Rapid diagnostics are critical infectious disease tools that are designed to detect a known biomarker using antibodies specific to that biomarker. However, a way to detect unknown disease variants has not yet been achieved in a paper test format. We describe here a route to make an adaptable paper immunoassay that can detect an unknown biomarker, demonstrating it on SARS-CoV-2 variants. The immunoassay repurposes cross reactive antibodies raised against the alpha variant. Gold nanoparticles of two different colors conjugated to two different antibodies create a colorimetric signal, and machine learning of the resulting colorimetric pattern is used to train the assay to discriminate between variants of alpha and Omicron BA.5. By using principal component analysis, the colorimetric test patterns can pick up and discriminate an unknown variant that it has not encountered before, Omicron BA.1. The test has an accuracy of 100% and a potential calculated discriminatory power of 900. We show that it can be used adaptively and that it can be used to pick up emerging variants without the need to raise new antibodies.

中文翻译：

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一种利用主成分分析增强新出现病毒变体检测的新型免疫分析技术


快速诊断是重要的传染病工具，旨在使用特定于已知生物标志物的抗体来检测该生物标志物。然而，纸质测试形式尚未实现检测未知疾病变异的方法。我们在此描述了一种制作适应性纸质免疫测定的方法，该免疫测定可以检测未知的生物标志物，并在 SARS-CoV-2 变体上进行了演示。免疫测定重新利用针对α变体产生的交叉反应抗体。两种不同颜色的金纳米粒子与两种不同的抗体缀合，产生比色信号，并对所得比色模式进行机器学习，用于训练检测方法以区分 alpha 和 Omicron BA.5 的变体。通过使用主成分分析，比色测试图案可以拾取并区分以前从未遇到过的未知变体，Omicron BA.1。该测试的准确度为 100%，潜在的计算判别力为 900。我们证明它可以适应性地使用，并且可以用来识别新出现的变异，而无需产生新的抗体。