A self-calibrating electrochemical aptasensing platform: Correcting external interference errors for the reliable and stable detection of avian influenza viruses

Abstract

Conventional electrochemical biosensing systems rely on a single output signal, which limits their certain practical application, specifically from the viewpoint of external interference factors causing electrochemical signal errors. This study reports a self-calibrating dual-electrode based electrochemical aptasensor for the reliable and independent detection of avian influenza viruses (AIVs), which are the primary cause of highly contagious respiratory diseases, under external interference factors. Both electrodes were fabricated using tungsten rods surface-modified with a 3D nanostructured porous silica film (3DNRE). Subsequently, methylene blue (MB) was loaded as a redox-active material into the pores and capped with corresponding aptamer. One electrode was capped with an anti-AIV nucleoprotein (NP) aptamer (AptAIV-MB@3DNRE) allowing target-specific binding, resulting in changes in electrochemical signal upon diffusional release of the loaded redox molecules. The other electrode was capped with a control aptamer (Aptcon-MB@3DNRE), serving as a reference to correct false responses generated by nonspecific aptamer detachment and MB release under environmental changes in pH and ion strength and presence of nontarget molecules from cell lysis debris. In the dual-electrode platform, Aptcon-MB@3DNRE provides a corrected baseline for the fluctuating original output signals from AptAIV-MB@3DNRE. Consequently, this dual-electrode platform exhibits excellent output-signal stability (relative standard deviation, RSD: 5.86%) compared to a conventional single-electrode platform (RSD: 30.13%) at equivalent concentrations of AIV NP samples under different reaction buffer conditions. Moreover, no further purification and washing steps were required, indicating that the strategy may represent a universal and reliable platform for the electrochemical aptamer-based detection of various biomolecules.