Surface enhanced Raman scattering spectroscopy for detection and identification of microbial pathogens isolated from human serum

Abstract

Clinical bacterial diagnostic techniques take between 24 and 48 h and require plating, growth, and examination of colony morphology or color for identification. Biosensors based on Surface Enhanced Raman Scattering (SERS) spectroscopy hold great promise as a platform for rapid and sensitive detection of bacterial pathogens by decreasing time of diagnosis and preventing infection-related morbidity and mortality. The objective of this study was to characterize and evaluate a handheld SERS-based diagnostic system for the detection and identification of bacteria in pooled human sera. Species of Acenitobacter baumannii, Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus, and Pseudomonas aeruginosa were individually inoculated into pooled human serum samples. Samples were processed by lysis filtration to separate and isolate bacteria. Processed bacterial samples were incubated onto silver nanorod substrates at 60 °C for 3 h. Spectra of bacteria recovered from serum were compared to spectra of pure culture bacteria. Principal Component Analysis and Partial Least Squares Differential Analysis were performed to determine bacterial “molecular fingerprint” (uniqueness and commonalities of measured spectra). Successful detection, identification, and classification of bacteria from human serum using a hand-held Raman spectrometer were demonstrated. Pure culture bacteria were readily identifiable and distinguishable by their SERS-based molecular fingerprints at the species level. Hydrophilic bacteria were readily detected and identified from serum samples without changes occurring to their spectra due to sample processing. Shifts in relative peak intensities of SERS spectra were observed primarily for hydrophobic bacteria after recovery from serum. Bacteria sensitive to lysis filtration require additional reference criteria for SERS identification. Keywords: SERS, Bacteria, Serum, Diagnostic, Infection, Raman