样本。然后将使用来自一组残留人血清样本的多重免疫反应和真实临床状态的定量信息来训练神经网络。该训练阶段将（1）创建血清学诊断算法，以使用多重抗体测量来鉴定对SARS-CoV-2感染（包括常见变体）或疫苗接种状态的阳性免疫应答，以及（2）鉴定抗体-抗原相互作用的关键子集，其最准确地代表和量化对SARS-CoV-2感染或通过疫苗接种保护的免疫应答。盲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，以严格验证经过训练的推理网络的泛化能力。通过验证用于COVID-19免疫保护的新型多重垂直流检测和血清诊断算法，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性的显着改善，这几乎没有增加成本或操作步骤，或所需的样本量。该项目还将建立一个补充性的教育推广计划，其中包括：（1）新闻媒体和互联网上的公开采访和科普文章;（2）涉及代表性不足的学生的本科生研究机会;以及（3）通过组织讲习班培训研究生，该奖项反映了NSF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进行评估，被认为值得支持的搜索.