Bionano Interface Optimization for Rational Lateral Flow Assay Development

Area of Science:

• Biotechnology
• Nanotechnology
• Biomedical Diagnostics

Background:

• Lateral flow assays (LFAs) are crucial point-of-care diagnostic tools.
• Biofluid variability and protein corona formation negatively impact LFA performance.
• Current LFA methods struggle with reproducibility due to complex biological matrices.

Purpose of the Study:

• To develop a robust methodology for optimizing nanoparticle detection probes in LFAs.
• To address the challenges posed by biofluid composition and protein corona formation.
• To enhance the reliability and reproducibility of LFA results.

Main Methods:

• Developed the Bionano interface Optimization for LFA Design (BOLD) workflow.
• Utilized mass spectrometry-based proteomics to characterize native protein coronas.
• Employed engineered protein coronas and Molecular Dynamics simulations for optimization.

Main Results:

• Identified kininogen-1 (KNG1) as a protein causing negative interference in LFAs.
• An engineered apolipoprotein corona (apolipoprotein A1, B, and C3) significantly improved LFA performance.
• Achieved over a 4-fold improvement in the coefficient of variation, reducing intersample variability.

Conclusions:

• The BOLD workflow enables rational optimization of the bionano interface for LFAs.
• Characterizing and engineering the protein corona is key to developing robust diagnostic tools.
• This approach enhances LFA sensitivity, specificity, and reliability in complex biofluids.