A simple agglutination system for rapid antigen detection from large sample volumes with enhanced sensitivity.

ABSTRACT

Lateral flow assays (LFAs) provide a simple and quick option for diagnosis and are widely adopted for point-of-care or at-home tests. However, their sensitivity is often limited. Most LFAs only allow 50 µL samples while various sample types such as saliva could be collected in much larger volumes. Adapting LFAs to accommodate larger sample volumes can improve assay sensitivity by increasing the number of target analytes available for detection. Here, a simple agglutination system comprising biotinylated antibody (Ab) and streptavidin (SA) is presented. The Ab and SA agglutinate into large aggregates due to multiple biotins per Ab and multiple biotin binding sites per SA. Dynamic light scattering (DLS) measurements showed that the agglutinated aggregate could reach a diameter of over 0.5 µm and over 1.5 µm using poly-SA. Through both experiments and Monte Carlo modeling, we found that high valency and equivalent concentrations of the two aggregating components were critical for successful agglutination. The simple agglutination system enables antigen capture from large sample volumes with biotinylated Ab and a swift transition into aggregates that can be collected via filtration. Combining the agglutination system with conventional immunoassays, an agglutination assay is proposed that enables antigen detection from large sample volumes using an in-house 3D-printed device. As a proof-of-concept, we developed an agglutination assay targeting SARS-CoV-2 nucleocapsid antigen for COVID-19 diagnosis from saliva. The assay showed a 10-fold sensitivity enhancement when increasing sample volume from 50 µL to 2 mL, with a final limit of detection (LoD) of 10 pg mL-1 (∼250 fM). The assay was further validated in negative saliva spiked with gamma-irradiated SARS-CoV-2 and showed an LoD of 250 genome copies per µL. The proposed agglutination assay can be easily developed from existing LFAs to facilitate the processing of large sample volumes for improved sensitivity.