Refereed journal article or data article (A1)

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

List of AuthorsPietiäinen Vilja, Polso Minttu, Migh Ede, Guckelsberger Christian, Harmati Maria, Diosdi Akos, Turunen Laura, Hassinen Antti, Potdar Swapnil, Koponen Annika, Sebestyen Edina Gyukity, Kovacs Ferenc, Kriston Andras, Hollandi Reka, Burian Katalin, Terhes Gabriella, Visnyovszki Adam, Fodor Eszter, Lacza Zsombor, Kantele Anu, Kolehmainen Pekka, Kakkola Laura, Strandin Tomas, Levanov Lev, Kallioniemi Olli, Kemeny Lajos, Julkunen Ilkka, Vapalahti Olli, Buzas Krisztina, Paavolainen Lassi, Horvath Peter, Hepojoki Jussi

PublisherCell Press

Publication year2023

Journal name in sourceCell reports methods

Journal acronymCell Rep Methods

Article number100565

Volume number3

Issue number8





Self-archived copy’s web address

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.

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Last updated on 2023-02-10 at 10:32