Identifying Bacterial Species Detecting Antibiotic Resistance and Susceptibilities in Clinical Samples Using Raman Spectroscopy

Abstract

Over the past few decades, Raman spectroscopy has found enormous applications in biology and medicine. Raman micro-spectroscopy is highly sensitive to the structure and composition of molecules and their surrounding moieties present in a sample. This technique is label-free and non-invasive and offers a significant advantage over other methods involving minimal sample preparation and water interference. Raman spectroscopy combined with various chemometrics methods can provide molecular insights into several biochemical processes. This thesis discusses the application of Raman spectroscopy in identifying bacterial species, determining antibiotic susceptibility in clinical samples, and detecting phenotypic antibiotic resistance. The thesis begins with a brief introduction to the principle and instrumentation of Raman spectroscopy (Chapter 1). It provides a brief literature review of the applications of Raman spectroscopy in studying biological molecules. Chapter 2 of the thesis discusses the various Raman spectral data analysis approaches. Herbicides are ubiquitous in modern society and often co-occur with antibiotics in runways, sewage, and water bodies (Chapter 3). The herbicides used in this study are 2,4 D and Glyphosate, as these are the most widely used herbicides in agriculture worldwide. The study uses a group of isogenic mutants of E. coli, and#8710;lon, and and#8710;acrB, displaying different antibiotic susceptibilities. Combined with supervised machine learning methods, Raman spectroscopy could efficiently track the emergence of antibiotic resistance and confirm that the induction of antibiotic resistance is AcrB-dependent. Identifying bacterial species from clinical samples is often challenging. The current gold standard for identifying bacteria is time-intensive, laborious, and may provide incorrect results if the sample matrix is not entirely removed (Chapter 4). This part of the thesis demonstrates Raman spectroscopy-based identification of pathogens from clinical samples using deep learning at the single-cell level