Microfluidics and nanoparticles based amperometric biosensor for the detection of cyanobacteria (Planktothrix agardhii NIVA-CYA 116) DNA

OLCER Z., ESEN E., ERSOY A., BUDAK S., Kaya D., GOK M. Y., ...More

BIOSENSORS & BIOELECTRONICS, vol.70, pp.426-432, 2015 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 70
  • Publication Date: 2015
  • Doi Number: 10.1016/j.bios.2015.03.052
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.426-432
  • Istanbul University Affiliated: Yes


Some of the cyanobacteria produce protease inhibitor oligopeptides such as cyanopeptolins and cause drinking water contamination; hence, their detection has great importance to monitor the well-being of water sources that is used for human consumption. In the current study, a fast and sensitive nucleic acid biosensor assay has been described where cyanopeptolin coding region of one of the cyanobacteria (Planktothrix agardhii NIVA-CYA 116) genome has been used as target for monitoring of the fresh water resources. A biochip that has two sets of Au electrode arrays, each consist of shared reference/counter electrodes and 3 working electrodes has been used for the assay. The biochip has been integrated to a microfluidics system and all steps of the assay have been performed during the reagent flow to achieve fast and sensitive DNA detection. On-line hybridization of the target on to the capture probe immobilized surface resulted in a very short assay duration with respect to the conventional static assays. The binding of the avidin and enzyme modified Au nanoparticles to the biotinylated detection probe and the subsequent injection of the substrate enabled a real-time amperometric measurement with a detection limit of 6 x 10(-12) M target DNA (calibration curve r(2)=0.98). The developed assay enables fast and sensitive detection of cyanopeptolin producing cyanobacteria from freshwater samples and hence shows a promising technology for toxic microorganism detection from environmental samples. (C) 2015 Elsevier B.V. All rights reserved.