ABSTRACT
The need to develop high-throughput diagnostic platforms for infectious diseases has never been more evident than with the emergence of SARS-CoV-2 and the ensued COVID-19 pandemic. Microfluidics, in tandem with its multiplexing capabilities, high sensitivity, and potential for automation, provides a unique advantage towards the development of high-throughput serological diagnostic platforms. Here, we present a microfluidic device that detects IgG or IgM raised against four SARS-CoV-2 antigens (spike, S;S1 subunit, S1;the receptor-binding domain, RBD;and nucleocapsid, N) from 50 serum samples in parallel. We validated the platform with a cross-sectional cohort of 66 samples from confirmed COVID-19 patients and a pre-pandemic control of 34 serum samples collected in 2018. The analysis of both antibodies against all four viral antigens provided a sensitivity of 90.4% and a specificity of 94.1%, with both parameters increasing to 100% in late-stage samples (21-30 days after symptoms onset). We expect our device to open the door to massive serological testing, impacting diagnostics, vaccine development, and epidemiological understanding of COVID-19. © 2021 MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.
ABSTRACT
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 â¼6,000 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.