Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2 - Pietiäinen Vilja; Polso Minttu; Migh Ede; Guckelsberger Christian; Harmati Maria; Diosdi Akos; Turunen Laura; Hassinen Antti; Potdar Swapnil; Koponen Annika; Sebestyen Edina Gyukity; Kovacs Ferenc; et al. (Cell Press, 2025)
We present a miniaturized immunofluorescence assay (mini-IFA) for measuring antibody response in patient blood samples. The method utilizes machine learning-guided image analysis and enables simultaneous measurement of immunoglobulin M (IgM), IgA, and IgG responses against different viral antigens in an automated and high-throughput manner. The assay relies on antigens expressed through transfection, enabling use at a low biosafety level and fast adaptation to emerging pathogens. Using severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the model pathogen, we demonstrate that this method allows differentiation between vaccine-induced and infection-induced antibody responses. Additionally, we established a dedicated web page for quantitative visualization of sample-specific results and their distribution, comparing them with controls and other samples. Our results provide a proof of concept for the approach, demonstrating fast and accurate measurement of antibody responses in a research setup with prospects for clinical diagnostics.