Fluorescence biosensing micropatterned surfaces based on immobilized human acetylcholinesterase
Human acetylcholinesterase (AChE) is a widely studied target enzyme in drug discovery for Alzheimer’s disease (AD). In this paper we report evaluation of the optimum structure and chemistry of the supporting material for a new AChE-based fluorescence sensing surface. To achieve this objective, multilayered silicon wafers with spatially controlled geometry and chemical diversity were fabricated. Specifically, silicon wafers with silicon oxide patterns (SiO2/Si wafers), platinum-coated silicon wafers with SiO2 patterns (SiO2/Pt/Ti/Si wafers), and Pt-coated wafers coated with different thicknesses of TiO2 and SiO2 (SiO2/TiO2/Pt/Ti/Si wafers) were labelled with the fluorescent conjugation agent HiLyte Fluor 555. Selection of a suitable material and the optimum pattern thickness required to maximize the fluorescence signal and maintain chemical stability was performed by confocal laser-scanning microscopy (CLSM). Results showed that the highest signal-to-background ratio was always obtained on wafers with 100 nm thick SiO2 features. Hence, these wafers were selected for covalent binding of human AChE. Batch-wise kinetic studies revealed that enzyme activity was retained after immobilization. Combined use of atomic-force microscopy and CLSM revealed that AChE was homogeneously and selectively distributed on the SiO2 microstructures at a suitable distance from the reflective surface. In the optimum design, efficient fluorescence emission was obtained from the AChE-based biosensing surface after labelling with propidium, a selective fluorescent probe of the peripheral binding site of AChE.
KeywordsAcetylcholinesterase Micropatterned silicon wafer Biosensing surface Atomic-force microscopy Confocal scanning microscopy Fluorescence labelling
This work was supported by a European Union-funded FP7 grant (Bio-Inspired Self-assembled Nano-Enabled Surfaces—BISNES, grant number 214538). Financial support from the University of Bologna (RFO) and MIUR (PRIN 2009) is also gratefully acknowledged. Davide Cavalletti is gratefully acknowledged for technical assistance with CLSM.
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