|Document Type:||Journal Article|
|Title:||In-situ Strain-level Detection and Identification of Vibrio parahaemolyticus Using Surface-enhanced Raman Spectroscopy|
|Author:||Jiajie Xu, Jeffrey W. Turner, Matthew Idso, Stanley V. Biryukov, Laurel Rognstad, Heng Gong, Vera L. Trainer, M. L. Wells, M. S. Strom, Qiuming Yu|
The surface of a Gram-negative bacterium is a complex matrix of biological structures including flagella, fimbriae, outer membrane proteins, and lipo- and capsular polysaccharides. Using the human pathogen Vibrio parahaemolyticus as a proof of concept species, we show that the biochemical information contained in these structures can be utilized for the rapid detection and identification of pathogenic strains using surface-enhanced Raman spectroscopy (SERS). For this purpose, we analyzed seven phylogenetically distinct strains originating from clinical and environmental sources in the Pacific Northwest (PNW) region of the United States (US). The unique quasi-3D (Q3D) plasmonic nanostructure arrays, optimized using finite-difference time-domain (FDTD) calculations, were used as SERS-active substrates for sensitive and reproducible detection of bacteria. SERS barcodes were generated based on SERS spectra and the capabilities to detect blind samples and mixtures were demonstrated and the limit of detection was also evaluated. The sensing and detection methods developed in this work could have broad applications in the areas of environmental monitoring, biomedical diagnostics, and homeland security.
Raman spectrocopy was used to differentiate Vibrio parahaemolyticus strains. Results showed that Raman spectral can differentiate Vibrio parahaemolyticus at a strain-specific level.
|Theme:||Sustaining Marine Ecosystem and Human Health|
Develop methods, technologies, and data integration tools to predict ocean-related public health risks into health early warning and ocean observing systems
Develop methods, identify data, and generate tools to describe communities and their connection to ocean environments to improve early warnings and predict impacts of hazardous events.