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 mea- surement 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 co- ronavirus 2 (SARS-CoV-2) as the model pathogen, we demonstrate
that this method allows differentiation be- tween 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.
