• 文献标题:   A homogeneous assay for highly sensitive detection of CaMV35S promoter in transgenic soybean by forster resonance energy transfer between nitrogen-doped graphene quantum dots and Ag nanoparticles
  • 文献类型:   Article
  • 作  者:   LI YQ, SUN L, QIAN J, WANG CK, LIU Q, HAN E, HAO N, ZHANG LP, CAI JR, WANG K
  • 作者关键词:   nitrogendoped graphene quantum dot, silver nanoparticle, fluorescence resonance energy transfer, homogeneous assay, transgenic soybean
  • 出版物名称:   ANALYTICA CHIMICA ACTA
  • ISSN:   0003-2670 EI 1873-4324
  • 通讯作者地址:   Jiangsu Univ
  • 被引频次:   11
  • DOI:   10.1016/j.aca.2016.10.031
  • 出版年:   2016

▎ 摘  要

In this work, a novel homogeneous assay for DNA quantitative analysis based on forster resonance energy transfer (FRET) was developed for cauliflwer mosaic virus 35s (CaMV35S) promoter of transgenic soybean detection. The homogenous FRET of fluorescence signal was fabricated by DNA hybridization with probe modified nitrogen-doped graphene quantum dots (NGQDs) and silver nanoparticles (AgNPs), which acted the donor-acceptor pairs for the first time. The highly efficient FRET and unique properties of the NGQDs made the proposed FRET system as a functionalized detection platform for labelling of DNA. Upon the recognition of specific target DNA (tDNA), the FRET between NGQDs and AgNPs was triggered to produce fluorescence quenching, which could be used for tDNA detection. The fabricated homogeneous FRET assay displayed a wide linear range of 0.1-500.0 nM and a low limit of detection 0.03 nM for the detection of CaMV35S (S/N = 3). This proposed biosensor revealed high specificity to detect tDNA, with acceptable intra-assay precision and excellent stability. This method was successfully applied to identify the real sample of 0.5% containing transgenic soybean, which achieved the most of national law regulations. This assay was further validated by polymerase chain reaction as the genetically modified organisms, suggesting that the proposed FRET system is a feasible tool for the further daily genetically modified organism detection. (C) 2016 Elsevier B.V. All rights reserved.