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

• DOI: 10.1016/j.crmeth.2023.100565
• Publication Date: 2023-08-22T14:34:48Z
• Authors: Vilja Pietiäinen; Minttu Polso; Ede Migh; Christian Guckels...; Maria Harmati; Akos Diosdi; Laura Turunen; Antti Hassinen; Swapnil Potdar; Annika Koponen; Edina Gyukity Seb...; Ferenc Kovacs; Andras Kriston; Reka Hollandi; Katalin Burian; Gabriella Terhes; Adam Visnyovszki; Eszter Fodor; Zsombor Lacza; Anu Kantele; Pekka Kolehmainen; Laura Kakkola; Tomas Strandin; Lev Levanov; Olli Kallioniemi; Lajos Kemeny; Ilkka Julkunen; Olli Vapalahti; Krisztina Buzas; Lassi Paavolainen; Peter Horvath; Jussi Hepojoki
• Keywords: Immunofluorescence; Biosafety; Multiplex

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, IgG responses against different viral antigens an automated high-throughput manner. relies on expressed through transfection, enabling use at low biosafety level fast adaptation to emerging pathogens. Using severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the model pathogen, we demonstrate that this allows differentiation between vaccine-induced infection-induced responses. Additionally, established dedicated web page quantitative visualization sample-specific results their distribution, comparing them with controls other Our provide proof concept approach, demonstrating accurate research setup prospects clinical diagnostics.