Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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2023-08-28
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en
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Cell Reports : Methods, Volume 3, issue 8
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.Description
Funding Information: The authors thank the Minerva Institute (Helsinki, Finland) for providing utilities for the project, Prof. Perttu Hämäläinen (Aalto University, Finland) for providing the expertise of his group for the project, the FIMM High Throughput Biomedicine Unit for providing access to high-throughput robotics, the FIMM High Content Imaging and Analysis Unit for HC imaging and analysis (HiLIFE, University of Helsinki and Biocenter Finland; EuroBioImaging, ISIDORe partner), and the CSC – IT Center for Science, Finland, for computational resources. We acknowledge support from the LENDULET-BIOMAG grant (2018-342), from the European Regional Development Funds ( GINOP-2.3.2-15-2016-00006 , GINOP-2.3.2-15-2016-00026 , and GINOP-2.3.2-15-2016-00037 ), from the H2020-discovAIR ( 874656 ), from the H2020 ATTRACT-SpheroidPicker , and from the Chan Zuckerberg Initiative , Seed Networks for the HCA-DVP. The Finnish TEKES/BusinessFinland FiDiPro Fellow Grant 40294/13 (to V.P., O.K., L.P., and P.H.), grants awarded by the Academy of Finland (iCOIN- 336496 to O.K., V.P., and O.V.; 308613 to J.H.; 321809 to T.S.; 310552 to L.P.; 337530 to I.J.; and FIRI2020-337036 to FIMM-HCA, A.H., L.P., V.P., and P.H.), the EU H2020 VEO project (O.V.), and a Minerva Foundation for COVID-19 Research project grant (to V.P.) are also acknowledged. C.G. is funded by the Academy of Finland Flagship program, Finnish Center for Artificial Intelligence. OrthoSera Ltd. was funded by NKFIH grants ( 2020-1.1.6-JÖVŐ-2021-00010 and TKP2020-NKA-17 ). The authors thank Dora Bokor, PharmD, for proofreading the manuscript.
Keywords
antibody response, cell-based assay, COVID-19, CP: Imaging, CP: Microbiology, high-content imaging, immunofluorescence assay, machine learning, mini-IFA, SARS-CoV-2, serology, virus
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Pietiäinen, V, Polso, M, Migh, E, Guckelsberger, C, Harmati, M, Diosdi, A, Turunen, L, Hassinen, A, Potdar, S, Koponen, A, Sebestyen, E G, Kovacs, F, Kriston, A, Hollandi, R, Burian, K, Terhes, G, Visnyovszki, A, Fodor, E, Lacza, Z, Kantele, A, Kolehmainen, P, Kakkola, L, Strandin, T, Levanov, L, Kallioniemi, O, Kemeny, L, Julkunen, I, Vapalahti, O, Buzas, K, Paavolainen, L, Horvath, P & Hepojoki, J 2023, ' Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2 ', Cell Reports : Methods, vol. 3, no. 8, 100565 . https://doi.org/10.1016/j.crmeth.2023.100565