{"entity": "publication", "iuid": "b88ce8505f7f481fa7190f18e02063e0", "timestamp": "2026-08-25T21:28:58.332Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/b88ce8505f7f481fa7190f18e02063e0.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/b88ce8505f7f481fa7190f18e02063e0"}}, "title": "Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2.", "authors": [{"family": "Pieti\u00e4inen", "given": "Vilja", "initials": "V"}, {"family": "Polso", "given": "Minttu", "initials": "M"}, {"family": "Migh", "given": "Ede", "initials": "E"}, {"family": "Guckelsberger", "given": "Christian", "initials": "C"}, {"family": "Harmati", "given": "Maria", "initials": "M"}, {"family": "Diosdi", "given": "Akos", "initials": "A"}, {"family": "Turunen", "given": "Laura", "initials": "L"}, {"family": "Hassinen", "given": "Antti", "initials": "A"}, {"family": "Potdar", "given": "Swapnil", "initials": "S"}, {"family": "Koponen", "given": "Annika", "initials": "A"}, {"family": "Sebestyen", "given": "Edina Gyukity", "initials": "EG"}, {"family": "Kovacs", "given": "Ferenc", "initials": "F"}, {"family": "Kriston", "given": "Andras", "initials": "A"}, {"family": "Hollandi", "given": "Reka", "initials": "R"}, {"family": "Burian", "given": "Katalin", "initials": "K"}, {"family": "Terhes", "given": "Gabriella", "initials": "G"}, {"family": "Visnyovszki", "given": "Adam", "initials": "A"}, {"family": "Fodor", "given": "Eszter", "initials": "E"}, {"family": "Lacza", "given": "Zsombor", "initials": "Z"}, {"family": "Kantele", "given": "Anu", "initials": "A"}, {"family": "Kolehmainen", "given": "Pekka", "initials": "P"}, {"family": "Kakkola", "given": "Laura", "initials": "L"}, {"family": "Strandin", "given": "Tomas", "initials": "T"}, {"family": "Levanov", "given": "Lev", "initials": "L"}, {"family": "Kallioniemi", "given": "Olli", "initials": "O"}, {"family": "Kemeny", "given": "Lajos", "initials": "L"}, {"family": "Julkunen", "given": "Ilkka", "initials": "I"}, {"family": "Vapalahti", "given": "Olli", "initials": "O"}, {"family": "Buzas", "given": "Krisztina", "initials": "K"}, {"family": "Paavolainen", "given": "Lassi", "initials": "L"}, {"family": "Horvath", "given": "Peter", "initials": "P"}, {"family": "Hepojoki", "given": "Jussi", "initials": "J"}], "type": "journal article", "published": "2023-08-28", "journal": {"title": "Cell Rep Methods", "issn": "2667-2375", "volume": "3", "issue": "8", "pages": "100565", "issn-l": null}, "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.", "doi": "10.1016/j.crmeth.2023.100565", "pmid": "37671026", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC10475844"}, {"db": "pii", "key": "S2667-2375(23)00209-6"}], "notes": [], "created": "2026-08-20T07:53:19.492Z", "modified": "2026-08-20T07:53:19.539Z"}