An ultrasensitive fluorescence aptasensor for carcino-embryonic antigen detection based on fluorescence resonance energy transfer from upconversion phosphors to Au nanoparticles (original) (raw)
* Corresponding authors
a Medical Technology and Engineering College, Fujian Medical University, Fuzhou 350004, Fujian, People's Republic of China
E-mail: cmjy503@126.com
b Department of Laboratory Medicine, The 1st Affiliated Hospital of Fujian Medical University, 20 Chazhong Road, Fuzhou 350004, Fujian, People's Republic of China
E-mail: ouqishui@163.com
c Faculty of Pharmacy, Fujian Medical University, Fuzhou 350108, People's Republic of China
Abstract
A novel fluorescence resonance energy transfer (FRET)-based aptasensor was proposed for carcino-embryonic antigen (CEA) detection utilizing NaYF4:Yb3+, Er3+ upconversion nanoparticles (UCNPs) and gold nanoparticles (Au-NPs) as the energy donor and acceptor, respectively. The FRET process occurs when the single-stranded DNA modified UCNPs and Au-NPs are linked together by the CEA aptamer, which leads to the fluorescence quenching of UCNPs. However, the presence of CEA splits the above fluorescence-suppressed Au-NPs–aptamer–UCNPs sandwich complex, resulting in the fluorescence recovery of UCNPs. The recovery of fluorescence intensity is linearly correlated to the concentration of CEA in the range of 0.05–2.0 ng mL−1, enabling the detection and quantification of CEA. Such a sandwich-type FRET-based assay possesses a low limit of detection (0.02 ng mL−1) and satisfactory selectivity and reproducibility, indicating that this biosensor is applicable for the trace detection of the CEA protein.
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Article information
DOI
https://doi.org/10.1039/C7AY02803B
Article type
Paper
Submitted
05 Dec 2017
Accepted
24 Feb 2018
First published
26 Feb 2018
Download Citation
Anal. Methods, 2018,10, 1552-1559
Permissions
An ultrasensitive fluorescence aptasensor for carcino-embryonic antigen detection based on fluorescence resonance energy transfer from upconversion phosphors to Au nanoparticles
X. Li, W. Sun, J. Wu, Y. Gao, J. Chen, M. Chen and Q. Ou,Anal. Methods, 2018, 10, 1552DOI: 10.1039/C7AY02803B
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