Multi-excitation Raman spectroscopy for label-free, strain-level characterisation of bacterial pathogens in artificial sputum media

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The current methods for diagnosis of acute and chronic infections are complex and skill-intensive. For complex clinical biofilm infections, it can take days from collecting and processing a patient’s sample to achieving a result. These aspects place a significant burden on healthcare providers, delay treatment, and can lead to adverse patient outcomes. We report the development and application of a novel multi-excitation Raman spectroscopy-based methodology for the label-free and non-invasive detection of microbial pathogens that can be used with unprocessed clinical samples directly and provide rapid data to inform diagnosis by a medical professional. The method relies on the differential excitation of non-resonant and resonant molecular components in bacterial cells to enhance the molecular finger-printing capability to obtain strain-level distinction in bacterial species. Here, we use this strategy to detect and characterize the respiratory pathogens Pseudomonas aeruginosa and Staphylococcus aureus as typical infectious agents associated with cystic fibrosis. Planktonic specimens were analyzed both in isolation and in artificial sputum media. The resonance Raman components, excited at different wavelengths, were characterized as carotenoids and porphyrins. By combining the more informative multi-excitation Raman spectra with multivariate analysis (support vector machine) the accuracy was found to be 99.75% for both species (across all strains), including 100% accuracy for drug-sensitive and drug-resistant S. aureus. The results demonstrate that our methodology based on multi-excitation Raman spectroscopy can underpin the development of a powerful platform for the rapid and reagentless detection of clinical pathogens to support diagnosis by a medical expert, in this case relevant to cystic fibrosis. Such a platform could provide translatable diagnostic solutions in a variety of disease areas and also be utilized for the rapid detection of anti-microbial resistance