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search.filters.applied.f.access: openAccess × Author: Popp, Jürgen × Author: Rösch, Petra ×

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Now showing 1 - 9 of 9
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    Modified 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, Thomas
    Raman 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
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    Use 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, Jürgen
    Deep-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.
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    Comparison of Different Label-Free Raman Spectroscopy Approaches for the Discrimination of Clinical MRSA and MSSA Isolates
    (Birmingham, Ala. : ASM, 2022) Pistiki, Aikaterini; Monecke, Stefan; Shen, Haodong; Ryabchykov, Oleg; Bocklitz, Thomas W.; Rösch, Petra; Ehricht, Ralf; Popp, Jürgen
    Methicillin-resistant Staphylococcus aureus (MRSA) is classified as one of the priority pathogens that threaten human health. Resistance detection with conventional microbiological methods takes several days, forcing physicians to administer empirical antimicrobial treatment that is not always appropriate. A need exists for a rapid, accurate, and cost-effective method that allows targeted antimicrobial therapy in limited time. In this pilot study, we investigate the efficacy of three different label-free Raman spectroscopic approaches to differentiate methicillin-resistant and -susceptible clinical isolates of S. aureus (MSSA). Single-cell analysis using 532 nm excitation was shown to be the most suitable approach since it captures information on the overall biochemical composition of the bacteria, predicting 87.5% of the strains correctly. UV resonance Raman microspectroscopy provided a balanced accuracy of 62.5% and was not sensitive enough in discriminating MRSA from MSSA. Excitation of 785 nm directly on the petri dish provided a balanced accuracy of 87.5%. However, the difference between the strains was derived from the dominant staphyloxanthin bands in the MRSA, a cell component not associated with the presence of methicillin resistance. This is the first step toward the development of label-free Raman spectroscopy for the discrimination of MRSA and MSSA using single-cell analysis with 532 nm excitation. IMPORTANCE Label-free Raman spectra capture the high chemical complexity of bacterial cells. Many different Raman approaches have been developed using different excitation wavelength and cell analysis methods. This study highlights the major importance of selecting the most suitable Raman approach, capable of providing spectral features that can be associated with the cell mechanism under investigation. It is shown that the approach of choice for differentiating MRSA from MSSA should be single-cell analysis with 532 nm excitation since it captures the difference in the overall biochemical composition. These results should be taken into consideration in future studies aiming for the development of label-free Raman spectroscopy as a clinical analytical tool for antimicrobial resistance determination.
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    Raman imaging of changes in the polysaccharides distribution in the cell wall during apple fruit development and senescence
    (Berlin ; Heidelberg : Springer, 2016) Szymańska-Chargot, Monika; Chylińska, Monika; Pieczywek, Piotr M.; Rösch, Petra; Schmitt, Michael; Popp, Jürgen; Zdunek, Artur
    Main conclusion Du ring on-tree ripening, the pectin distribution changed from polydispersed in cell wall to cumulated in cell wall corners. During apple storage, the pectin distribution returned to evenly dispersed along the cell wall. The plant cell wall influences the texture properties of fruit tissue for example apples become softer during ripening and postharvest storage. This softening process is believed to be mainly connected with changes in the cell wall composition due to polysaccharides undergoing an enzymatic degradation. These changes in polysaccharides are currently mainly investigated via chemical analysis or monoclonal labeling. Here, we propose the application of Raman microscopy for evaluating the changes in the polysaccharide distribution in the cell wall of apples during both ripening and postharvest storage. The apples were harvested 1 month and 2 weeks before optimal harvest date as well as at the optimal harvest date. The apples harvested at optimal harvest date were stored for 3 months. The Raman maps, as well as the chemical analysis were obtained for each harvest date and after 1, 2 and 3 months of storage, respectively. The analysis of the Raman maps showed that the pectins in the middle lamella and primary cell wall undergo a degradation. The changes in cellulose and hemicellulose were less pronounced. These findings were confirmed by the chemical analysis results. During development changes of pectins from a polydispersed form in the cell walls to a cumulated form in cell wall corners could be observed. In contrast after 3 months of apple storage we could observe an substantial pectin decrease. The obtained results demonstrate that Raman chemical imaging might be a very useful tool for a first identification of compositional changes in plant tissue during their development. The great advantage Raman microspectroscopy offers is the simultaneous localization and identification of polysaccharides within the cell wall and plant tissue.
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    Comparison 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, Jürgen
    It 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.
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    Isolation 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, Jürgen
    Raman 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.
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    Using Raman spectroscopy in infection research
    (Heidelberg : Spektrum, 2022) Cialla-May, Dana; Rösch, Petra; Popp, Jürgen
    Raman 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.
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    Raman Stable Isotope Probing of Bacteria in Visible and Deep UV-Ranges
    (Basel : MDPI, 2021) Azemtsop Matanfack, Georgette; Pistiki, Aikaterini; Rösch, Petra; Popp, Jürgen
    Raman stable isotope probing (Raman-SIP) is an excellent technique that can be used to access the overall metabolism of microorganisms. Recent studies have mainly used an excitation wavelength in the visible range to characterize isotopically labeled bacteria. In this work, we used UV resonance Raman spectroscopy (UVRR) to evaluate the spectral red-shifts caused by the uptake of isotopes (13C, 15N, 2H(D) and 18O) in E. coli cells. Moreover, we present a new approach based on the extraction of labeled DNA in combination with UVRR to identify metabolically active cells. The proof-of-principle study on E. coli revealed heterogeneities in the Raman features of both the bacterial cells and the extracted DNA after labeling with 13C, 15N, and D. The wavelength of choice for studying 18O- and deuterium-labeled cells is 532 nm is, while 13C-labeled cells can be investigated with visible and deep UV wavelengths. However, 15N-labeled cells are best studied at the excitation wavelength of 244 nm since nucleic acids are in resonance at this wavelength. These results highlight the potential of the presented approach to identify active bacterial cells. This work can serve as a basis for the development of new techniques for the rapid and efficient detection of active bacteria cells without the need for a cultivation step.
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    A 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, Heinrich
    Burkholderia (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