Introduction
The COVID-19 pandemic has emphasized the need for precise and reliable methods to assess immune responses, improve viral surveillance, and evaluate vaccine efficacy. This study employs flow cytometry in conjunction with magnetic beads functionalized with SARS-CoV-2 S-proteins to quantify virus-specific antibodies. Compared to ELISA, this method offers superior specificity, higher sensitivity, reduced background interference, and more precise quantitative assessment.
Methods
Magnetic beads coated with the SARS-CoV-2 S-protein were incubated with human serum samples to capture virus-specific antibodies. These bead-antibody complexes were subsequently analyzed using flow cytometry. Antibody–bound beads were differentiated from unbound beads based on fluorescence intensity detected by flow cytometry. Bound antibodies were labeled with fluorescently conjugated secondary antibodies specific for human immunoglobulins, resulting in increased fluorescence intensity compared to unbound beads, enabling precise quantification and differentiation. The results obtained were validated against the commercially available anti-SARS-CoV-2 QuantiVac IgG ELISA assay.
Results
Flow cytometry demonstrated remarkable sensitivity and specificity in detecting SARS-CoV-2 antibodies at serum dilutions of 1:100 and 1:400. The results closely aligned with commercial ELISAs, confirming the accuracy and reliability of this alternative diagnostic method.
Conclusion
Flow cytometry utilizing magnetic bead-coupled S-protein presents a robust and precise tool for evaluating COVID-19 antibody responses. This technique enhances existing serological diagnostic platforms by significantly reducing background interference and enabling precise antibody quantification and provides valuable insights into immune response assessments in both clinical and research settings.
Acknowledgment
This research was conducted in collaboration with the National Laboratory of Virology and the Flow Cytometry Core Facility at the University of Pécs. Funding was provided by the National Research, Development, and Innovation Office of Hungary through grants 2020-2.1.1-ED-2020-00100 and RRF-2.3.1-21-2022-00010.
