Journal
CELL REPORTS METHODS
Volume 3, Issue 8, Pages -Publisher
CELL PRESS
DOI: 10.1016/j.crmeth.2023.100565
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We have developed a miniaturized immunofluorescence assay (mini-IFA) that utilizes machine learning-guided image analysis to measure antibody response against different viral antigens. This method allows simultaneous measurement of IgM, IgA, and IgG responses and can differentiate between vaccine-induced and infection-induced antibody responses. The assay has the potential for clinical diagnostics.
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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