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- ItemIsolation of bacteria from artificial bronchoalveolar lavage fluid using density gradient centrifugation and their accessibility by Raman spectroscopy(Berlin [u.a.] : Springer, 2021) Wichmann, Christina; Rösch, Petra; Popp, JürgenRaman spectroscopy is an analytical method to identify medical samples of bacteria. Because Raman spectroscopy detects the biochemical properties of a cell, there are many factors that can influence and modify the Raman spectra of bacteria. One possible influence is a proper method for isolation of the bacteria. Medical samples in particular never occur in purified form, so a Raman-compatible isolation method is needed which does not affect the bacteria and thus the resulting spectra. In this study, we present a Raman-compatible method for isolation of bacteria from bronchoalveolar lavage (BAL) fluid using density gradient centrifugation. In addition to measuring the bacteria from a patient sample, the yield and the spectral influence of the isolation on the bacteria were investigated. Bacteria isolated from BAL fluid show additional peaks in comparison to pure culture bacteria, which can be attributed to components in the BAL sample. The isolation gradient itself has no effect on the spectra, and with a yield of 63% and 78%, the method is suitable for isolation of low concentrations of bacteria from a complex matrix. Graphical abstract.
- ItemUse of polymers as wavenumber calibration standards in deep-UVRR(Amsterdam [u.a.] : Elsevier Science, 2022) Pistiki, Aikaterini; Ryabchykov, Oleg; Bocklitz, Thomas W.; Rösch, Petra; Popp, JürgenDeep-UV resonance Raman spectroscopy (UVRR) allows the classification of bacterial species with high accuracy and is a promising tool to be developed for clinical application. For this attempt, the optimization of the wavenumber calibration is required to correct the overtime changes of the Raman setup. In the present study, different polymers were investigated as potential calibration agents. The ones with many sharp bands within the spectral range 400–1900 cm−1 were selected and used for wavenumber calibration of bacterial spectra. Classification models were built using a training cross-validation dataset that was then evaluated with an independent test dataset obtained after 4 months. Without calibration, the training cross-validation dataset provided an accuracy for differentiation above 99 % that dropped to 51.2 % after test evaluation. Applying the test evaluation with PET and Teflon calibration allowed correct assignment of all spectra of Gram-positive isolates. Calibration with PS and PEI leads to misclassifications that could be overcome with majority voting. Concerning the very closely related and similar in genome and cell biochemistry Enterobacteriaceae species, all spectra of the training cross-validation dataset were correctly classified but were misclassified in test evaluation. These results show the importance of selecting the most suitable calibration agent in the classification of bacterial species and help in the optimization of the deep-UVRR technique.
- ItemA Machine Learning-Based Raman Spectroscopic Assay for the Identification of Burkholderia mallei and Related Species(Basel : MDPI, 2019) Silge, Anja; Moawad, Amira A.; Bocklitz, Thomas; Fischer, Katja; Rösch, Petra; Roesler, Uwe; Elschner, Mandy C.; Popp, Jürgen; Neubauer, HeinrichBurkholderia (B.) mallei, the causative agent of glanders, and B. pseudomallei, the causative agent of melioidosis in humans and animals, are genetically closely related. The high infectious potential of both organisms, their serological cross-reactivity, and similar clinical symptoms in human and animals make the differentiation from each other and other Burkholderia species challenging. The increased resistance against many antibiotics implies the need for fast and robust identification methods. The use of Raman microspectroscopy in microbial diagnostic has the potential for rapid and reliable identification. Single bacterial cells are directly probed and a broad range of phenotypic information is recorded, which is subsequently analyzed by machine learning methods. Burkholderia were handled under biosafety level 1 (BSL 1) conditions after heat inactivation. The clusters of the spectral phenotypes and the diagnostic relevance of the Burkholderia spp. were considered for an advanced hierarchical machine learning approach. The strain panel for training involved 12 B. mallei, 13 B. pseudomallei and 11 other Burkholderia spp. type strains. The combination of top- and sub-level classifier identified the mallei-complex with high sensitivities (>95%). The reliable identification of unknown B. mallei and B. pseudomallei strains highlighted the robustness of the machine learning-based Raman spectroscopic assay. © 2019 by the authors
- ItemModified PCA and PLS: Towards a better classification in Raman spectroscopy–based biological applications(New York, NY : Wiley Interscience, 2020) Guo, Shuxia; Rösch, Petra; Popp, Jürgen; Bocklitz, ThomasRaman spectra of biological samples often exhibit variations originating from changes of spectrometers, measurement conditions, and cultivation conditions. Such unwanted variations make a classification extremely challenging, especially if they are more significant compared with the differences between groups to be separated. A classifier is prone to such unwanted variations (ie, intragroup variations) and can fail to learn the patterns that can help separate different groups (ie, intergroup differences). This often leads to a poor generalization performance and a degraded transferability of the trained model. A natural solution is to separate the intragroup variations from the intergroup differences and build the classifier based on merely the latter information, for example, by a well-designed feature extraction. This forms the idea of this contribution. Herein, we modified two commonly applied feature extraction approaches, principal component analysis (PCA) and partial least squares (PLS), in order to extract merely the features representing the intergroup differences. Both of the methods were verified with two Raman spectral datasets measured from bacterial cultures and colon tissues of mice, respectively. In comparison to ordinary PCA and PLS, the modified PCA was able to improve the prediction on the testing data that bears significant difference to the training data, while the modified PLS could help avoid overfitting and lead to a more stable classification. © 2019 The Authors. Journal of Chemometrics published by John Wiley & Sons Ltd
- ItemComparison of bacteria in different metabolic states by micro-Raman spectroscopy(New York, NY [u.a.] : Elsevier, 2022) Shen, Haodong; Rösch, Petra; Thieme, Lara; Pletz, Mathias W.; Popp, JürgenIt was shown that several metabolic states of bacteria with various characteristics such as chemical composition participate in the formation of biofilms. To study the connections and differences among different bacterial metabolic states, five species of bacteria in exponential phase, stationary phase and biofilm have been compared and investigated by micro-Raman spectroscopy. The spectral differences between different metabolic states showed that the chemical composition varied among those metabolic states. Moreover, as can be shown by the spectral differences and principal components (PCs), different species and strains of bacteria behave differently. Furthermore, a principal component analysis (PCA) combined with support vector machines (SVM) was applied to distinguish species of bacteria within the same metabolic states. Our study provides valuable data for the comparison of bacteria between different metabolic states utilizing micro-Raman spectroscopy in combination with chemometrics models.
- ItemUsing Raman spectroscopy in infection research(Heidelberg : Spektrum, 2022) Cialla-May, Dana; Rösch, Petra; Popp, JürgenRaman spectroscopy allows to analyze bacteria and other microorganisms label and destruction free. With different Raman techniques either colonies on agar plates or small structures like single bacterial cells can be analyzed allowing for their identification as well as enabling 2D and 3D information of intracellular bacteria or biofilms. Using surface enhanced Raman spectroscopy (SERS) allows detecting and identifying viruses as well as antibiotics relevant in the treatment of infections.