In military medicine, one of the challenges in dealing with large combat-related injuries is the prevalence of bacterial infection, including multidrug resistant organisms. This can prolong the wound healing process and lead to wound dehiscence. Current methods of identifying bacterial infection rely on culturing microbes from patient material and performing biochemical tests, which together can take 2-3 days to complete. Surface Enhanced Raman Spectroscopy (SERS) is a powerful vibrational spectroscopy technique that allows for highly sensitive structural detection of analytes adsorbed onto specially prepared metal surfaces. In the past, we have been able to discriminate between bacterial isolates grown on solid culture media using standard Raman spectroscopic methods. Here, SERS is utilized to assess the presence of bacteria in wound effluent samples taken directly from patients. To our knowledge, this is the first attempt for the application of SERS directly to wound effluent. The utilization of SERS as a point-of-care diagnostic tool would enable physicians to determine course of treatment and drug administration in a matter of hours.
Recent studies have demonstrated the potential advantages of the use of Raman spectroscopy in the biomedical field due to its rapidity and noninvasive nature. In this study, Raman spectroscopy is applied as a method for differentiating between bacteria isolates for Gram status and Genusspecies. We created models for identifying 28 bacterial isolates using spectra collected with a 785 nm laser excitation Raman spectroscopic system. In order to investigate the groupings of these samples, partial least squares discriminant analysis (PLSDA) and hierarchical cluster analysis (HCA) was implemented. In addition, cluster analyses of the isolates were performed using various data types consisting of, biochemical tests, gene sequence alignment, high resolution melt (HRM) analysis and antimicrobial susceptibility tests of minimum inhibitory concentration (MIC) and degree of antimicrobial resistance (SIR). In order to evaluate the ability of these models to correctly classify bacterial isolates using solely Raman spectroscopic data, a set of 14 validation samples were tested using the PLSDA models and consequently the HCA models. External cluster evaluation criteria of purity and Rand index were calculated at different taxonomic levels to compare the performance of clustering using Raman spectra as well as the other datasets. Results showed that Raman spectra performed comparably, and in some cases better than, the other data types with Rand index and purity values up to 0.933 and 0.947, respectively. This study clearly demonstrates that the discrimination of bacterial species using Raman spectroscopic data and hierarchical cluster analysis is possible and has the potential to be a powerful point-of-care tool in clinical settings.