Development of nano-based biosensors for the detection and differentiation of bacterial pathogens using surface enhanced Raman spectroscopy
Rivera, Omar Eduardo
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Rapid and sensitive detection of pathogens is an essential strategy for intervention of a possible disease outbreak. The unique ability of SERS to provide analyte specific response has allowed for the development of this method for rapid biomedical diagnosis needed for routine analyses of different species and strains of pathogens. The specific agents we examined in the first part of this project are lipophilic extracts containing mycolic acids isolated from Tuberculosis (MTB) and non-tuberculosis (NTM) strains using chromatography, mass spectrometry (MS), nuclear magnetic resonance (NMR), and Raman spectroscopy. Surface-enhanced Raman (SERS) spectra were obtained from the mycolic acids extracted from the bacterial cell envelopes of the MTB or NTM mycobacterial species. The Raman spectral profiles were used to develop a classification method based on chemometrics for identification of the mycobacterial species. Multivariate statistical analysis methods, including principal component analysis (PCA), hierarchical cluster analysis (HCA),and partial least squares discriminant analysis (PLS-DA) of the SERS spectra enabled differentiation of NTM mycobacteria from one another with 100% accuracy. These methods are also sensitive enough to differentiate clinically-isolated MTB strains that differed only by the presence or absence of a single extracytoplasmic sigma factor. We examined as well Mycoplasma pneumoniae which is a major cause of respiratory disease in humans and accounts for as much as 20% of all community-acquired pneumonia. There is a critical need to develop a new platform for mycoplasma detection that has high sensitivity, specificity, and expediency. Here we report three different layer-by-layer (LBL) encapsulation procedures of M. pneumoniae and mycoplasma commensals cells with Ag nanoparticles in polyelectrolyte matrixes. We evaluated nanoparticle encapsulated mycoplasma cells as a platform for the differentiation of M. pneumoniae mycoplasma commensal strains using surface enhanced Raman scattering (SERS) combined with multivariate statistical analysis. Since the LBL-SERS has inherent biochemical specificity, we analyzed a panel of 11 human commensal from a pathogen strain of mycoplasma to demonstrate that this platform could distinguish M129 from its clinically relevant phylogenetic relatives.. The feature selection information was used to perform PLS-DA and SVM-DA models. These results suggest that SERS along with multivariate statistics can be used as an accurate and sensitive method for species and strain discrimination in mycobacteria and mycoplasma.