Raman spectroscopy for the differentiation of Enterobacteriaceae: a comparison of two methods.

Enterobacteriaceae are the leading cause of urinary tract infections, and include pathogens such as E. coli, K. pneumoniae and P. mirabilis. Due to their similarity, the correct identification of these pathogens is difficult and time-consuming. Raman spectroscopy has been demonstrated extensively as a tool for rapid microbiological differentiation. However, for pathogenic Enterobacteriaceae the application of Raman spectroscopy has been particularly challenging. In this study, two promising methods for Raman-based microbiological diagnostics were compared for differentiating Enterobacteriaceae. Spectra were collected from single-cells with Raman microspectroscopy and from colonies on agar with an NIR Raman fiber-probe. A comprehensive dataset of spectra from 8 different, clinically relevant, genera was collected. Visually, the spectra obtained from both methods presented little difference between the genera. For classification, single cell analysis yielded limited results, while the fiber-probe spectra enabled perfect classification of all 16 isolates. Moreover, the model was validated on new replicates and 15/16 strains were correctly identified (94% overall accuracy). This is the first study to focus on the closely related Enterobacteriaceae, who have previously been avoided or differentiated poorly. It shows how, with the correct spectroscopic setup, even challenging questions in clinical microbiology can be resolved with Raman spectroscopy, highlighting the method's potential for improving patient care.

Label-free differentiation of clinical E. coli and Klebsiella isolates with Raman spectroscopy.

Raman spectroscopy is a promising spectroscopic technique for microbiological diagnostics. In routine diagnostic, the differentiation of pathogens of the Enterobacteriaceae family remain challenging. In this study, Raman spectroscopy was applied for the differentiation of 24 clinical E. coli, Klebsiella pneumoniae and Klebsiella oxytoca isolates. Spectra were collected with two spectroscopic approaches: UV-Resonance Raman spectroscopy (UVRR) and single-cell Raman microspectroscopy with 532 nm excitation. A description of the different biochemical profiles provided by the different excitation wavelengths was performed followed by machine-learning models for the classification at the genus and species levels. UVRR was shown to outperform 532 nm excitation, enabling correct classification at the genus level of 23/24 isolates. Furthermore, for the first time, Klebsiella species were correctly classified at the species level with 92% accuracy, classifying all three K. oxytoca isolates correctly. These findings should guide future applicative studies, increasing the scope of Raman spectroscopy's suitability for clinical applications.

Detection of multi-resistant clinical strains of E. coli with Raman spectroscopy.

In recent years, we have seen a steady rise in the prevalence of antibiotic-resistant bacteria. This creates many challenges in treating patients who carry these infections, as well as stopping and preventing outbreaks. Identifying these resistant bacteria is critical for treatment decisions and epidemiological studies. However, current methods for identification of resistance either require long cultivation steps or expensive reagents. Raman spectroscopy has been shown in the past to enable the rapid identification of bacterial strains from single cells and cultures. In this study, Raman spectroscopy was applied for the differentiation of resistant and sensitive strains of Escherichia coli. Our focus was on clinical multi-resistant (extended-spectrum ß-lactam and carbapenem-resistant) bacteria from hospital patients. The spectra were collected using both UV resonance Raman spectroscopy in bulk and single-cell Raman microspectroscopy, without exposure to antibiotics. We found resistant strains have a higher nucleic acid/protein ratio, and used the spectra to train a machine learning model that differentiates resistant and sensitive strains. In addition, we applied a majority of voting system to both improve the accuracy of our models and make them more applicable for a clinical setting. This method could allow rapid and accurate identification of antibiotic resistant bacteria, and thus improve public health.

Quantification of bacteria in water using PLS analysis of emission spectra of fluorescence and excitation-emission matrices.

Bacterial contamination of drinking water is a considerable concern for public health. Tryptophan-like fluorescence (TLF) has been widely suggested to enable fast and inexpensive monitoring and quantification of bacterial contamination of water. Typically, TLF is determined at a certain excitation (ex)/emission (em) wavelengths pair. The aim of this study was to assess fluorescence spectroscopy supported with partial least squares (PLS) algorithms as a tool for a rapid evaluation of the microbial quality of water, by comparing the use of a single ex/em wavelengths pair, of the spectrum of emission obtained at a single excitation wavelength to that of whole excitation-emission matrices (EEMs). For that, laboratory-grown Escherichia coli, Bacillus subtilis and Pseudomonas aeruginosa were studied as the model systems, as well as 90 groundwater samples from 6 different wells in Israel. The groundwater samples were characterized for fluorescence emission, coliforms, fecal coliforms, fecal streptococci and heterotrophic plate counts. The PLS analysis of emission spectra and, especially, of EEMs was capable of meaningfully reducing the detection limit of microorganisms in model systems, as compared with the single ex/em wavelengths pair-based determination commonly used, reaching a detection threshold as low as 10 CFU/ml. Use of PLS-analyzed EEMs becomes beneficial also in terms of correlation and similarity between the actual and predicted bacterial concentrations. Similarly, improved detection of bacteria was also achieved in groundwater samples. Furthermore, at a level of >104 CFU/ml, use of EEMs coupled with PLS enabled discrimination between E. coli, B. subtilis and P. aeruginosa.