SERS imaging-based aptasensor for ultrasensitive and reproducible detection of influenza virus A

材料科学 纳米技术 化学 病毒学 生物
作者
Hao Chen,Sung‐Gyu Park,Namhyun Choi,Joung‐Il Moon,Hajun Dang,Anupam Das,Seunghun Lee,Do-Geun Kim,Lingxin Chen,Jaebum Choo
出处
期刊:Biosensors and Bioelectronics [Elsevier]
卷期号:167: 112496-112496 被引量:141
标识
DOI:10.1016/j.bios.2020.112496
摘要

Surface-enhanced Raman scattering (SERS)-based aptasensors display high sensitivity for influenza A/H1N1 virus detection but improved signal reproducibility is required. Therefore, in this study, we fabricated a three-dimensional (3D) nano-popcorn plasmonic substrate using the surface energy difference between a perfluorodecanethiol (PFDT) spacer and the Au layer. This energy difference led to Au nanoparticle self-assembly; neighboring nanoparticles then created multiple hotspots on the substrate. The localized surface plasmon effects at the hot spots dramatically enhanced the incident field. Quantitative evaluation of A/H1N1 virus was achieved using the decrease of Raman peak intensity resulting from the release of Cy3-labeled aptamer DNAs from nano-popcorn substrate surfaces via the interaction between the aptamer DNA and A/H1N1 virus. The use of a Raman imaging technique involving the fast mapping of all pixel points enabled the reproducible quantification of A/H1N1 virus on nano-popcorn substrates. Average ensemble effects obtained by averaging all randomly distributed hot spots mapped on the substrate made it possible to reliably quantify target viruses. The SERS-based imaging aptasensor platform proposed in this work overcomes the issues inherent in conventional approaches (the time-consuming and labor-intensiveness of RT-PCR and low sensitivity and quantitative analysis limits of lateral flow assay kits). Our SERS-based assay for detecting A/H1N1 virus had an estimated limit of detection of 97 PFU mL−1 (approximately three orders of magnitude more sensitive than that determined by the enzyme-linked immunosorbent assay) and the approximate assay time was estimated to be 20 min. Thus, this approach provides an ultrasensitive, reliable platform for detecting viral pathogens.

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