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Surface enhanced Raman spectroscopy (SERS) is a rapid and highly sensitive spectroscopic technique that has the potential to measure chemical changes in bacterial cell surface in response to environmental changes. The objective of this study was to determine whether SERS had sufficient resolution to differentiate closely related bacteria within a genus grown on solid and liquid medium, and a single Arthrobacter strain grown in multiple chromate concentrations. Fourteen closely related Arthrobacter strains, based on their 16S rRNA gene sequences, were used in this study. After performing principal component analysis in conjunction with Linear Discriminant Analysis, we used a novel, adapted cross-validation method, which more faithfully models the classification of spectra. All fourteen strains could be classified with up to 97% accuracy. The hierarchical trees comparing SERS spectra from the liquid and solid media datasets were different. Additionally, hierarchical trees created from the Raman data were different from those obtained using 16S rRNA gene sequences (a phylogenetic measure). A single bacterial strain grown on solid media culture with three different chromate levels also showed significant spectral distinction at discrete points identified by the new Elastic Net regularized regression method demonstrating the ability of SERS to detect environmentally induced changes in cell surface composition. This study demonstrates that SERS is effective in distinguishing between a large number of very closely related Arthrobacter strains and could be a valuable tool for rapid monitoring and characterization of phenotypic variations in a single population in response to environmental conditions.

Surface enhanced Raman spectroscopy (SERS) for the discrimination of Arthrobacter strains based on variations in cell surface composition