A high-throughput inhibition assay to study MERS-CoV antibody interactions using image cytometry

Abstract

The emergence of new pathogens, such as Middle East respiratory syndrome coronavirus (MERS-CoV), poses serious challenges to global public health and highlights the urgent need for methods to rapidly identify and characterize potential therapeutic or prevention options, such as neutralizing antibodies. Spike (S) proteins are present on the surface of MERS-CoV virions and mediate viral entry. S is the primary target for MERS-CoV vaccine and antibody development, and it has become increasingly important to understand MERS-CoV antibody binding specificity and function. Commonly used serological methods like ELISA, biolayer interferometry, and flow cytometry are informative, but limited. Here, we demonstrate a high-throughput protein binding inhibition assay using image cytometry. The image cytometry-based high-throughput screening method was developed by selecting a cell type with high DPP4 expression and defining optimal seeding density and protein binding conditions. The ability of monoclonal antibodies to inhibit MERS-CoV S binding was then tested. Binding inhibition results were comparable with those described in previous literature for MERS-CoV spike monomer and showed similar patterns as neutralization results. The coefficient of variation (CV) of our cell-based assay was <10%. The proposed image cytometry method provides an efficient approach for characterizing potential therapeutic antibodies for combating MERS-CoV that compares favorably with current methods. The ability to rapidly determine direct antibody binding to host cells in a high-throughput manner can be applied to study other pathogen-antibody interactions and thus can impact future research on viral pathogens.

Keywords: Antibody binding; Antibody neutralization; Celigo; Image cytometry; Inhibition assay; MERS-CoV.

Fig. 1

Exogenously expressed DPP4 in various cell lines. Fluorescence images (A) and fluorescence intensity FlowJo analysis histograms (B) comparing untransfected A549, BHK-21, or Vero E6 cells (left panels) to cells expressing optimal amounts of DPP4, depicted by red staining (right panels).

Fig. 2

Optimization of cell density for binding and inhibition assay. Bright field images of BHK-21 cells plated at various densities: 2 × 10⁴ cells/well (A), 5 × 10³ cells/well (B), 1 × 10³ cells/well (C), in a 96-well plate.

Fig. 3

MERS-CoV S1 and S proteins binding to DPP4-expressing cells. Whole well fluorescence images of MERS-CoV S1 (A) and MERS-CoV S (B) proteins binding DPP4-expressing BHK-21 cells. Both S1 monomer and S trimer proteins were stained with anti-MERS-CoV S polyclonal AL488, demonstrating binding to DPP4-expressing cells in green. FlowJo analysis of the image-based fluorescence intensity data for MERS-CoV S1 monomer (C) and S trimer (D) showed peak shifts resulting from binding.

Fig. 4

Ability of mAbs to inhibit MERS-CoV S protein binding to DPP4-expressing cells. Inhibition of MERS-CoV S binding to DPP4 by D12 mAb was measured by image cytometry (A) and fluorescence intensity was analyzed by FlowJo histograms (B). As a negative control, a non-relevant isotype mAb was used. Dose response curves of Log mAb concentration versus percent inhibition were generated for D12 (C), G2 (D), and G4 (E) mAbs. (C–E) Each dot represents duplicates. Error bars represent SEM.

Fig. 5

Ability of mAbs to neutralize MERS-CoV in cells exogenously-expressing DPP4. Neutralization of MERS-CoV pseudovirus by D12 (red), G2 (blue), and G4 (black) in DPP4-expressing BHK-21 cells. 50% neutralization titers (IC₅₀) were calculated based on sigmoidal curves. Each dot represents duplicates. Error bars represents SEM.