Urinary tract infection (UTI) is a very common infection. Up to every second woman will experience at least one UTI episode during her lifetime. The gold standard for identifying the infectious microorganisms is the urine culture. However, culture methods are time-consuming and need at least 24 h until the results are available. Here, we report about a culture independent identification procedure by using Raman microspectroscopy in combination with innovative chemometrics. We investigated, for the first time directly, urine samples by Raman microspectroscopy on a single-cell level. In a first step, a database of eleven important UTI bacterial species, which were grown in sterile filtered urine, was built up. A support vector machine (SVM) was used to generate a statistical model, which allows a classification of this data set with an accuracy of 92% on a species level. This model was afterward used to identify infected urine samples of ten patients directly without a preceding culture step. Thereby, we were able to determine the predominant bacterial species (seven Escherichia coli and three Enterococcus faecalis ) for all ten patient samples. These results demonstrate that Raman microspectroscopy in combination with support vector machines allow an identification of important UTI bacteria within two hours without the need of a culture step.

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Both biofilm formations as well as planktonic cells of water bacteria such as diverse species of the Legionella genus as well as Pseudomonas aeruginosa, Klebsiella pneumoniae, and Escherichia coli were examined in detail by Raman microspectroscopy. Production of various molecules involved in biofilm formation of tested species in nutrient-deficient media such as tap water was observed and was particularly evident in the biofilms formed by six Legionella species. Biofilms of selected species of the Legionella genus differ significantly from the planktonic cells of the same organisms in their lipid amount. Also, all Legionella species have formed biofilms that differ significantly from the biofilms of the other tested genera in the amount of lipids they produced. We believe that the significant increase in the synthesis of this molecular species may be associated with the ability of Legionella species to form biofilms. In addition, a combination of Raman microspectroscopy with chemometric approaches can distinguish between both planktonic form and biofilms of diverse bacteria and could be used to identify samples which were unknown to the identification model. Our results provide valuable data for the development of fast and reliable analytic methods based on Raman microspectroscopy, which can be applied to the analysis of tap water-adapted microorganisms without any cultivation step.

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