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Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2


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.

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.

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Additional indexing

Item Type:Journal Article, not_refereed, original work
Communities & Collections:05 Vetsuisse Faculty > Veterinärwissenschaftliches Institut > Institute of Veterinary Pathology
Dewey Decimal Classification:570 Life sciences; biology
Scopus Subject Areas:Life Sciences > Biotechnology
Life Sciences > Biochemistry
Life Sciences > Biochemistry, Genetics and Molecular Biology (miscellaneous)
Life Sciences > Genetics
Health Sciences > Radiology, Nuclear Medicine and Imaging
Physical Sciences > Computer Science Applications
Uncontrolled Keywords:Computer Science Applications, Radiology, Nuclear Medicine and imaging, Genetics, Biochemistry, Genetics and Molecular Biology (miscellaneous), Biochemistry, Biotechnology
Language:English
Date:1 August 2023
Deposited On:01 Jan 2024 14:19
Last Modified:29 Jun 2024 01:41
Publisher:Cell Press (Elsevier)
ISSN:2667-2375
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1016/j.crmeth.2023.100565
PubMed ID:37671026
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)