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

Cell Rep Methods. 2023 Aug 22;3(8):100565. doi: 10.1016/j.crmeth.2023.100565. eCollection 2023 Aug 28.

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.

Keywords: COVID-19; SARS-CoV-2; antibody response; cell-based assay; high-content imaging; immunofluorescence assay; machine learning; mini-IFA; serology; virus.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Acclimatization
  • COVID-19 Testing
  • COVID-19*
  • Humans
  • Machine Learning
  • SARS-CoV-2*