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In ACS nano ; h5-index 203.0

Cytokine storm, known as an exaggerated hyperactive immune response characterized by elevated release of cytokines, has been described as a feature associated with life-threatening complications in COVID-19 patients. A critical evaluation of a cytokine storm and its mechanistic linkage to COVID-19 requires innovative immunoassay technology capable of rapid, sensitive, selective detection of multiple cytokines across a wide dynamic range at high-throughput. In this study, we report a machine-learning-assisted microfluidic nanoplasmonic digital immunoassay to meet the rising demand for cytokine storm monitoring in COVID-19 patients. Specifically, the assay was carried out using a facile one-step sandwich immunoassay format with three notable features: (i) a microfluidic microarray patterning technique for high-throughput, multiantibody-arrayed biosensing chip fabrication; (ii) an ultrasensitive nanoplasmonic digital imaging technology utilizing 100 nm silver nanocubes (AgNCs) for signal transduction; (iii) a rapid and accurate machine-learning-based image processing method for digital signal analysis. The developed immunoassay allows simultaneous detection of six cytokines in a single run with wide working ranges of 1-10,000 pg mL-1 and ultralow detection limits down to 0.46-1.36 pg mL-1 using a minimum of 3 μL serum samples. The whole chip can afford a 6-plex assay of 8 different samples with 6 repeats in each sample for a total of 288 sensing spots in less than 100 min. The image processing method enhanced by convolutional neural network (CNN) dramatically shortens the processing time ∼6,000 fold with a much simpler procedure while maintaining high statistical accuracy compared to the conventional manual counting approach. The immunoassay was validated by the gold-standard enzyme-linked immunosorbent assay (ELISA) and utilized for serum cytokine profiling of COVID-19 positive patients. Our results demonstrate the nanoplasmonic digital immunoassay as a promising practical tool for comprehensive characterization of cytokine storm in patients that holds great promise as an intelligent immunoassay for next generation immune monitoring.

Gao Zhuangqiang, Song Yujing, Hsiao Te Yi, He Jiacheng, Wang Chuanyu, Shen Jialiang, MacLachlan Alana, Dai Siyuan, Singer Benjamin H, Kurabayashi Katsuo, Chen Pengyu


coronavirus disease 2019, cytokine storm, digital/single-molecule detection, machine learning, microfluidic immunoassay, nanoplasmonics