Accurate prediction of neutralization capacity from deep mining of SARS-CoV-2 serology

深度挖掘SARS-CoV-2血清学，准确预测中和能力

• 批准号: 10195613
• 负责人: SHOHEI KOIDE
• 金额: $ 46.61万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-19 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10195613
• 关键词: 2019-nCoV; Algorithms; Antibodies; Antigens; Biological Assay; Blood specimen; COVID-19 pandemic; Cells; Cessation of life; Clinical; Data; Data Set; Development; Environment; Enzyme-Linked Immunosorbent Assay; Epitopes; Exposure to; FDA approved; Flow Cytometry; Goals; Gold; Health; Hospitals; Immune response; Immunity; Immunoglobulin A; Immunoglobulin G; Immunoglobulin M; Infection; Methods; Microspheres; Mining; Nucleocapsid Proteins; Positioning Attribute; Protein Engineering; Reaction; Reporter; Reproducibility; Sampling; Series; Serologic tests; Serum; Surface; Technology; Testing; Therapeutic; Time; Training; Transfusion; Vaccines; Variant; Viral; Viral Antigens; Viral Markers; Virus; data mining; density; design; high risk population; improved; innovation; learning strategy; multidimensional data; mutant; neutralizing antibody; pathogen; prediction algorithm; predictive marker; profiles in patients; rapid technique; receptor binding; response; skills; supervised learning; vaccine development; vaccine efficacy; virology

ABSTRACT The goal of this project is to establish an accurate and sensitive method for predicting the neutralization capacity against SARS-CoV-2 of serum samples by deep mining of antibody profiles. The COVID-19 pandemic remains a global threat with nearly seven million cases and 400K deaths. In the absence of effective vaccines and therapeutics, immunity against SARS-CoV-2 is a main mechanism of protection against SARS-CoV-2 (re)infection. Our recent studies of convalescent serum samples revealed that their levels of neutralization capacity vary greatly (over 100-fold) and only a small subset has high neutralization capacity. Because viral neutralization assays are inherently low throughput, it is unrealistic to apply it to a high-risk population such as hospital workers in a timely manner. Unfortunately, there is only moderate correlation between the neutralization capacity and the level of anti-SARS-CoV-2 antibody levels determined using standard ELISA. Clearly, we still do not understand what types of antibodies contribute to viral neutralization. Our overarching hypothesis to be tested in this project is that by examining the antibody profile in patient serum more deeply and quantitatively in terms of antigens, epitopes and antibody types, we will be able to identify quantitative predictive markers for viral neutralization. To this end, we will develop multiplex assay for SARS-CoV-2 serology that will enable us to deeply characterize the antibody profile. We will then develop a predictive algorithm by utilizing. We have assembled a team of experts with truly complementary skills in antibody characterization, virology and data mining. We have access to a large number of convalescent serum samples, which will enable us to critically validate our technology. We will expeditiously execute the following aims. (1) We will develop multiplex serology assay for SARS-CoV-2 that can profile up to 15 antibody-antigen interactions in a single reaction. The main technical innovation is the introduction of multi-dimensional flow cytometry. We will produce multiple antigens including Spike, receptor-binding domain and nucleocapsid protein, and their natural and designed variants. We will refine and validate the assay using a large panel of convalescent serum samples. (2) We will develop an improved viral neutralization assay to better quantify the neutralization capacity. (3) We will develop a predictive algorithm for neutralization capacity that utilizes the antibody profiles from our multiplex assay. This analysis will identify serology parameters that contribute to neutralization. The end products of this project will include a high-throughput serology assay that gives far- richer antibody profiles than the current standard accompanied with an accurate predictive algorithm. Together, this platform will help advance a fundamental understanding of SARS-CoV-2 infection as well as the development of vaccines and therapeutics against this formidable pathogen.

抽象的 该项目的目标是建立一种准确且灵敏的方法来预测中和作用 通过深入挖掘抗体谱来提高血清样本对抗 SARS-CoV-2 的能力。 COVID-19 大流行 仍然是一个全球性威胁，有近 700 万病例和 40 万人死亡。在没有有效疫苗的情况下 和治疗方面，针对 SARS-CoV-2 的免疫是针对 SARS-CoV-2 的主要保护机制 （再）感染。我们最近对恢复期血清样本的研究表明，它们的中和水平 容量差异很大（超过 100 倍），并且只有一小部分具有高中和容量。因为病毒式传播 中和测定本质上是低通量的，将其应用于高风险人群是不现实的，例如 医院工作人员及时处理。不幸的是，两者之间只有中等程度的相关性。 使用标准 ELISA 测定的中和能力和抗 SARS-CoV-2 抗体水平。 显然，我们仍然不了解哪些类型的抗体有助于病毒中和。我们的首要任务 该项目要测试的假设是，通过更深入地检查患者血清中的抗体谱 并在抗原、表位和抗体类型方面进行定量，我们将能够定量识别 病毒中和的预测标记。为此，我们将开发 SARS-CoV-2 的多重检测 血清学将使我们能够深入表征抗体谱。然后我们将开发一个预测 利用算法。我们组建了一支在抗体领域具有真正互补技能的专家团队 表征、病毒学和数据挖掘。我们可以获得大量恢复期血清样本， 这将使我们能够批判性地验证我们的技术。我们将迅速实现以下目标。 (1) 我们将开发针对 SARS-CoV-2 的多重血清学检测，可分析多达 15 种抗体-抗原 单一反应中的相互作用。主要技术创新是多维流的引入 细胞计数术。我们将生产多种抗原，包括Spike、受体结合结构域和核衣壳 蛋白质及其天然和设计的变体。我们将使用大量的数据来完善和验证该检测 恢复期血清样本。 (2) 我们将开发一种改进的病毒中和试验，以更好地量化 中和能力。 (3) 我们将开发一种中和能力的预测算法，该算法利用 来自我们的多重检测的抗体谱。该分析将确定有助于 中和。该项目的最终产品将包括高通量血清学检测，该检测可提供远 比当前标准更丰富的抗体谱以及准确的预测算法。一起， 该平台将有助于加深对 SARS-CoV-2 感染以及 开发针对这种可怕病原体的疫苗和治疗方法。

项目成果

A Rapid and Sensitive Microfluidics-Based Tool for Seroprevalence Immunity Assessment of COVID-19 and Vaccination-Induced Humoral Antibody Response at the Point of Care.

• DOI: 10.3390/bios12080621
• 发表时间: 2022-08-10
• 期刊: BIOSENSORS-BASEL
• 影响因子: 5.4
• 作者: Rajsri, Kritika Srinivasan;McRae, Michael P.;Simmons, Glennon W.;Christodoulides, Nicolaos J.;Matz, Hanover;Dooley, Helen;Koide, Akiko;Koide, Shohei;McDevitt, John T.
• 通讯作者: McDevitt, John T.