Issue 11, 2022

An entropy-driven signal-off DNA circuit for label-free, visual detection of small molecules with enhanced accuracy

Abstract

An entropy-driven DNA circuit offers an efficient means of sensitive analyte detection with signal amplification. In this article, we rationally engineered an aptamer-based entropy-driven signal-off DNA circuit for colorimetric detection of small molecules. The proposed signal-off DNA circuit is activated by target small molecule binding to drive the collapse of G-quadruplex DNAzyme, accompanied by the colour change of the detection solution from dark blue to light blue. Entropy-driven recycling hybridization significantly magnified the input signal of the target small molecule. Such an assay enables naked-eye detection of adenosine triphosphate and oxytetracycline at concentrations as low as 0.5 μM and 1 μM respectively. Moreover, when compared with the signal-on DNA circuit, the entropy-driven signal-off DNA circuit for colorimetric detection has two advantages. Firstly, unlike in the signal-on DNA circuit, the unavoidable formation of waste complexes in the absence of a target in the signal-off DNA circuit has no influence on target detection performance as its background signal is only determined by the substrate complex. Secondly, the signal-on DNA circuit cannot distinguish false-positive signals generated by invasive catalysts (e.g., HRP, serum, Fe3O4), while the signal-off DNA circuit can distinguish those signals as undesired signals. Overall, the signal-off DNA circuit affords a novel strategy for sensitive and accurate detection of small molecules.

Graphical abstract: An entropy-driven signal-off DNA circuit for label-free, visual detection of small molecules with enhanced accuracy

Supplementary files

Article information

Article type
Paper
Submitted
16 Nov 2021
Accepted
10 Feb 2022
First published
11 Feb 2022

Anal. Methods, 2022,14, 1140-1147

An entropy-driven signal-off DNA circuit for label-free, visual detection of small molecules with enhanced accuracy

T. Li, Y. Wang, Y. Zhang, G. Zhou and L. Li, Anal. Methods, 2022, 14, 1140 DOI: 10.1039/D1AY01939B

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