Issue 10, 2015

Enzymatically generated long polyT-templated copper nanoparticles for versatile biosensing assay of DNA-related enzyme activity

Abstract

DNA-templated copper nanoparticles (CuNPs) have emerged as promising fluorescent probes for biochemical assays with the advantage of a short, simple synthesis. Herein, a strategy for generating polyT ssDNA-templated CuNPs with terminal deoxynucleotidyl transferase (TdT) to detect DNA-related enzyme activities is proposed. A short oligonucleotide primer triggered polymerization in a template-free way through TdT to form a long polyT, which acted as a template in the formation of fluorescent CuNPs. In comparison with short T-rich CuNPs, the fluorescence intensity of TdT-generated polyT-CuNPs was greatly improved. The proposed method can be used to develop a label-free turn-on fluorescence assay to detect TdT activity with a detectable minimum concentration of 3.75 U mL−1. Furthermore, our strategy can be used to perform versatile turn-on DNA-related enzyme assays based on enzyme-activated TdT polymerization. Alkaline phosphatase (ALP) and BamHI were selected as model targets to test this strategy. As a result, simple, low-cost, selective detection of DNA-related enzymes was achieved with a competitive limit of detection of 0.052 × 10−3 U mL−1 for ALP and 0.005 U mL−1 for BamHI. This method showed a high signal-to-background ratio of 62.2, 44.6, and 31.4 corresponding to TdT, ALP, and BamHI. This TdT polymerization-based DNA-CuNP synthesis strategy can be used as a universal and efficient biosensing platform.

Graphical abstract: Enzymatically generated long polyT-templated copper nanoparticles for versatile biosensing assay of DNA-related enzyme activity

Supplementary files

Article information

Article type
Paper
Submitted
13 Feb 2015
Accepted
16 Apr 2015
First published
16 Apr 2015

Anal. Methods, 2015,7, 4355-4361

Enzymatically generated long polyT-templated copper nanoparticles for versatile biosensing assay of DNA-related enzyme activity

F. Peng, Z. Liu, W. Li, Y. Huang, Z. Nie and S. Yao, Anal. Methods, 2015, 7, 4355 DOI: 10.1039/C5AY00423C

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