An approach to use machine learning to optimize paper immunoassays for SARS-CoV-2 IgG and IgM antibodies†

IF 3.5 Q2 CHEMISTRY, ANALYTICAL
Josselyn Mata Calidonio and Kimberly Hamad-Schifferli
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Abstract

Optimizing paper immunoassay conditions for diagnostic accuracy is often achieved by tuning running conditions in a trial and error manner. We developed an approach to use machine learning (ML) in the optimization process, demonstrating it on a COVID-19 assay to detect IgG and IgM antibodies for both SARS CoV-2 spike and nucleocapsid proteins. The multiplexed test had a multicolor readout by using red and blue gold nanoparticles. Spike and nucleocapsid proteins were immobilized on a nitrocellulose strip at different locations, and the assay was run with red nanoparticles conjugated to anti-IgG and blue nanostars conjugated to anti-IgM. The spatial location of the signal indicated whether the antibody present was anti-spike or anti-nucleocapsid, and the test area color indicated the antibody type (IgG vs. IgM). Linear discriminant analysis (LDA) and ML were used to evaluate the test accuracy, and then used iteratively to modify running conditions (presence of quencher molecules, nanoparticle types, washes) until the test accuracy reached 100%. The resulting assay could be trained to distinguish between 9 different antibody profiles indicative of different disease cases (prior infection vs. vaccinated, early/mid/late stage post infection). Results show that supervised learning can accelerate test development, and that using the test as a selective array rather than a specific sensor could enable rapid immunoassays to obtain more complex information.

Abstract Image

利用机器学习优化 SARS-CoV-2 IgG 和 IgM 抗体纸质免疫测定的方法
我们开发了一种 COVID-19 纸免疫测定法,可以检测 SARS CoV-2 棘突蛋白和核壳蛋白的 IgG 和 IgM 抗体。该检测法是一种多色检测法,使用不同形状(球形和星形)的金纳米颗粒作为标记,在检测区域呈现出视觉上明显的红色和蓝色。该检测使用固定在硝酸纤维素条上不同位置的钉状和核壳原蛋白,运行的纳米颗粒-抗体共轭物是与抗-IgG 共轭的红色纳米颗粒和与抗-IgM 共轭的蓝色纳米星体。信号的空间位置表明存在的抗体是抗尖峰抗体还是抗核头壳抗体,测试区域的颜色表明是哪种抗体类型(IgG 还是 IgM)。使用线性判别分析(LDA)和机器学习(ML)来评估测试准确性,然后使用迭代法来修改检测运行条件(淬火剂分子的存在、纳米粒子类型、洗涤),直到测试准确性达到 100%。最终得出的检测结果可区分 9 种不同的抗体图谱,表明不同的疾病病例(先前感染与接种疫苗、感染后早期/中期/晚期)。结果表明,将测试从特定传感扩展到阵列选择性传感,可使快速免疫测定获得更复杂的信息,而监督学习可加速测试开发。
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CiteScore
2.30
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0.00%
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