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Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2
Vilja Pietiainen; Minttu Polso; Ede Migh; Christian Guckelsberger; Maria Harmati; Akos Diosdi; Laura Turunen; Antti Hassinen; Swapnil Potdar; Annika Koponen; Edina Gyukity-Sebestyen; Ferenc Kovacs; Andras Kriston; Reka Hollandi; Katalin Burian; Gabriella Terhes; Adam Visnyovszki; Eszter Fodor; Zsombor Lacza; Anu Kantele; Pekka Kolehmainen; Laura Kakkola; Tomas Strandin; Lev Levanov; Olli Kallioniemi; Lajos Kemeny; Ilkka Julkunen; Olli Vapalahti; Krisztina Buzas; Lassi Paavolainen; Peter Horvath; Jussi Hepojoki.
Afiliação
  • Vilja Pietiainen; Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland
  • Minttu Polso; Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland
  • Ede Migh; Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Szeged, Hungary
  • Christian Guckelsberger; Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland; Department of Computer
  • Maria Harmati; Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Szeged, Hungary
  • Akos Diosdi; Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Szeged, Hungary
  • Laura Turunen; Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland
  • Antti Hassinen; Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland
  • Swapnil Potdar; Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland
  • Annika Koponen; Minerva Foundation Institute for Medical Research, Helsinki, Finland; Department of Anatomy, Faculty of Medicine, University of Helsinki, Helsinki, Finland
  • Edina Gyukity-Sebestyen; Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Szeged, Hungary
  • Ferenc Kovacs; Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Szeged, Hungary; Single-Cell Technologies
  • Andras Kriston; Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Szeged, Hungary; Single-Cell Technologies
  • Reka Hollandi; Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Szeged, Hungary
  • Katalin Burian; Department of Medical Microbiology, Faculty of Medicine, University of Szeged, Szeged, Hungary
  • Gabriella Terhes; Department of Medical Microbiology, Faculty of Medicine, University of Szeged, Szeged, Hungary
  • Adam Visnyovszki; 1st Department of Internal Medicine, Faculty of Medicine, University of Szeged, Szeged, Hungary
  • Eszter Fodor; Department of Sports Physiology, Inst. Sports and Health Sciences, University of Physical Education, Budapest, Hungary
  • Zsombor Lacza; Department of Sports Physiology, Inst. Sports and Health Sciences, University of Physical Education, Budapest, Hungary
  • Anu Kantele; Meilahti Infectious Diseases and Vaccine Research Center, MeiVac, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Human Microbiome R
  • Pekka Kolehmainen; Institute of Biomedicine, University of Turku, Turku, Finland
  • Laura Kakkola; Institute of Biomedicine, University of Turku, Turku, Finland
  • Tomas Strandin; Department of Virology, Medicum, University of Helsinki, Helsinki, Finland
  • Lev Levanov; Department of Virology, Medicum, University of Helsinki, Helsinki, Finland
  • Olli Kallioniemi; Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland;Department of Oncology-
  • Lajos Kemeny; HCEMM-USZ Skin Research Group, Department of Dermatology and Allergology, University of Szeged, Szeged, Hungary
  • Ilkka Julkunen; Institute of Biomedicine, University of Turku, Turku, Finland; Turku University Hospital, Turku, Finland
  • Olli Vapalahti; Department of Virology, Medicum, University of Helsinki, Helsinki, Finland; Department of Veterinary Biosciences, University of Helsinki, Helsinki, Finland; HUS
  • Krisztina Buzas; Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Szeged, Hungary; Department of Immunology,
  • Lassi Paavolainen; Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland
  • Peter Horvath; Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland; Laboratory of Microsco
  • Jussi Hepojoki; Department of Virology, Medicum, University of Helsinki, Helsinki, Finland; University of Zurich, Vetsuisse Faculty, Institute of Veterinary Pathology, Zurich,
Preprint em Inglês | medRxiv | ID: ppmedrxiv-22279729
ABSTRACT
Here, we describe a scalable and automated, high-content microscopy -based mini-immunofluorescence assay (mini-IFA) for serological testing i.e., detection of antibodies. Unlike conventional IFA, which often relies on the use of cells infected with the target pathogen, our assay employs transfected cells expressing individual viral antigens. The assay builds on a custom neural network-based image analysis pipeline for the automated and multiplexed detection of immunoglobulins (IgG, IgA, and IgM) in patient samples. As a proof-of-concept, we employed high-throughput equipment to set up the assay for measuring antibody response against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection with spike (S), membrane (M), and nucleo (N) proteins, and the receptor-binding domain (R) as the antigens. We compared the automated mini-IFA results from hundreds of patient samples to the visual observations of human experts and to the results obtained with conventional ELISA. The comparisons demonstrated a high correlation to both, suggesting high sensitivity and specificity of the mini-IFA. By testing pre-pandemic samples and those collected from patients with RT-PCR confirmed SARS-CoV-2 infection, we found mini-IFA to be most suitable for IgG and IgA detection. The results demonstrated N and S proteins as the ideal antigens, and the use of these antigens can serve to distinguish between vaccinated and infected individuals. The assay principle described enables detection of antibodies against practically any pathogen, and none of the assay steps require high biosafety level environment. The simultaneous detection of multiple Ig classes allows for distinguishing between recent and past infection. Public abstractThe manuscript describes a miniaturized immunofluorescence assay (mini-IFA) for measuring antibody response in patient blood samples. The automated method builds on machine-learning -guided image analysis with SARS-CoV-2 as the model pathogen. The method enables simultaneous measurement of IgM, IgA, and IgG responses against different virus antigens in a high throughput manner. The assay relies on antigens expressed through transfection and allows for differentiation between vaccine-induced and infection-induced antibody responses. The transfection-based antigen expression enables performing the assay at a low biosafety level laboratory and allows fast adaptation of the assay to emerging pathogens. Our results provide proof-of-concept for the approach, demonstrating fast and accurate measurement of antibody responses in a clinical and research set-up.
Licença
cc_by_nc_nd
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Estudo diagnóstico / Estudo observacional / Estudo prognóstico Idioma: Inglês Ano de publicação: 2022 Tipo de documento: Preprint
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Estudo diagnóstico / Estudo observacional / Estudo prognóstico Idioma: Inglês Ano de publicação: 2022 Tipo de documento: Preprint
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