Study of factors influencing bacterial identification by Raman spectroscopy

The formation of microbial biofilms causes serious problems in natural environments, industrial systems and in medical situations. Therefore, for timely, appropriate treatment and control measures, there is a need to develop an analytical technique that can facilitate rapid, in situ microbiological identification directly from biofilms. Raman spectroscopy has been promoted as a non-invasive optical technique for differential bacterial identification at species and strain level as well as the study of bacterial growth phases. To the best of our knowledge, the bacterial cells in these Raman spectroscopic studies were mostly from planktonic suspension, cells recovered from biofilm, cells within pseudo-mixed biofilms and cells from singlespecies biofilms under controlled laboratory conditions. This thesis explores the implications for extending Raman spectroscopy identification techniques to more practical real-world settings. In particular, real-world biofilm samples will include bacteria from different points in their life cycle, responding to the presence of other organisms in the consortium and exposed to different physicochemical and environmental conditions. In view of the fact that individual cellular differences in macromolecular composition contribute metabolic heterogeneity within a bacterial population, it is necessary to attain specific bacterial identification and classification from different time points of the growth cycle as well as throughout biofilm development. In this study, Raman spectroscopy in combination with chemometric analysis was applied for the identification of (and discrimination between) diverse bacterial species at various growth time points. The results showed that bacterial cells from a particular growth time point (as well as from random growth phase) can be well-discriminated among the four species using principal component analysis (PCA). The results also showed that the bacteria from different growth phases can be classified with the help of a prediction model, based on principal component and linear discriminant analysis (PC-LDA). These findings demonstrated that Raman spectroscopy with the application of PC-LDA model rooted in chemotaxonomic analysis may provide valuable applications in rapid sensing of microbial cells in environmental and clinical studies. However, it is not yet representative of real-world situation for biofilm study. Mya Myintzu Hlaing PhD Thesis/ii Therefore, Raman spectroscopy experiments were performed on intact bacterial colony and biofilm for moving towards realistic settings by examining the cellular changes of surface-attached bacterial cells. The results showed that the content of carbohydrates, proteins and nucleic acids in the biofilm matrix increased significantly along with the biofilm growth of the four bacterial species. The findings strongly suggested the Raman spectroscopy has significant potential for studying chemical variations during biofilm formation. However, poor classification results were obtained for surface-attached biofilm cells using PC-LDA planktonic model. It is generally believed that cells within biofilm experience a unique mode of growth and behave differently from their planktonic counterparts. Thus, it was not surprising that the planktonic cell model was ineffective in classifying results of cells from biofilm, highlighting that a new model was required for surface-attached cells. A PCLDA biofilm model was calibrated using single spectra from biofilm cells of each species and validated using pure E. coli biofilms grown on quartz substrate, achieving a high accuracy in potential classification. The application of this biofilm model provided 75% sensitivity in detecting the presence of E. coli and V. vulnificus species in dual-species biofilms. This prediction accuracy is useful not only for understanding species interactions but also for analysing biofilm formation with species of interest in a more complex community. Finally, the effects of different surface chemistries on Raman identifiable macromolecular changes in surface-attached bacterial cells were examined. The interaction of E. coli cells with plasma polymer thin films containing hydrocarbon, amine and carboxyl groups provided differences in cell attachment phenotypes, cell viability and subsequent biofilm formations. The identification of surface-attached cells from polymer surfaces was challenged by weak spectral features resulted from polymer background involvement. Correct identification outcomes were achieved when the surface attached cells were removed from the polymer coated substrate and smeared onto clear CaF2 slides. While these results were encouraging, the outcome may be dependent on intra-species variability versus inter-species variability and the identification may become more difficult as more species are added to the identification database. Nevertheless, this study opens the pathway to extend further