|dc.description.abstract||Over the last few decades, the development of several new techniques as well as sophisticated instruments have contributed to a better understanding of biological systems. Among these, Raman spectroscopy has emerged as an indispensable tool. Traditionally a chemist’s tool, Raman spectroscopy has recently found numerous applications in the field of biology and medicine. Of particular advantage is the fact that Raman spectroscopy is non-invasive, non-destructive, label-free, requires minimal sample volume and offers multi-component analysis in a single scan. Biological samples are complex and are made up of several biomolecules like proteins, lipids, carbohydrates and
nucleic acids. These molecules have unique structures and, therefore, yield unique spectral fingerprints. The structural changes in the biomolecules can be tracked during disease or any other biological process. In short, a Raman spectrum reflects a biological entity’s underlying chemistry and any perturbation in the cellular chemistry can be tracked efficiently and rapidly. However, interpreting Raman spectra obtained from complex biological systems like cells, tissues and body fluids can be quite challenging. Therefore, multivariate statistical algorithms like principal component analysis, discriminant analysis etc have to be employed to enable the extraction of useful information.
In the present thesis, multiple applications of Raman spectroscopy are demonstrated: identification of two closely related bacterial strains, tracking the emergence of antimicrobial resistance in bacteria and delineating biomarkers of sepsis in mice model systems as well as human patient samples.||en_US