Issue 36, 2019

A fluorescent biosensor for highly specific and ultrasensitive detection of adenosine triphosphate based on ligation-triggered branched rolling circle amplification

Abstract

Herein, a fluorescence sensing strategy for ultrasensitive and highly selective detection of adenosine triphosphate (ATP) was presented by taking advantage of the exponential amplification efficiency of branched rolling circle amplification (BRCA) and the extreme specificity of T4 DNA ligase toward ATP. In this strategy, T4 DNA ligase is used to catalyze the cyclization of the template for the subsequent BRCA reaction. And the targeted ATP is the key component to activate the enzyme. The obtained products can then be probed with SYBR Green I (SG I), a kind of DNA duplex specific indicator, giving a dramatically increased fluorescence signal. The experimental results showed that the fluorescence enhancement is highly ATP concentration-dependent, thus making the label-free detection of ATP possible. By using the proposed method, sensitive ATP quantitation with a detection limit of 30 pM was achieved. Since the activity of T4 DNA ligase is highly specific ATP dependent, such a method successfully overcomes the drawback of poor selectivity faced by traditional aptamer-based ATP assays. Moreover, the proposed sensing platform was demonstrated to work well for real sample analysis, suggesting that it has promising potential in practical applications in clinical diagnosis and biochemical research.

Graphical abstract: A fluorescent biosensor for highly specific and ultrasensitive detection of adenosine triphosphate based on ligation-triggered branched rolling circle amplification

Supplementary files

Article information

Article type
Paper
Submitted
12 Jul 2019
Accepted
17 Aug 2019
First published
19 Aug 2019

Anal. Methods, 2019,11, 4629-4636

A fluorescent biosensor for highly specific and ultrasensitive detection of adenosine triphosphate based on ligation-triggered branched rolling circle amplification

H. Jiang, G. Han, Y. Xu, J. Li, X. Liu and D. Kong, Anal. Methods, 2019, 11, 4629 DOI: 10.1039/C9AY01482A

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