Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2

Vilja Pietiäinen, Minttu Polso, Ede Migh, Christian Guckelsberger, Maria Harmati, Akos Diosdi, Laura Turunen, Antti Hassinen, Swapnil Potdar, Annika Koponen, Edina Gyukity Sebestyen, Ferenc Kovacs, Andras Kriston, Reka Hollandi, Katalin Burian, Gabriella Terhes, Adam Visnyovszki, Eszter Fodor, Zsombor Lacza, Anu Kantele et al. (12 additional authors not shown)

Abstract: 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.

External IDs:doi:10.1016/j.crmeth.2023.100565