and so this approach is considered only semi-supervised; i.e. the algorithm is not told the amount of isotope to which the bacteria had been cultivated in. To effect quantitative analysis partial least square regression (PLSR) was employed to predict the concentrations of 12C/13C and 14N/15N using the entire SERS spectral data from 500 to 3000 cm-1. PCA was also applied to the SERS image to visualize the contrast in the image according to the PC scores. The PCA loadings were subsequently inspected to identify the significant peaks contributing to such contrast. Results and discussion The SERS-based SIP technique is emerging as a powerful and valuable platform for identification, differentiation, discrimination and characterization of microorganisms associated with particular vital functions in complex microbial communities. Nevertheless, studies reported so far are generally focused on cultivating bacteria in fully labelled substrates or partially labelled 13C-glucose only to reveal useful information about metabolic capabilities of microorganisms., Thus, the aim of our study is to demonstrate the potential application of SERS-based SIP as a metabolic fingerprinting platform for quantitative detection and differentiation of cells grown on different ratios of isotopically enriched E. coli 13C-glucose and/or 15N-ammonium chloride substrates. The total concentration of these substrates was kept constant at 5 g/L glucose and 1 g/L nitrogen and it is perhaps worth noting that although in this experiment cells were exposed to different ratios of E. coli isotopically labelled carbon and nitrogen substrates, the growth profiles (Figure 2) of the cells were comparable and not affected by the varying concentrations of the heavy isotopes, which is also consistent with our previously reported findings. This means that all E. coli
and so this approach is considered only semi-supervised; i.e. the algorithm is not told the amount of isotope to which the bacteria had been cultivated in. To effect quantitative analysis partial least square regression (PLSR) was employed to predict the concentrations of 12C/13C and 14N/15N using the entire SERS spectral data from 500 to 3000 cm-1. PCA was also applied to the SERS image to visualize the contrast in the image according to the PC scores. The PCA loadings were subsequently inspected to identify the significant peaks contributing to such contrast. Results and discussion The SERS-based SIP technique is emerging as a powerful and valuable platform for identification, differentiation, discrimination and characterization of microorganisms associated with particular vital functions in complex microbial communities. Nevertheless, studies reported so far are generally focused on cultivating bacteria in fully labelled substrates or partially labelled 13C-glucose only to reveal useful information about metabolic capabilities of microorganisms., Thus, the aim of our study is to demonstrate the potential application of SERS-based SIP as a metabolic fingerprinting platform for quantitative detection and differentiation of E. coli cells grown on different ratios of isotopically enriched 13C-glucose and/or 15N-ammonium chloride substrates. The total concentration of these substrates was kept constant at 5 g/L glucose and 1 g/L nitrogen and it is perhaps worth noting that although in this experiment E. coli cells were exposed to different ratios of isotopically labelled carbon and nitrogen substrates, the growth profiles (Figure 2) of the cells were comparable and not affected by the varying concentrations of the heavy isotopes, which is also consistent with our previously reported findings. This means that all E. coli
SIP_SERS_Manuscript_File.docx