Image-based & machine learning-guided multiplexed serology test for SARS-CoV-2
Authors: Pietiäinen Vilja , Polso Minttu , Migh Ede , Guckelsberger Christian , Harmati Maria , Diosdi Akos , Turunen Laura , Hassinen Antti , Potdar Swapnil , Koponen Annika , Sebestyen Edina Gyukity , Kovacs Ferenc , Kriston Andras , Hollandi Reka , Burian Katalin , Terhes Gabriella , Visnyovszki Adam , Fodor Eszter , Lacza Zsombor , Kantele Anu , Kolehmainen Pekka , Kakkola Laura , Strandin Tomas , Levanov Lev , Kallioniemi Olli , Kemeny Lajos , Julkunen Ilkka , Vapalahti Olli , Buzas Krisztina , Paavolainen Lassi , Horvath Peter , Hepojoki Jussi 
Published: September 2022

AbstractHere, 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.