Epitope-imprinted nanogels for high-performance mass-based detection of pathogenic viruses

Rapid and selective detection of viral pathogens is essential for effective disease surveillance and outbreak control. To meet this demand, thermo-responsive epitope-imprinted nanogels were integrated into a quartz crystal microbalance (QCM) platform for the selective detection of Hepatitis A virus (HAV) in real samples. Two HAV-derived epitopes (Epitope 2 and Epitope 3), identified by in-silico analysis, were employed as templates for the solid-phase synthesis of molecularly imprinted polymer nanogels (MIP-NGs). Successful nanogel formation was confirmed by Fourier-transform infrared (FTIR) spectroscopy, dynamic light scattering, and electrophoretic light scattering. The binding affinities of the imprinted nanogels were comparatively evaluated using surface plasmon resonance, yielding dissociation constants (K_D) of 2.49 × 10^-11 M and 5.07 × 10^-13 M for Epitope-2 MIP-NGs and Epitope-3 MIP-NGs, respectively. Based on their superior affinity, Epitope-3 MIP-NGs were selected as recognition elements and immobilized onto the QCM sensor surface for mass-based virus detection. Two immobilization strategies were investigated: drop-casting and electrochemical deposition via cyclic voltammetry in the presence of o-aminophenol and graphene quantum dots. Sensor surface formation was verified by atomic force microscopy and contact angle measurements. HAV detection was performed at 38 °C, under conditions where nanogel collapse supports binding via epitope-specific cavities. Real-time QCM measurements revealed concentration-dependent frequency shifts corresponding to HAV binding, achieving a detection limit of 1.05 pM over a working concentration range of 1.33-85 pM. Selectivity was assessed using non-imprinted polymer nanogels yielding an imprinting factor of 1.9, while specificity was confirmed against other pathogenic viruses. Finally, successful HAV detection in human serum demonstrated the applicability of the sensing platform in complex biological matrices.