Pietiäinen, Vilja and Polso, Minttu and Migh, Ede and Guckelsberger, Christian and Harmati, Mária and Diósdi, Ákos and Turunen, Laura and Hassinen, Antti and Gyukity-Sebestyén, Edina and Kovács, Ferenc and Kriston, András and Hollandi, Réka and Burián, Katalin and Terhes, Gabriella and Fodor, Eszter and Lacza, Zsombor and Buzás, Krisztina and Horváth, Péter (2023) Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2. CELL REPORTS METHODS, 3 (8). ISSN 2667-2375
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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 mea- surement 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 co- ronavirus 2 (SARS-CoV-2) as the model pathogen, we demonstrate that this method allows differentiation be- tween 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.
Item Type: | Article |
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Subjects: | Q Science / természettudomány > QH Natural history / természetrajz > QH301 Biology / biológia |
SWORD Depositor: | MTMT SWORD |
Depositing User: | MTMT SWORD |
Date Deposited: | 26 Sep 2023 11:13 |
Last Modified: | 26 Sep 2023 11:13 |
URI: | http://real.mtak.hu/id/eprint/175067 |
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