[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126798-en":3,"doc-seo-126798-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126798,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2","Pietiäinen and colleagues present a miniaturized, automated microscopy-based serology assay for patient blood samples that uses machine learning-guided image analysis. The platform simultaneously measures IgM, IgA, and IgG responses to multiple SARS-CoV-2 antigens via transfection-expressed targets, enabling low-biosafety operation. Built as a high-throughput, objective workflow, it differentiates vaccine-induced from infection-induced antibody patterns and includes a web resource for quantitative visualization and distribution comparisons.","Article  \nImage-based and machine learning-guided multiplexed serology test for SARS-CoV-2  \nAuthors  \nVilja Pieti¨ainen, Minttu Polso, Ede Migh, ..., Lassi Paavolainen, Peter Horvath, Jussi Hepojoki  \nCorrespondence  \nvilja.pietiainen@helsinki.fi (V.P.), [horvath.peter@brc.hu](horvath.peter@brc.hu) (P.H.), jussi.hepojoki@helsinki.fi (J.H.)  \nIn brief  \nPieti¨ainen et al. have developed an automated, miniaturized, microscopybased assay for multiparametric analysis of antibody responses in patient blood samples. Using SARS-CoV-2 antigens, they demonstrate that it is a costeffective, rapid, and reliable serology test with low biosafety level requirements and adaptability to other pathogens.  \nd Combines transfection-based antigens, high-throughput imaging, and machine learning  \nd Rapid and cost effective with low biosafety level requirements  \nd Tested using SARS-CoV-2 antigens but adaptable to other pathogens  \nPieti¨ainen et al., 2023, Cell Reports Methods 3, 100565 August 28, 2023 ª 2023 The Authors.  \n[https://doi.org/10.1016/j.crmeth.2023.100565](https://doi.org/10.1016/j.crmeth.2023.100565)  \nll  \nll  \nOPEN ACCESS  \nArticle  \nImage-based and machine learning-guided multiplexed serology test for SARS-CoV-2  \nVilja Pieti¨ainen,1,24,* Minttu Polso,1,23 Ede Migh,2,23 Christian Guckelsberger,1,3,4,5,23 Maria Harmati,2 Akos Diosdi,2 Laura Turunen,1 Antti Hassinen,1 Swapnil Potdar,1 Annika Koponen,6,7 Edina Gyukity Sebestyen,2 Ferenc Kovacs,2,8 Andras Kriston,2,8 Reka Hollandi,2 Katalin Burian,9 Gabriella Terhes,9 Adam Visnyovszki,10 Eszter Fodor,11  \n(Author list continued on next page)  \n1Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland  \n2Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Szeged, Hungary  \n3Department of Computer Science, Aalto University, Espoo, Finland  \n4Finnish Center for Artificial Intelligence, Espoo, Finland  \n5School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK  \n6Minerva Foundation Institute for Medical Research, Helsinki, Finland  \n7Department of Anatomy, Faculty of Medicine, University of Helsinki, Helsinki, Finland  \n8Single-Cell Technologies Ltd. , Szeged, Hungary  \n(Affiliations continued on next page)  \nMOTIVATION Serological assays are essential for studying and controlling infectious disease outbreaks, such as the current coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) . Immunofluorescence assays relying on cells infected with a given virus provide rapidly available means for demonstrating antibody response against emerging pathogens. However, setting up such assays may require a high-biosafety-level facility for handling the virus. In addition, such assays are often low throughput, and interpretation of the results can be labor intensive and subjective. Here, we describe a mini-immunofluorescence assay (mini-IFA), an automated, high-throughput, microscopy-based serology assay requiring a low-biosafety-level environment. We demonstrate its efficacy using SARS-CoV-2 as a model.  \nSUMMARY  \nWe 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) asthe 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","cbCaipnW4sftEi8m","https://ap.wps.com/l/cbCaipnW4sftEi8m","pdf",6438361,1,20,"English","en",105,"# Motivation\n# Summary\n# Introduction\n## Mini-IFA design and workflow\n## Machine learning image analysis\n## Differentiating vaccine vs infection responses","[{\"question\":\"What is the mini-IFA assay described for SARS-CoV-2?\",\"answer\":\"It is a miniaturized immunofluorescence serology assay that uses automated, high-content microscopy with machine learning-guided image analysis to detect antibodies in patient blood samples.\"},{\"question\":\"How does the assay achieve multiplexing while requiring low biosafety levels?\",\"answer\":\"It uses transfection-expressed viral antigens instead of infected cells, allowing simultaneous measurement of IgM, IgA, and IgG against different antigens under low-biosafety conditions.\"},{\"question\":\"What capability does the study demonstrate using SARS-CoV-2 as a model pathogen?\",\"answer\":\"The method differentiates vaccine-induced from infection-induced antibody responses and provides quantitative visualization of sample-specific results through a dedicated web page.\"}]","Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2 | 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