Quantitative circular flow immunoassays with trained object recognition to detect antibodies to SARS-CoV-2 membrane glycoprotein.

ABSTRACT

Amidst infectious disease outbreaks, a practical tool that can quantitatively monitor individuals' antibodies to pathogens is vital for disease control. The currently used serological lateral flow immunoassays (LFIAs) can only detect the presence of antibodies for a single antigen. Here, we fabricated a multiplexed circular flow immunoassay (CFIA) test strip with YOLO v4-based object recognition that can quickly quantify and differentiate antibodies that bind membrane glycoprotein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or hemagglutinin of influenza A (H1N1) virus in the sera of immunized mice in one assay using one sample. Spot intensities were found to be indicative of antibody titers to membrane glycoprotein of SARS-CoV-2 and were, thus, quantified relative to spots from immunoglobulin G (IgG) reaction in a CFIA to account for image heterogeneity. Quantitative intensities can be displayed in real time alongside an image of CFIA that was captured by a built-in camera. We demonstrate for the first time that CFIA is a specific, multi-target, and quantitative tool that holds potential for digital and simultaneous monitoring of antibodies recognizing various pathogens including SARS-CoV-2.