@article{MTMT:34107493,
	title = {Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2},
	url = {https://m2.mtmt.hu/api/publication/34107493},
	author = {Pietiäinen, Vilja and Polso, Minttu and Migh, Ede and Guckelsberger, Christian and Harmati, Mária and Diósdi, Ákos and Turunen, Laura and Hassinen, Antti and Potdar, Swapnil and Koponen, Annika and Gyukity-Sebestyén, Edina and Kovács, Ferenc and Kriston, András and Hollandi, Réka and Burián, Katalin and Terhes, Gabriella and Visnyovszki, Ádám and Fodor, Eszter and Lacza, Zsombor and Kantele, Anu and Kolehmainen, Pekka and Kakkola, Laura and Strandin, Tomas and Levanov, Lev and Kallioniemi, Olli and Kemény, Lajos and Julkunen, Ilkka and Vapalahti, Olli and Buzás, Krisztina and Paavolainen, Lassi and Horváth, Péter and Hepojoki, Jussi},
	doi = {10.1016/j.crmeth.2023.100565},
	journal-iso = {CELL REP METH},
	journal = {CELL REPORTS METHODS},
	volume = {3},
	unique-id = {34107493},
	issn = {2667-2375},
	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.},
	year = {2023},
	eissn = {2667-2375},
	orcid-numbers = {Harmati, Mária/0000-0002-4875-5723; Diósdi, Ákos/0000-0002-3118-5576; Gyukity-Sebestyén, Edina/0000-0003-1383-6301; Burián, Katalin/0000-0003-1300-2374; Terhes, Gabriella/0000-0002-7301-9672; Kemény, Lajos/0000-0002-2119-9501; Buzás, Krisztina/0000-0001-8933-2033}
}