Cultivation-Free Raman Spectroscopic Investigations of Bacteria

Lorenz, Björn; Wichmann, Christina; Stöckel, Stephan; Rösch, Petra; Popp, Jürgen

doi:10.1016/j.tim.2017.01.002

Cited by 176 publications

(190 citation statements)

References 93 publications

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“…Its recent spectacular awakening has brought it at the forefront of modern biochemical science (Baena & Lendl, ). In reviewing recent publications on biomolecules, it becomes clear how Raman spectroscopy has proved invaluable in unveiling a variety of issues in a number of key fields of microbiology, including metabolism of cells (M. Li, Romero‐Gonzales, Banwart, & Huang, ; Notingher, ; Notingher, Verrier, Haque, Polak, & Hench, ; Smith, Wright, & Ashton, ), bacteria (Gonchukov, Sukhinina, Bakhmutov, & Minaeva, ; Lorenz, Wichmann, Stockel, Rosch, & Popp, ; Novelli‐Rousseau et al, ; Strola et al, ), and viruses (Otange, Birech, Okonda, & Rop, ; Tuma & Thomas, ). The so far reported research also holds a promise for more accurate, fully quantitative, and rapid microbiological identifications of crucial issues in clinical diagnostics.…”

Section: Introductionmentioning

confidence: 99%

Metabolic machinery encrypted in the Raman spectrum of influenza A virus‐inoculated mammalian cells

Pezzotti

Zhu

Adachi

et al. 2019

Journal Cellular Physiology

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Raman spectroscopy was applied with a high spectral resolution to a structural study of Influenza (type A) virus before and after its inoculation into Madin–Darby canine kidney cells. This study exploits the fact that the major virus and cell constituents, namely DNA/RNA, lipid, and protein molecules, exhibit peculiar fingerprints in the Raman spectrum, which clearly differed between cells and viruses, as well as before and after virus inoculation into cells. These vibrational features, which allowed us to discuss viral assembly, membrane lipid evolution, and nucleoprotein interactions of the virus with the host cells, reflected the ability of the virus to alter host cells’ pathways to enhance its replication efficiency. Upon comparing Raman signals from the host cells before and after virus inoculation, we were also able to discuss in detail cell metabolic reactions against the presence of the virus in terms of compositional variations of lipid species, the formation of fatty acids, dephosphorylation of high‐energy adenosine triphosphate molecules, and enzymatic hydrolysis of the hemagglutinin glycoprotein.

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Section: Introductionmentioning

confidence: 99%

Metabolic machinery encrypted in the Raman spectrum of influenza A virus‐inoculated mammalian cells

Pezzotti

Zhu

Adachi

et al. 2019

Journal Cellular Physiology

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“…Raman spectroscopy saw dramatic growth in biological applications in the last two decades, thanks to its features: label‐free, noninvasive, and almost insensitive to water . Raman‐based studies have covered a large variety of biological fields, including but not limited to toxicology and forensics, microbiology, drug discovery, metabolic investigations, and even in vivo detection . Notably, Raman spectroscopy has found its place in process analytical technology (PAT); for example, it was employed to optimize ethanol fermentation .…”

Section: Introductionmentioning

confidence: 99%

Modified PCA and PLS: Towards a better classification in Raman spectroscopy–based biological applications

Guo

Rösch

Popp

et al. 2019

Journal of Chemometrics

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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.

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“…The improvement of chemometrics for Raman spectral data led to two benefits: The sensitivity of biological diagnostics is significantly increased, and large‐sized datasets can be analysed. These facts make the combination of Raman spectroscopy with chemometrics a versatile technique for biological applications, including disease detection, metabolic profiling, bacterial identification, intraoperative decision making, and forensic analysis …”

Section: Introductionmentioning

confidence: 99%

Model transfer for Raman‐spectroscopy‐based bacterial classification

Guo

Heinke

Stöckel

et al. 2018

J Raman Spectroscopy

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Raman spectroscopy has gained increasing attention in biomedical diagnostics thanks to instrumental developments and chemometric models that enhance the accuracy and speed of this technique. In particular, a model transfer procedure is needed if the chemometric models are utilized to predict a new dataset measured under (secondary) conditions different to the training data (primary). The model transfer methods try to achieve satisfactory prediction on the secondary dataset with minimal or no training samples measured under secondary conditions. Model transfer methods that have been reported are mostly applied for near‐infrared spectroscopy and in regression problems. The investigation of model transfer in Raman spectroscopy and classification is rare. Our recently reported Tikhonov regularization based on partial least squares regression (TR‐PLSR) was utilized for model transfer of Raman‐based classification models for spore species. In the present work, we show that the TR‐PLSR also works for Raman spectra of vegetative bacteria, even though the Raman spectra of 3 species of bacteria were acquired on 3 different Raman spectrometers. Additionally, we report 2 newly developed model transfer methods for Raman spectra: movement of principal components scores and spectral augmentation. Both methods were validated based on the Raman spectra of bacterial spores and vegetative bacteria, where a significant improvement of the model transferability was observed. The movement of principal components scores method yielded results comparable with those of the TR‐PLSR. However, the new methods are superior to TR‐PLSR in 2 ways: No training samples in the secondary conditions are necessary, and the methods are not restricted to partial least squares regression but can also be applied to other models. Both advantages are important in real‐world applications and represent a large step for improving the model transfer of Raman spectra.

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Cultivation-Free Raman Spectroscopic Investigations of Bacteria

Cited by 176 publications

References 93 publications

Metabolic machinery encrypted in the Raman spectrum of influenza A virus‐inoculated mammalian cells

Metabolic machinery encrypted in the Raman spectrum of influenza A virus‐inoculated mammalian cells

Modified PCA and PLS: Towards a better classification in Raman spectroscopy–based biological applications

Model transfer for Raman‐spectroscopy‐based bacterial classification

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