Transgenic Plant Detection Using an AuNPs Based SPR Biosensor
Related Experiment Video
Updated: Jan 6, 2026

Wide-Field, Real-Time Imaging of Local and Systemic Wound Signals in Arabidopsis
Published on: June 4, 2021
Bartosz F Grześkowiak1, Karol Tuśnio2,3, Anna Woźniak4
1The NanoBioMedical Centre, Adam Mickiewicz University, Wszechnicy Piastowskiej 3, 61-614 Poznan, Poland. bartoszg@amu.edu.pl.
Abstract:
The intensive development and commercialization of genetically modified plants observed over the last decade has led to the development of transgenic detection methods that are rapid and sensitive. Among the strategies used for the detection/monitoring of genetically modified organisms (GMOs), surface plasmon resonance (SPR) meets the necessary criteria. This optical technique measures the changes in the refractive index in the vicinity of thin metal layers (i.e., gold) in response to biomolecular interactions occurring at a flat metal‒solution interface. Additionally, it allows the application of functionalized gold nanoparticles (AuNPs) in SPR research to enhance the signal intensity. In the present study, an SPR method, enhanced by the application of AuNPs, was developed to detect transgenic tobacco plants carrying a Streptococcus mutans antigen. The basis for the detection of the target DNA was the hybridization between the genomic DNA isolated from the leaves, stems, and roots of the transgenic tobacco and the biotinylated oligonucleotide probes immobilized onto a streptavidin (SA) sensor chip. SA-functionalized AuNPs coated with a second type of biotinylated probe were applied to increase the sensitivity of the detection method. Analysis of the results indicated that the constructed SPR-based sensor chip can potentially recognize complementary standard fragments (nonamplified genomic DNA) at concentrations as low as 1 pM. Thus, nonamplified transgenic DNA was detected using a label-free and real-time AuNPs-enhanced SPR biosensing method. This unique approach could be used to detect GMOs with high efficiency, even at a low detection limit, high repeatability, and with less time and a lower cost needed for each analysis.

