Raman spectral signature reflects transcriptomic features of antibiotic resistance in Escherichia coli

Germond, Arno; Ichimura, Taro; Horinouchi, Takaaki; Fujita, Hideaki; Furusawa, Chikara; Watanabe, Tomonobu M.

doi:10.1038/s42003-018-0093-8

Cited by 74 publications

(93 citation statements)

References 30 publications

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“…While some studies demonstrated that the fingerprint region of Raman spectra (200-1800 cm −1 ) were able to analyse the bacterial phenotype under antibiotic influences, and determine the MIC and resistant mechanisms of E. coli in different antibiotics (Athamneh et al, 2014;Teng et al, 2016;Tao et al, 2017;Xu et al, 2017;Germond et al, 2018;Kirchhoff et al, 2018;Novelli-Rousseau et al, 2018), the C-D band from SCRS is a simple and distinguishable Raman biomarker to identify the MA-ARB in environmental samples (Xu et al, 2017). In this study, we demonstrated that the C-D band can be used to identify the MA-ARB in human gut microbiota.…”

Section: Resultsmentioning

confidence: 99%

Raman‐activated sorting of antibiotic‐resistant bacteria in human gut microbiota

Wang

Kong

et al. 2020

Environmental Microbiology

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Summary The antibiotic‐resistant bacteria (ARB) and antibiotic‐resistant genes (ARGs) in human gut microbiota have significant impact on human health. While high throughput metagenomic sequencing reveals genotypes of microbial communities, the functionality, phenotype and heterogeneity of human gut microbiota are still elusive. In this study, we applied Raman microscopy and deuterium isotope probing (Raman–DIP) to detect metabolic active ARB (MA‐ARB) in situ at the single‐cell level in human gut microbiota from two healthy adults. We analysed the relative abundances of MA‐ARB under different concentrations of amoxicillin, cephalexin, tetracycline, florfenicol and vancomycin. To establish the link between phenotypes and genotypes of the MA‐ARB, Raman‐activated cell sorting (RACS) was used to sort MA‐ARB from human gut microbiota, and mini‐metagenomic DNA of the sorted bacteria was amplified, sequenced and analysed. The sorted MA‐ARB and their associated ARGs were identified. Our results suggest a strong relation between ARB in human gut microbiota and personal medical history. This study demonstrates that the toolkit of Raman–DIP, RACS and DNA sequencing can be useful to unravel both phenotypes and genotypes of ARB in human gut microbiota at the single‐cell level.

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Section: Resultsmentioning

confidence: 99%

Raman‐activated sorting of antibiotic‐resistant bacteria in human gut microbiota

Wang

Kong

et al. 2020

Environmental Microbiology

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“…For intra-dataset analysis, 50% of the spectra were randomly selected as training data, and the remaining 50% were used as test data; the random selection was repeated 100 times. Classification models were built using PCA and quadratic discriminant analysis (QDA) 22 . Likelihood ratios of QDA to discriminate KML1 cells from B cells were calculated, and log-likelihood ratio = 0 was used as threshold value of the discrimination.…”

Section: Resultsmentioning

confidence: 99%

“…S1 and S3). Collecting the full spectrum, including small peaks, will be important for discrimination, as noted in a previous study using a different type of cells 22 . Laser power will need to meet these two requirements.…”

Section: Discussionmentioning

confidence: 99%

Discrimination of dissociated lymphoma cells from leukocytes by Raman spectroscopy

Iwasaki

Kawagishi

Takase

et al. 2020

Sci Rep

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Diagnosis of intraocular lymphoma is difficult. Among the hurdles in the diagnosis are the variety of reactive inflammatory and ischemic changes among intraocular lymphoma patients. Thus, a novel diagnostic method is desired such that lymphoma cells can be distinguished by the signals intrinsic to the cells, not by those from the surrounding tissues with reactive changes. Raman spectroscopy is a technique that can detect intrinsic signals from each cell. Therefore, Raman spectroscopy is a good candidate for an intraocular evaluation technology that could contribute to improve the diagnosis of intraocular lymphoma. In this study, we tested whether the intrinsic Raman signals from malignant lymphoma cells, in the absence of surrounding tissue, were sufficient for the discrimination of malignant lymphoma cells from leukocytes. We acquired spectra from dissociated lymphoma cells, along with spectra from normal B cells and other leukocytes involved in intraocular inflammatory diseases. We analysed the spectra using principal component analyses and quadratic discriminant analyses. We found that Raman spectra from dissociated cells without confounding tissues showed high discriminating ability, regardless of the variation due to day-to-day differences and donor differences. The present study demonstrates the possible effectiveness of Raman spectroscopy as a tool for intraocular evaluation.

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“…A normalized spectral data should be used as a training dataset, and an unknown dataset (without label) from a replicate experiment should be used as test data, if possible. NOTE: Discriminant analysis performed on some vector of a principal component analysis (PCA-DA 10 ), projection on latent scores followed by discriminant analysis (PLS-DA), and support vector machines (SVM) 11 are models often used in the field, and each presents different statistical considerations. Preprocessing of data should be performed consequently.…”

Section: Preprocessing Of Spectral Data and Multivariate Analysesmentioning

confidence: 99%

“…NOTES: The VIP score of a variable is calculated as a weighted sum of the squared correlations between the PLS-DA components and original variable. Details regarding PLS and VIP scores algorithm can be found in the literature 11,12 .…”

Section: Preprocessing Of Spectral Data and Multivariate Analysesmentioning

confidence: 99%

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects

Ali

Abouleila

Germond

2020

JoVE

Self Cite

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Cells are known to be inherently heterogeneous in their responses to drugs. Therefore, it is essential that single-cell heterogeneity is accounted for in drug discovery studies. This can be achieved by accurately measuring the plethora of cellular interactions between a cell and drug at the single-cell level (i.e., drug uptake, metabolism, and effect). This paper describes a single-cell Raman spectroscopy and mass spectrometry (MS) platform to monitor metabolic changes of cells in response to drugs. Using this platform, metabolic changes in response to the drug can be measured by Raman spectroscopy, while the drug and its metabolite can be quantified using mass spectrometry in the same cell. The results suggest that it is possible to access information about drug uptake, metabolism, and response at a single-cell level.

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Raman spectral signature reflects transcriptomic features of antibiotic resistance in Escherichia coli

Cited by 74 publications

References 30 publications

Raman‐activated sorting of antibiotic‐resistant bacteria in human gut microbiota

Raman‐activated sorting of antibiotic‐resistant bacteria in human gut microbiota

Discrimination of dissociated lymphoma cells from leukocytes by Raman spectroscopy

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects

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