A study on identification of bacteria in environmental samples using single-cell Raman spectroscopy: feasibility and reference libraries

Baritaux, Jean-Charles; Simon, A.; Schultz, E; Emain, Charlotte; Laurent, Patricia; Dinten, Jean‐Marc

doi:10.1007/s11356-015-5953-x

Cited by 12 publications

(5 citation statements)

References 17 publications

(21 reference statements)

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“…From 2004, with the advent of confocal microscopy, high-magnification objectives and availability of highly sensitivity detectors, single bacterial identification and imaging through Raman have been explored [28,29]. Studies have reported single bacterial identification for medically and environmentally relevant bacteria like anthrax [30], in urinary tract infection [31], in water pathogens [32,33], in meat-associated pathogens [34], and in many other samples [16,[35][36][37] using multivariate analysis techniques. Raman spectroscopy along with supervised multivariate analysis techniques like support vector machine (SVM) has also been employed to identify TB pathogens, but no studies has reported use of Raman for identification and viability assessment [38].…”

Section: Discussionmentioning

confidence: 99%

Rapid detection of bacterial infection and viability assessment with high specificity and sensitivity using Raman microspectroscopy

et al. 2020

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Infectious diseases caused by bacteria still pose major diagnostic challenges in spite of the availability of various molecular approaches. Irrespective of the type of infection, rapid identification of the causative pathogen with a high degree of sensitivity and specificity is essential for initiating appropriate treatment. While existing methods like PCR possess high sensitivity, they are incapable of identifying the viability status of the pathogen and those which can, like culturing, are inherently slow. To overcome these limitations, we developed a diagnostic platform based on Raman microspectroscopy, capable of detecting biochemical signatures from a single bacterium for identification as well as viability assessment. The study also establishes a decontamination protocol for handling live pathogenic bacteria which does not affect identification and viability testing, showing applicability in the analysis of sputum samples containing pathogenic mycobacterial strains. The minimal sample processing along with multivariate analysis of spectroscopic signatures provides an interface for automatic classification, allowing the prediction of unknown samples by mapping signatures onto available datasets. Also, the novelty of the current work is the demonstration of simultaneous identification and viability assessment at a single bacterial level for pathogenic bacteria.

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Section: Discussionmentioning

confidence: 99%

Rapid detection of bacterial infection and viability assessment with high specificity and sensitivity using Raman microspectroscopy

et al. 2020

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“…Raman spectra of a single bacterial cell, also known as single-cell Raman spectrum, represent detailed information of various biomolecules present in the bacterial cells like protein, DNA, carbohydrates, and lipids. Thus, the Raman spectrum of a bacterial cell can be an accurate fingerprint of the bacterial cell type. − It offers a significant advantage over existing clinical approaches, especially where the number of bacteria in a biomaterial is limited or very low. This approach can be used directly for samples that are isolated from patients without needing additional cultivation steps, which can affect the result and thus make it less time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Culture-Independent Raman Spectroscopic Identification of Bacterial Pathogens from Clinical Samples Using Deep Transfer Learning

et al. 2022

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The rapid identification of bacterial pathogens in clinical samples like blood, urine, pus, and sputum is the need of the hour. Conventional bacterial identification methods like culturing and nucleic acid-based amplification have limitations like poor sensitivity, high cost, slow turnaround time, etc. Raman spectroscopy, a label-free and noninvasive technique, has overcome these drawbacks by providing rapid biochemical signatures from a single bacterium. Raman spectroscopy combined with chemometric methods has been used effectively to identify pathogens. However, a robust approach is needed to utilize Raman features for accurate classification while dealing with complex data sets such as spectra obtained from clinical isolates, showing high sample-to-sample heterogeneity. In this study, we have used Raman spectroscopy-based identification of pathogens from clinical isolates using a deep transfer learning approach at the single-cell level resolution. We have used the data-augmentation method to increase the volume of spectra needed for deep-learning analysis. Our ResNet model could specifically extract the spectral features of eight different pathogenic bacterial species with a 99.99% classification accuracy. The robustness of our model was validated on a set of blinded data sets, a mix of cultured and noncultured bacterial isolates of various origins and types. Our proposed ResNet model efficiently identified the pathogens from the blinded data set with high accuracy, providing a robust and rapid bacterial identification platform for clinical microbiology.

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“…Raman microspectroscopy allows exploring intracellular transformations and their features and helps to understand the processes occurring in the cell when studying the properties of the selective interaction of reagents in the cell . Recently, achievements in Raman microspectroscopy have opened up new prospects for the rapid and sensitive detection of bacteria of various types …”

Section: Introductionmentioning

confidence: 99%

Micro Raman spectroscopy for NETosis detection

Arzumanyan

Гурьев²,

Kravtsunova³

et al. 2020

J Raman Spectroscopy

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Over the past few years, Raman spectroscopy has become a powerful diagnostic tool in the life sciences. The present work is devoted to the application of Raman microspectroscopy for distinction of neutrophils transformed during NETosis and the quantitative determination of the level of their transformation based on the analysis of the neutrophil Raman spectra acquired from the samples of human blood at different levels of transformation. NETosis is a process of the programmed neutrophil cell death involved in the development of many diseases, including those associated with high mortality. Our goal was to search for possible spectral markers in neutrophil Raman spectra, caused precisely by NETotic transformation of neutrophils. The results of the work were (a) obtaining of neutrophil Raman spectra at different levels of cell transformation; (b) creation of spectral archetypes of neutrophils (as a "representative" Raman spectrum for spectra group) with a given level of cell transformation; (c) detection of statistically significant differences in the spectral archetypes of the neutrophil Raman spectra at different levels of transformation. K E Y W O R D S human blood neutrophils, NETosis, microRaman spectroscopy, spectra preprocessing, spectral marker 1 | INTRODUCTION Raman microspectroscopy is widely used in the studies of cell biology, microbiology, and medicine for optical analysis of biological objects at the cellular level. [1-6] Raman microspectroscopy allows exploring intracellular transformations and their features [7-12] and helps to understand the processes occurring in the cell when studying the properties of the selective interaction of reagents in the cell. [13-15] Recently, achievements in Raman microspectroscopy have opened up new prospects for the rapid and sensitive detection of bacteria of various types. [16-21] Neutrophils are the most common human blood leukocytes, which are the most important part of the innate immunity and carry out a fast response to microbial invasion. In 2004, a new type of programmed cell death of neutrophils, called NETosis, [22] was described. Currently,

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A study on identification of bacteria in environmental samples using single-cell Raman spectroscopy: feasibility and reference libraries

Cited by 12 publications

References 17 publications

Rapid detection of bacterial infection and viability assessment with high specificity and sensitivity using Raman microspectroscopy

Rapid detection of bacterial infection and viability assessment with high specificity and sensitivity using Raman microspectroscopy

Culture-Independent Raman Spectroscopic Identification of Bacterial Pathogens from Clinical Samples Using Deep Transfer Learning

Micro Raman spectroscopy for NETosis detection

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