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.

The identification of pathogens in ascitic fluid is standardly performed by ascitic fluid culture, but this standard procedure often needs several days. Additionally, more than half of the ascitic fluid cultures are negative in case of suspected spontaneous bacterial peritonitis (SBP). It is therefore important to identify and characterize the causing pathogens since not all of them are covered by the empirical antimicrobial therapy. The aim of this study is to show that pathogen identification in ascitic fluid is possible by means of Raman microspectroscopy and chemometrical evaluation with the advantage of strongly increased speed. Therefore, a Raman database containing more than 10000 single-cell Raman spectra of 34 bacterial strains out of 13 different species was built up. The performance of the used statistical model was validated with independent bacterial strains, which were grown in ascitic fluid.

In this study, Raman microspectroscopy has been utilized to identify mycobacteria to the species level. Because of the slow growth of mycobacteria, the per se cultivation-independent Raman microspectroscopy emerges as a perfect tool for a rapid on-the-spot mycobacterial diagnostic test. Special focus was laid upon the identification of Mycobacterium tuberculosis complex (MTC) strains, as the main causative agent of pulmonary tuberculosis worldwide, and the differentiation between pathogenic and commensal nontuberculous mycobacteria (NTM). Overall the proposed model considers 26 different mycobacteria species as well as antibiotic susceptible and resistant strains. More than 8800 Raman spectra of single bacterial cells constituted a spectral library, which was the foundation for a two-level classification system including three support vector machines. Our model allowed the discrimination of MTC samples in an independent validation dataset with an accuracy of 94% and could serve as a basis to further improve Raman microscopy as a first-line diagnostic point-of-care tool for the confirmation of tuberculosis disease.

We developed a Raman-compatible chip for isolating microorganisms from complex media. The isolation of bacteria is achieved by using antibodies as capture molecules. Due to the very specific interaction with the targets, this approach is promising for isolation of bacteria even from complex matrices such as body fluids. Our choice of capture molecules also enabled the investigation of samples containing yet unidentified bacteria, as the antibodies can capture a large variety of bacteria based on their analogue cell wall surface structures. The capability of our system is demonstrated for a broad range of different Gram-positive and Gram-negative germs. Subsequent identification is done by recording Raman spectra of the bacteria. Further, it is shown that classification with chemometric methods is possible.