Issue 32, 2019

Core–shell CdSeTe/ZnS quantum dots for the detection of microRNA-155 based on the fluorescence resonance energy transfer technique via the formation of a network structure

Abstract

The microRNA family shows great promise for the diagnosis and prediction of different cancers as biomarkers, in which miRNA-155 (miR155) is recognized as a biomarker for lung cancer and thus has drawn much attention. Therefore, the development of specific and accurate methods to detect miR155 has become a potential research hotspot. In this work, we presented a fluorescence resonance energy transfer (FRET) technique by utilizing core–shell CdSeTe/ZnS quantum dots (QDs) as a signal reporter and a target recycling strategy as a signal amplifier for the detection of miR155. In the presence of miR155, part of the hairpin probe (HP) immobilized onto the surface of gold nanoparticles (AuNPs)@SiO2, could hybridize with it. Then, numerous H1-AuNPs@SiO2 could be produced with the aid of exonuclease III. With the linkage of the assistant chain A2, H1-AuNPs@SiO2 combined with A1-QDs and then the fluorescence (FL) signal of QDs was quenched by AuNPs@SiO2via FRET. The more miR155 present in solution, the weaker the detected FL signal. A linear response range of 0.01 fM to 1 nM and low detection limit of 3 aM were obtained using the proposed method. Furthermore, this method showed high specificity and satisfactory recovery between 93 and 105% in real human serum samples, illustrating its great potential for practical applications.

Graphical abstract: Core–shell CdSeTe/ZnS quantum dots for the detection of microRNA-155 based on the fluorescence resonance energy transfer technique via the formation of a network structure

Article information

Article type
Paper
Submitted
14 Apr 2019
Accepted
29 Jun 2019
First published
11 Jul 2019

Anal. Methods, 2019,11, 4137-4145

Core–shell CdSeTe/ZnS quantum dots for the detection of microRNA-155 based on the fluorescence resonance energy transfer technique via the formation of a network structure

F. Cui, Q. Qiu, G. Peng, X. Li, X. Liu and X. Chen, Anal. Methods, 2019, 11, 4137 DOI: 10.1039/C9AY00783K

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