Virus Detection Using Filament-Coupled Antibodies

Abstract

Two attractive features of ELISA are the specificity of antibody-antigen recognition and the sensitivity achieved by enzymatic amplification. We describe the development of a non-enzymatic virus detection platform based on circumferential bands of antibody probes coupled to a 120 mm diameter polyester filament. Automated processing was achieved by sequential positioning of filament-coupled probes through a series of liquid filled glass microreaction chambers. Antibody regions were first positioned within a microcapillary tube containing a solution of M13KO7 virus before being moved through subsequent chambers, where the filament-coupled probes were washed, exposed to a fluorescently labeled detecting antibody, and washed again. Using anti-M13KO7 mAb coupled to a polyester filament, the presence of 8.3 x 108 M13KO7 virus particles produced a 30-fold increase in fluorescence over an immobilized negative control antibody. Similar to ELISA, this filament-based approach had a lower limit of sensitivity of ~1.7 x 107 virus particles. We then combined the automated filament processing with an integrated laser-based optical detector to enable real-time controlled detection of virions in solution. A 638 nm laser with a photomultiplier at a right angle provided continuous monitoring for the presence of the fluorescently labeled detecting antibody. A virus incubation time of 1 minute detected 1010 virions/mL. Repeated incubations of antibody regions in either the virus or labeled antibody chambers increased fluorescence roughly proportional to the incubation times. This technology was used to identify and characterize a reovirus strain. We developed a decision tree that tested for reovirus with increasing specificity at each level of the tree. Using three types of reovirus and one bacteriophage, our system correctly detected and identified all three reovirus strains at a concentration of 2 x 1012 virions/ml and M13K07 phage at 3 x 1011 virions/ml. Fluorescence from all peak regions was determined to be significantly higher that background regions (p < 0.05). Using online feedback to guide testing, this scheme could easily be expanded into a much more complicated system with numerous levels and branches. This platform may prove attractive for point-of-care settings, the detection of biohazardous materials, or other applications where sensitive, rapid, and automated molecular recognition is desired.