Quantitative detection of isotopically enriched E. coli cells by SERS

It is clear that investigating how bacterial cells work by analysing their functional roles in microbial communities is very important in environmental, clinical and industrial microbiology. The benefits of linking genes to their respective functions include reliable identification of causative agents of various diseases, which would permit appropriate and timely treatment in healthcare systems. In industrial and municipal wastewater treatment and management, such knowledge may allow for the manipulation of microbial communities, such as bioaugmentation, in order to improve the efficiency and effectiveness of bioremediation process. Stable isotope probing coupled with identification techniques has emerged to be a potentially reliable tool for discrimination, identification and characterization of bacteria at community and single cell level; knowledge which can be utilized to link microbially mediated bioprocesses to phylogeny. The development of surface-enhanced Raman scattering (SERS) technique offers an exciting alternative to the Raman and Fourier-transform infrared spectroscopic techniques in understanding metabolic processes of microorganisms in situ. SERS employing Ag and Au nanoparticles can significantly enhance the Raman signal making it an exciting candidate for the analysis of cellular components of microorganisms. In this study, Escherichia coli cells were cultivated in minimal medium containing different ratios of 12C/13C glucose and 14N/15N ammonium chloride as the only carbon and nitrogen sources respectively, with the overall final concentrations of these substrates being constant. After growth E. coli cells were analyzed with SERS employing in situ synthesis of Ag nanoparticles. This novel investigation of SERS spectral data with multivariate chemometrics demonstrated clear clusters which could be correlated to SERS spectral shifts of biomolecules from cells grown and hence labeled with 13C and 15N atoms. These shifts reflect the isotopic content of the bacteria and quantification of the isotope levels could be established using chemometrics based on partial least squares regression.