Bionano Interface Optimization for Rational Lateral Flow Assay Development

Point-of-care diagnostic tools, such as lateral flow assays (LFAs), play a critical role in disease management and outbreak control. LFAs detect the presence of target antigens in disease-relevant biofluids, utilizing nanoparticles (termed detection probes) to produce colorimetric readouts. However, significant intra- and interpatient variation in the biochemical composition of biofluids has downstream consequences for assay performance. Robust LFAs must be able to function alongside such variability to produce reliable and reproducible test outcomes. Beyond this, biofluids (such as serum) contain significant amounts of proteins, which can interact with detection probes used in LFAs to form a protein corona. The consequences of protein corona formation on LFA performance are poorly understood. Using a model antigen-biofluid LFA (human epidermal growth factor receptor 2 (HER2) and human serum), we observed significant discrepancies in LFA performance when using conventional nanoparticle functionalization methods, including the use of generic, nonhuman protein blocking agents. To overcome these performance differences, we developed a methodology for Bionano interface Optimization for LFA Design (termed BOLD). The BOLD workflow employs mass spectrometry-based proteomics to characterize the native protein corona, followed by formation of an engineered corona to produce an optimized bionano interface. We identified a specific protein (kininogen-1, KNG1) that demonstrated negative interference, significantly reducing the observed LFA test line intensity. This experimental finding is complemented by Molecular Dynamics simulations, which probe the binding modes of KNG1 to platinum nanoparticles. Further, through the employment of an apolipoprotein engineered corona (apolipoprotein A1, B, and C3), a robust LFA was developed, increasing test line intensity and significantly reducing intersample variation (with over a 4-fold improvement in the coefficient of variation). Overall, the BOLD workflow presents a method for the rational optimization of detection probes in LFAs through the characterization of the bionano interface to produce robust LFAs.