Rapid identification of mycobacteria by Raman spectroscopy

Methods for the Inactivation of Mycobacterium tuberculosis: a Systematic Review of the Literature

To systematize published laboratory methods to inactivate Mycobacterium tuberculosis (MTB) and to describe their effectiveness. We carried out a review of the scientific literature to identify the publications that reported methods for the inactivation of MTB, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses recommendations. The search addressed inactivation methodologies used in Public health laboratories for the treatment of biological material and only included studies reporting the efficacy of the method. The database used were PubMed (National Library of Medicine) and LILACS (Latin American and Caribbean Literature in Health Sciences). We evaluated the quality of the studies with the JBI (Joanna Briggs Institute) Critical Instrument Appraisal Checklist. We included 14 publications meeting the established inclusion and exclusion criteria. These 14 studies actually described a total of 35 inactivation methods. Most of them combined heat treatment with some chemical or enzymatic agent, and there were very few applying a single strategy. Twenty-four (68.57%) methods demonstrated significant efficacy in inactivating MTB. The systematic review identified 35 methods of inactivation of MTB, published in 14 studies. Most of the methods combined physical treatment techniques, especially heat, with chemical and/or enzymatic treatment techniques, obtaining mostly good results in preventing the reproduction of the microorganism. PROSPERO (International Prospective Register of Ongoing Systematic Reviews) (Code CRD42024503621).

Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms

Raman spectroscopy is an emerging method for the identification of bacteria. Nevertheless, a lot of different parameters need to be considered to establish a reliable database capable of identifying real-world samples such as medical or environmental probes. In this review, the establishment of such reliable databases with the proper design in microbiological Raman studies is demonstrated, shining a light into all the parts that require attention. Aspects such as the strain selection, sample preparation and isolation requirements, the phenotypic influence, measurement strategies, as well as the statistical approaches for discrimination of bacteria, are presented. Furthermore, the influence of these aspects on spectra quality, result accuracy, and read-out are discussed. The aim of this review is to serve as a guide for the design of microbiological Raman studies that can support the establishment of this method in different fields.

Machine learning analysis of SERS fingerprinting for the rapid determination of Mycobacterium tuberculosis infection and drug resistance

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Handheld Raman spectrometer is able to generate SERS spectra with sufficient quality for Mycobacterium tuberculosis detection.

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It is feasible to accurately discriminate Mtb-positive sputum from Mtb-negative sputum through SERS spectrometry.

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Pulmonary and extra-pulmonary Mtb strains were able to be accurately distinguished via SERS spectral analysis.

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Profiling of antibiotic resistance of Mtb strains was successfully achieved through machine learning analysis of SERS spectra.

Over the past decades, conventional methods and molecular assays have been developed for the detection of tuberculosis (TB). However, these techniques suffer limitations in the identification of Mycobacterium tuberculosis (Mtb), such as long turnaround time and low detection sensitivity, etc., not even mentioning the difficulty in discriminating antibiotics-resistant Mtb strains that cause great challenges in TB treatment and prevention. Thus, techniques with easy implementation for rapid diagnosis of Mtb infection are in high demand for routine TB diagnosis. Due to the label-free, low-cost and non-invasive features, surface enhanced Raman spectroscopy (SERS) has been extensively investigated for its potential in bacterial pathogen identification. However, at current stage, few studies have recruited handheld Raman spectrometer to discriminate sputum samples with or without Mtb, separate pulmonary Mtb strains from extra-pulmonary Mtb strains, or profile Mtb strains with different antibiotic resistance characteristics. In this study, we recruited a set of supervised machine learning algorithms to dissect different SERS spectra generated via a handheld Raman spectrometer with a focus on deep learning algorithms, through which sputum samples with or without Mtb strains were successfully differentiated (5-fold cross-validation accuracy = 94.32%). Meanwhile, Mtb strains isolated from pulmonary and extra-pulmonary samples were effectively separated (5-fold cross-validation accuracy = 99.86%). Moreover, Mtb strains with different drug-resistant profiles were also competently distinguished (5-fold cross-validation accuracy = 99.59%). Taken together, we concluded that, with the assistance of deep learning algorithms, handheld Raman spectrometer has a high application potential for rapid point-of-care diagnosis of Mtb infections in future.

First preliminary study on identification of bacterial fish pathogens with Raman spectroscopy

Accurate and rapid determination of bacterial disease agents of fish is an important step for sustainable and efficient aquaculture production. In general, biochemical and molecular methods are used for pathogen detection but they are usually time-consuming and required qualified personnel. Recently spectroscopic methods are preferred in clinical and food microbiology and declared as a promising alternative method for pathogens diagnosis with many advantages. In this study, the significant spectra of three important bacterial fish pathogens (Lactococcus garvieae, Vibrio anguillarum and Yersinia ruckeri) were determined by Raman spectroscopy. The first data of the pathogens were obtained and the distinctive differences in polysaccharides, nucleic acids, fatty acids and amino acids were identified. This preliminary study aimed to be pioneer for further studies in aquaculture and veterinary microbiology toward developing an alternative method for routine identification.

Sensible Functional Linear Discriminant Analysis Effectively Discriminates Enhanced Raman Spectra of Mycobacterium Species

Tuberculosis caused by Mycobacterium tuberculosis complex (MTBC) is one of the major infectious diseases in the world. Identification of MTBC and differential diagnosis of nontuberculous mycobacteria (NTM) species impose challenges because of their taxonomic similarity. This study describes a differential diagnosis method using the surface-enhanced Raman scattering (SERS) measurement of molecules released by Mycobacterium species. Conventional principal component analysis and linear discriminant analysis methods successfully separated the acquired spectrum of MTBC from those of NTM species but failed to distinguish between the spectra of different NTM species. A novel sensible functional linear discriminant analysis (SLDA), projecting the averaged spectrum of a bacterial specie to the subspace orthogonal to the within-species random variation, thereby eliminating its influence in applying linear discriminant analysis, was employed to effectively discriminate not only MTBC but also species of NTM. The successful demonstration of this SERS-SLDA method opens up new opportunities for the rapid differentiation of Mycobacterium species.

Biofilms of the non-tuberculous Mycobacterium chelonae form an extracellular matrix and display distinct expression patterns

Mycobacterium chelonae is an environmental, non-tuberculous mycobacterial species, capable of causing infections in humans. Biofilm formation is a key strategy used by M. chelonae in colonising niches in the environment and in the host. We studied a water-air interface (pellicle) biofilm of M. chelonae using a wide array of approaches to outline the molecular structure and composition of the biofilm. Scanning electron micrographs showed that M. chelonae biofilms produced an extracellular matrix. Using a combination of biochemical analysis, Raman spectroscopy, and fluorescence microscopy, we showed the matrix to consist of proteins, carbohydrates, lipids and eDNA. Glucose was the predominant sugar present in the biofilm matrix, and its relative abundance decreased in late (established) biofilms. RNA-seq analysis of the biofilms showed upregulation of genes involved in redox metabolism. Additionally, genes involved in mycolic acid, other lipid and glyoxylate metabolism were also upregulated in the early biofilms.

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

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. Graphical abstract.

Raman spectroscopy reveals distinct differences between two closely related bacterial strains, Mycobacterium indicus pranii and Mycobacterium intracellulare

A common technique used to differentiate bacterial species and to determine evolutionary relationships is sequencing their 16S ribosomal RNA genes. However, this method fails when organisms exhibit high similarity in these sequences. Two such strains that have identical 16S rRNA sequences are Mycobacterium indicus pranii (MIP) and Mycobacterium intracellulare. MIP is of significance as it is used as an adjuvant for protection against tuberculosis and leprosy; in addition, it shows potent anti-cancer activity. On the other hand, M. intracellulare is an opportunistic pathogen and causes severe respiratory infections in AIDS patients. It is important to differentiate these two bacterial species as they co-exist in immuno-compromised individuals. To unambiguously distinguish these two closely related bacterial strains, we employed Raman and resonance Raman spectroscopy in conjunction with multivariate statistical tools. Phenotypic profiling for these bacterial species was performed in a kinetic manner. Differences were observed in the mycolic acid profile and carotenoid pigments to show that MIP is biochemically distinct from M. intracellulare. Resonance Raman studies confirmed that carotenoids were produced by both MIP as well as M. intracellulare, though the latter produced higher amounts. Overall, this study demonstrates the potential of Raman spectroscopy in differentiating two closely related mycobacterial strains. Graphical abstract.

Effect of growth media and phase on Raman spectra and discrimination of mycobacteria

When developing a Raman spectral library to identify bacteria, differences between laboratory and real world conditions must be considered. For example, culturing bacteria in laboratory settings is performed under conditions for ideal bacteria growth. In contrast, culture conditions in the human body may differ and may not support optimized bacterial growth. To address these differences, researchers have studied the effect of conditions such as growth media and phase on Raman spectra. However, the majority of these studies focused on Gram-positive or Gram-negative bacteria. This article focuses on the influence of growth media and phase on Raman spectra and discrimination of mycobacteria, an acid-fast genus. Results showed that spectral differences from growth phase and media can be distinguished by spectral observation and multivariate analysis. Results were comparable to those found for other types of bacteria, such as Gram-positive and Gram-negative. In addition, the influence of growth phase and media had a significant impact on machine learning models and their resulting classification accuracy. This study highlights the need for machine learning models and their associated spectral libraries to account for various growth parameters and stages to further the transition of Raman spectral analysis of bacteria from laboratory to clinical settings.

Micro-Raman spectroscopy in medicine

A potential role of optical technologies in medicine including micro-Raman spectroscopy is diagnosis of bacteria, cells and tissues which is covered in this chapter. The main advantage of Raman-based methods to complement and augment diagnostic tools is that unsurpassed molecular specificity is achieved without labels and in a nondestructive way. Principles and applications of micro-Raman spectroscopy in the context of medicine will be described. First, Raman spectra of biomolecules representing proteins, nucleic acids, lipids and carbohydrates are introduced. Second, microbial applications are summarized with the focus on typing on species and strain level, detection of infections, antibiotic resistance and biofilms. Third, cytological applications are presented to classify single cells and study cell metabolism and drug–cell interaction. Fourth, applications to tissue characterization start with discussion of lateral resolution for Raman imaging followed by Raman-based detection of pathologies and combination with other modalities. Finally, an outlook is given to translate micro-Raman spectroscopy as a clinical tool to solve unmet needs in point-of-care applications and personalized treatment of diseases.

Identification of mycolic acid forms using surface-enhanced Raman scattering as a fast detection method for tuberculosis

Background

Tuberculosis (TB) is the ninth leading cause of death worldwide and the leading cause from a single infectious agent, based on the WHO Global Tuberculosis Report in 2017. TB causes massive health care burdens in many parts of the world, specifically in the resource constrained developing world. Most deaths from TB could be prevented with cost effective early diagnosis and appropriate treatment.

Purpose

Conventional TB detection methods are either too slow as it takes a few weeks for diagnosis or they lack the specificity and accuracy. Thus the objective of this study was to develop a fast and efficient detection for TB using surface enhanced Raman scattering (SERS) technique.

Methods

SERS spectra for different forms of mycolic acids (MAs) that are both synthetic origin and actual extracts from the mycobacteria species were obtained by label-free direct detection mode. Similarly, we collected SERS spectra for γ-irradiated whole bacteria (WB). Measurements were done using silver (Ag) coated silicon nanopillar (Ag SNP) as SERS substrate.

Results

We report the SERS based detection of MA, which is a biomarker for mycobacteria species including Mycobacterium tuberculosis. For the first time, we also establish the SERS spectral characterization of the three major forms of MA - αMA, methoxy-MA, and keto-MA, in bacterial extracts and also in γ-irradiated WB. We validated our findings by mass spectrometry. SERS detection of these three forms of MA could be useful in differentiating pathogenic and nonpathogenic Mycobacterium spp.

Conclusions

We have demonstrated the direct detection of three major forms of MA - αMA, methoxy-MA, and keto-MA, in two different types of MA extracts from MTB bacteria, namely delipidated MA and undelipidated MA and finally in γ-irradiated WB. In the near future, this study could pave the way for a fast and efficient detection method for TB, which is of high clinical significance.

Biological and Medical Applications of Multivariate Curve Resolution Assisted Raman Spectroscopy

Biological specimens such as cells, tissues and biofluids (urine, blood) contain mixtures of many different biomolecules, all of which contribute to a Raman spectrum at any given point. The separation and identification of pure biochemical components remains one of the biggest challenges in Raman spectroscopy. Multivariate curve resolution, a matrix factorization method, is a powerful, yet flexible, method that can be used with constraints, such as non-negativity, to decompose a complex spectroscopic data matrix into a small number of physically meaningful pure spectral components along with their relative abundances. This paper reviews recent applications of multivariate curve resolution by alternating least squares analysis to Raman spectroscopic and imaging data obtained either in vivo or in vitro from biological and medical samples.

Diagnostic Bacteriology: Raman Spectroscopy

Current clinical methodology for identification of bacterial infections relies predominantly on culturing microbes from patient material and performing biochemical tests. This can often be an inefficient and lengthy process, which has a significant detrimental effect upon patient care. Techniques used in other aspects of molecular research have the potential to revolutionize the way in which diagnostic tests are used and delivered in the clinical setting. The need for rapid, accurate, and cost-effective molecular techniques in the diagnostic laboratory is imperative to improving patient care, preventing the spread of drug resistance and decreasing the overall burden associated with nosocomial infections. Raman spectroscopy and surface-enhanced Raman spectroscopy (SERS) are powerful vibrational spectroscopy techniques that are being developed for highly sensitive pathogen identification in complex clinical samples. Raman spectroscopy is a molecular technique that is capable of probing samples noninvasively and nondestructively. It has been used with high specificity to assess tissue and bacterial samples at the molecular level with diverse clinical and diagnostic applications. SERS has recently developed out of the advances in the Raman spectroscopy arena. This technique is designed to amplify Raman scattering and allows for better differentiation of bacterial isolates. Although the current parameters for the use of SERS require a pure culture and are relatively monoparametric, current breakthroughs and testing are pushing the technology to new levels and thus changing the face of modern bacterial diagnostics.

Tuberculosis determination using SERS and chemometric methods

Nanostructures have been multiplying the advantages of Raman spectroscopy and further amplify the advantages of Raman spectroscopy is a continuous effort focused on the appropriate design of nanostructures. Herein, we designed different shapes of plasmonic nanostructures such as Vertical, Zig Zag, Slant nanorods and Spherical nanoparticles employing the DC magnetron sputtering system as SERS-active substrates for ultrasensitive detection of target molecules. The fabricated plasmonic nanostructures sensitivity and uniformity were exploited by reference dye analyte. These nanostructures were utilized in the label free detection of infectious disease, Tuberculosis (TB). For the first time, TB detection from serum samples using SERS has been demonstrated. Various multivariate statistical methods such as principal component analysis, support vector machine, decision tree and random forest were developed and tested their ability to discriminate the healthy and active TB samples. The results demonstrate the performance of the SERS spectra, chemometric methods and potential of the method in clinical diagnosis.

Detecting Phenotypically Resistant Mycobacterium tuberculosis Using Wavelength Modulated Raman Spectroscopy

Raman spectroscopy is a non-destructive and label-free technique. Wavelength modulated Raman (WMR) spectroscopy was applied to investigate Mycobacterium tuberculosis cell state, lipid rich (LR) and lipid poor (LP). Compared to LP cells, LR cells can be up to 40 times more resistant to key antibiotic regimens. Using this methodology single lipid rich (LR) from lipid poor (LP) bacteria can be differentiated with both high sensitivity and specificity. It can also be used to investigate experimentally infected frozen tissue sections where both cell types can be differentiated. This methodology could be utilized to study the phenotype of mycobacterial cells in other tissues.

A Critical Review on Ultrasensitive, Spectroscopic-based Methods for High-throughput Monitoring of Bacteria during Infection Treatment

The world is in the midst of a pre-emptive public health emergency, one that is just as dramatic as the global aggressive viruses-related crises (Ebola, Zika, or SARS), but not as visible. The "superbugs" and their antimicrobial resistance do not cause much public alarm or awareness, but provoke financial losses of $100 trillion annually (WHO, http://www.who.int/mediacentre/commentaries/superbugs-action-now/en/ ). This status quo review offers an overview of ultrasensitive methods for high-throughput monitoring of bacteria during infection treatment, the effects of antibiotics on bacteria at single-cell level and the challenges we will face in their detection due to the extraordinary capability of these "superbugs" to gain and constantly improve multiresistance to antibiotics. A special emphasis is put on the ultrasensitive spectroscopic-based analysis techniques, using nanotechnology or not necessarily, that are more and more promising alternatives to conventional culture-based ones. The particular case of Mycobacteria detection is discussed based on recent reported work.

Label-free optical vibrational spectroscopy to detect the metabolic state of M. tuberculosis cells at the site of disease

Tuberculosis relapse is a barrier to shorter treatment. It is thought that lipid rich cells, phenotypically resistant to antibiotics, may play a major role. Most studies investigating relapse use sputum samples although tissue bacteria may play an important role. We developed a non-destructive, label-free technique combining wavelength modulated Raman (WMR) spectroscopy and fluorescence detection (Nile Red staining) to interrogate Mycobacterium tuberculosis cell state. This approach could differentiate single "dormant" (lipid rich, LR) and "non-dormant" (lipid poor, LP) cells with high sensitivity and specificity. We applied this to experimentally infected guinea pig lung sections and were able to distinguish both cell types showing that the LR phenotype dominates in infected tissue. Both in-vitro and ex-vivo spectra correlated well, showing for the first time that Mycobacterium tuberculosis, likely to be phenotypically resistant to antibiotics, are present in large numbers in tissue. This is an important step in understanding the pathology of relapse supporting the idea that they may be caused by M. tuberculosis cells with lipid inclusions.

Accurate and Rapid Differentiation of Acinetobacter baumannii Strains by Raman Spectroscopy: a Comparative Study

In recent years, matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) has become the standard for routine bacterial species identification due to its rapidity and low costs for consumables compared to those of traditional DNA-based methods. However, it has been observed that strains of some bacterial species, such as Acinetobacter baumannii strains, cannot be reliably identified using mass spectrometry (MS). Raman spectroscopy is a rapid technique, as fast as MALDI-TOF, and has been shown to accurately identify bacterial strains and species. In this study, we compared hierarchical clustering results for MS, genomic, and antimicrobial susceptibility test data to hierarchical clustering results from Raman spectroscopic data for 31 A. baumannii clinical isolates labeled according to their pulsed-field gel electrophoresis data for strain differentiation. In addition to performing hierarchical cluster analysis (HCA), multiple chemometric methods of analysis, including principal-component analysis (PCA) and partial least-squares discriminant analysis (PLSDA), were performed on the MS and Raman spectral data, along with a variety of spectral preprocessing techniques for best discriminative results. Finally, simple HCA algorithms were performed on all of the data sets to explore the relationships between, and natural groupings of, the strains and to compare results for the four data sets. To obtain numerical comparison values of the clustering results, the external cluster evaluation criteria of the Rand index of the HCA dendrograms were calculated. With a Rand index value of 0.88, Raman spectroscopy outperformed the other techniques, including MS (with a Rand index value of 0.58).

Effect of Principal Component Analysis Centering and Scaling on Classification of Mycobacteria from Raman Spectra

Raman spectroscopy has been used for decades to detect and identify biological substances as it provides specific molecular information. Spectra collected from biological samples are often complex, requiring the aid of data truncation techniques such as principal component analysis (PCA) and multivariate classification methods. Classification results depend on the proper selection of principal components (PCs) and how PCA is performed (scaling and/or centering). There are also guidelines for choosing the optimal number of PCs such as a scree plot, Kaiser criterion, or cumulative percent variance. The goal of this research is to evaluate these methods for best implementation of PCA and PC selection to classify Raman spectra of bacteria. Raman spectra of three different isolates of mycobacteria ( Mycobacterium sp. JLS, Mycobacterium sp. KMS, Mycobacterium sp. MCS) were collected and then passed through PCA and linear discriminant analysis for classification. Principal component analysis implementation as well as PC selection was evaluated by comparing the highest possible classification accuracies against accuracies determined by PC selection methods for each centering and scaling option. Centered and unscaled data provided the best results when selecting PCs based on cumulative percent variance.

Raman spectroscopic identification of Mycobacterium tuberculosis

In this study, Raman microspectroscopy has been utilized to identify mycobacteria to the species level. Because of the slow growth of mycobacteria, the per se cultivation-independent Raman microspectroscopy emerges as a perfect tool for a rapid on-the-spot mycobacterial diagnostic test. Special focus was laid upon the identification of Mycobacterium tuberculosis complex (MTC) strains, as the main causative agent of pulmonary tuberculosis worldwide, and the differentiation between pathogenic and commensal nontuberculous mycobacteria (NTM). Overall the proposed model considers 26 different mycobacteria species as well as antibiotic susceptible and resistant strains. More than 8800 Raman spectra of single bacterial cells constituted a spectral library, which was the foundation for a two-level classification system including three support vector machines. Our model allowed the discrimination of MTC samples in an independent validation dataset with an accuracy of 94% and could serve as a basis to further improve Raman microscopy as a first-line diagnostic point-of-care tool for the confirmation of tuberculosis disease.

Advances in microfluidics in combating infectious diseases

One of the important pursuits in science and engineering research today is to develop low-cost and user-friendly technologies to improve the health of people. Over the past decade, research efforts in microfluidics have been made to develop methods that can facilitate low-cost diagnosis of infectious diseases, especially in resource-poor settings. Here, we provide an overview of the recent advances in microfluidic devices for point-of-care (POC) diagnostics for infectious diseases and emphasis is placed on malaria, sepsis and AIDS/HIV. Other infectious diseases such as SARS, tuberculosis, and dengue are also briefly discussed. These infectious diseases are chosen as they contribute the most to disability-adjusted life-years (DALYs) lost according to the World Health Organization (WHO). The current state of research in this area is evaluated and projection toward future applications and accompanying challenges are also discussed.

Raman-based microarray readout: a review

For a quarter of a century, microarrays have been part of the routine analytical toolbox. Label-based fluorescence detection is still the commonest optical readout strategy. Since the 1990s, a continuously increasing number of label-based as well as label-free experiments on Raman-based microarray readout concepts have been reported. This review summarizes the possible concepts and methods and their advantages and challenges. A common label-based strategy is based on the binding of selective receptors as well as Raman reporter molecules to plasmonic nanoparticles in a sandwich immunoassay, which results in surface-enhanced Raman scattering signals of the reporter molecule. Alternatively, capture of the analytes can be performed by receptors on a microarray surface. Addition of plasmonic nanoparticles again leads to a surface-enhanced Raman scattering signal, not of a label but directly of the analyte. This approach is mostly proposed for bacteria and cell detection. However, although many promising readout strategies have been discussed in numerous publications, rarely have any of them made the step from proof of concept to a practical application, let alone routine use. Graphical Abstract Possible realization of a SERS (Surface-Enhanced Raman Scattering) system for microarray readout.

Nanotools and molecular techniques to rapidly identify and fight bacterial infections

Reducing the emergence and spread of antibiotic-resistant bacteria is one of the major healthcare issues of our century. In addition to the increased mortality, infections caused by multi-resistant bacteria drastically enhance the healthcare costs, mainly because of the longer duration of illness and treatment. While in the last 20years, bacterial identification has been revolutionized by the introduction of new molecular techniques, the current phenotypic techniques to determine the susceptibilities of common Gram-positive and Gram-negative bacteria require at least two days from collection of clinical samples. Therefore, there is an urgent need for the development of new technologies to determine rapidly drug susceptibility in bacteria and to achieve faster diagnoses. These techniques would also lead to a better understanding of the mechanisms that lead to the insurgence of the resistance, greatly helping the quest for new antibacterial systems and drugs. In this review, we describe some of the tools most currently used in clinical and microbiological research to study bacteria and to address the challenge of infections. We discuss the most interesting advancements in the molecular susceptibility testing systems, with a particular focus on the many applications of the MALDI-TOF MS system. In the field of the phenotypic characterization protocols, we detail some of the most promising semi-automated commercial systems and we focus on some emerging developments in the field of nanomechanical sensors, which constitute a step towards the development of rapid and affordable point-of-care testing devices and techniques. While there is still no innovative technique that is capable of completely substituting for the conventional protocols and clinical practices, many exciting new experimental setups and tools could constitute the basis of the standard testing package of future microbiological tests.

Potential of Raman Spectroscopy To Monitor Arsenic Toxicity on Bacteria: Insights toward Multiparametric Bioassays

In the field of toxicological bioassays, the latest progress in Raman spectroscopy opens new research perspectives on a fast method of observing metabolic responses against toxic agents. This technique offers a multiparametric approach, providing an overview of the physiological changes that are caused by pollutants. However, physiological spectral fingerprints require complex chemometric methods for proper analysis. In this study, particular attention has been given to the elaboration of an "aberrant spectra" detection strategy to highlight the effects of arsenic on the bacteria Escherichia coli. This strategy significantly improved spectra classification, consistent with a dose-response effect of the four tested concentrations of the metal. Indeed, the correct classification score of the spectra increased from 88 to more than 99%. The exposure time effect has also been investigated. The fine analysis of Raman spectroscopy fingerprints enabled the design of different "spectral signatures", highlighting early and late effects of arsenic on bacteria. The observed variations are in agreement with the expected toxicity and encourage the use of Raman spectroscopy for toxic element detection.

Isolation and identification of bacteria by means of Raman spectroscopy

Bacterial detection is a highly topical research area, because various fields of application will benefit from the progress being made. Consequently, new and innovative strategies which enable the investigation of complex samples, like body fluids or food stuff, and improvements regarding the limit of detection are of general interest. Within this review the prospects of Raman spectroscopy as a reliable tool for identifying bacteria in complex samples are discussed. The main emphasis of this work is on important aspects of applying Raman spectroscopy for the detection of bacteria like sample preparation and the identification process. Several approaches for a Raman compatible isolation of bacterial cells have been developed and applied to different matrices. Here, an overview of the limitations and possibilities of these methods is provided. Furthermore, the utilization of Raman spectroscopy for diagnostic purposes, food safety and environmental issues is discussed under a critical view.

Label-Free in Situ Discrimination of Live and Dead Bacteria by Surface-Enhanced Raman Scattering

Techniques to distinguish between live and dead bacteria in a quantitative manner are in high demand in numerous fields including medical care, food safety, and public security as well as basic science research. This work demonstrates new nanostructures (silver nanoparticles coating bacteria structure, Bacteria@AgNPs) and their utility for rapid counting of live and dead bacteria by surface-enhanced Raman scattering (SERS). We found that suspensions containing Gram-negative organisms as well as AgNPs give strong SERS signals of live bacteria when generated selectively on the particle surface. However, almost no SERS signals can be detected from Bacteria@AgNPs suspensions containing dead bacteria. We demonstrate successful quantification of different percentages of dead bacteria both in bulk liquid and on glass surfaces by using SERS mapping on a single cell basis. Furthermore, different chemicals have been used to elucidate the mechanism involved in this observation. Finally, we used the Bacteria@AgNPs method to detect antibiotic resistance of E. coli strains against several antibiotics used in human medicine.

Evaluation of antibiotic effects on Pseudomonas aeruginosa biofilm using Raman spectroscopy and multivariate analysis

We investigate the mode of action and classification of antibiotic agents (ceftazidime, patulin, and epigallocatechin gallate; EGCG) on Pseudomonas aeruginosa (P. aeruginosa) biofilm using Raman spectroscopy with multivariate analysis, including support vector machine (SVM) and principal component analysis (PCA). This method allows for quantitative, label-free, non-invasive and rapid monitoring of biochemical changes in complex biofilm matrices with high sensitivity and specificity. In this study, the biofilms were grown and treated with various agents in the microfluidic device, and then transferred onto gold-coated substrates for Raman measurement. Here, we show changes in biochemical properties, and this technology can be used to distinguish between changes induced in P. aeruginosa biofilms using three antibiotic agents. The Raman band intensities associated with DNA and proteins were decreased, compared to control biofilms, when the biofilms were treated with antibiotics. Unlike with exposure to ceftazidime and patulin, the Raman spectrum of biofilms exposed to EGCG showed a shift in the spectral position of the CH deformation stretch band from 1313 cm(-1) to 1333 cm(-1), and there was no difference in the band intensity at 1530 cm(-1) (C = C stretching, carotenoids). The PCA-SVM analysis results show that antibiotic-treated biofilms can be detected with high sensitivity of 93.33%, a specificity of 100% and an accuracy of 98.33%. This method also discriminated the three antibiotic agents based on the cellular biochemical and structural changes induced by antibiotics with high sensitivity and specificity of 100%. This study suggests that Raman spectroscopy with PCA-SVM is potentially useful for the rapid identification and classification of clinically-relevant antibiotics of bacteria biofilm. Furthermore, this method could be a powerful approach for the development and screening of new antibiotics.

Identification of mycobacteria based on spectroscopic analyses of mycolic acid profiles

This report examines lipophilic extracts containing mycolic acids isolated from tuberculosis (MTB) and non-tuberculosis (NTM) mycobacterial strains using chromatography, mass spectrometry (MS), nuclear magnetic resonance (NMR), and Raman spectroscopy. Gas chromatography-MS was used to identify major fatty acid mycolate components, while proton NMR confirmed the presence of characteristic cis- and trans-cyclopropane rings within different mycolic acid sub-types. Surface-enhanced Raman (SERS) spectra were obtained from the mycolic acids extracted from the bacterial cell envelopes of the MTB or NTM mycobacterial species. The Raman spectral profiles were used to develop a classification method based on chemometrics for identification of the mycobacterial species. Multivariate statistical analysis methods, including principal component analysis (PCA), hierarchical cluster analysis (HCA), and partial least squares discriminant analysis (PLS-DA) of the SERS spectra enabled differentiation of NTM mycobacteria from one another with 100% accuracy. These methods are also sensitive enough to differentiate clinically-isolated MTB strains that differed only by the presence or absence of a single extracytoplasmic sigma factor with 83-100% sensitivity and 80-100% specificity. The current work is the first report on discrimination of mycobacteria strains based on the SERS spectra of the constituent mycolic acids in lipophilic extracts. These results suggest that SERS can be used as an accurate and sensitive method for species and strain discrimination in mycobacteria.

Imaging of Chlamydia and host cell metabolism

ABSTRACT: 

Chlamydial infections cause a wide range of acute and chronic diseases. Chlamydia trachomatis is the most common sexually transmitted bacterium while Chlamydia pneumoniae causes infections of the upper and lower respiratory tract. Chlamydia are obligate, intracellular bacteria with a biphasic developmental cycle that involves unique metabolic changes. Aside from entering an actively replicating state, Chlamydia may also implement persistent infections depending on different microenvironmental factors. In addition, changes in local oxygen availability and the composition of surrounding host microbiota are suggested to affect chlamydial growth and metabolism. Both bacteria and host cells endure characteristic metabolic changes during infection. Technical developments in recent years enable us to separately characterize chlamydial and host cell metabolism in living cells. This article focuses on novel approaches to analyze chlamydial metabolism such as NAD(P)H fluorescence lifetime imaging by two-photon microscopy. In addition, we provide an overview regarding promising future possibilities to further elucidate host–pathogen metabolic interactions.

Raman spectrometric discrimination of flexirubin pigments from two genera of Bacteroidetes

Flexirubins are specific polyene pigments produced by several genera of Bacteroidetes. Colonies and cell extracts of Flavobacterium johnsoniae and Flexibacter elegans have been investigated by Raman spectroscopy to show that this fast and non-destructive technique can be used to differentiate these pigments from carotenoids and to compare the flexirubin content of the two microorganisms. The presence or absence of certain distinguishing features in the CH combination band region at 2500-2750 cm(-1) can assist in the discrimination between the two flexirubins investigated. Raman spectroscopy is thus a suitable tool not only to detect flexirubin pigments in bacterial cells, but also to further characterize the pigments present in members of the Bacteroidetes genera that are rich in flexirubins.

Raman spectroscopy provides a powerful, rapid diagnostic tool for the detection of tuberculous meningitis in ex vivo cerebrospinal fluid samples

In this letter, we propose a novel method for diagnosis of tuberculous meningitis using Raman spectroscopy. The silicate Raman signature obtained from Mycobacterium tuberculosis positive cases enables specific and sensitive detection of tuberculous meningitis from acquired cerebrospinal fluid samples. The association of silicates with the tuberculosis mycobacterium is discussed. We envision that this new method will facilitate rapid diagnosis of tuberculous meningitis without application of exogenous reagents or dyes and can be aptly used as a complementary screening tool to the existing gold standard methods.

Detecting and tracking nosocomial methicillin-resistant Staphylococcus aureus using a microfluidic SERS biosensor

Rapid detection and differentiation of methicillin-resistant Staphylococcus aureus (MRSA) are critical for the early diagnosis of difficult-to-treat nosocomial and community acquired clinical infections and improved epidemiological surveillance. We developed a microfluidics chip coupled with surface enhanced Raman scattering (SERS) spectroscopy (532 nm) "lab-on-a-chip" system to rapidly detect and differentiate methicillin-sensitive S. aureus (MSSA) and MRSA using clinical isolates from China and the United States. A total of 21 MSSA isolates and 37 MRSA isolates recovered from infected humans were first analyzed by using polymerase chain reaction (PCR) and multilocus sequence typing (MLST). The mecA gene, which refers resistant to methicillin, was detected in all the MRSA isolates, and different allelic profiles were identified assigning isolates as either previously identified or novel clones. A total of 17 400 SERS spectra of the 58 S. aureus isolates were collected within 3.5 h using this optofluidic platform. Intra- and interlaboratory spectral reproducibility yielded a differentiation index value of 3.43-4.06 and demonstrated the feasibility of using this optofluidic system at different laboratories for bacterial identification. A global SERS-based dendrogram model for MRSA and MSSA identification and differentiation to the strain level was established and cross-validated (Simpson index of diversity of 0.989) and had an average recognition rate of 95% for S. aureus isolates associated with a recent outbreak in China. SERS typing correlated well with MLST indicating that it has high sensitivity and selectivity and would be suitable for determining the origin and possible spread of MRSA. A SERS-based partial least-squares regression model could quantify the actual concentration of a specific MRSA isolate in a bacterial mixture at levels from 5% to 100% (regression coefficient, >0.98; residual prediction deviation, >10.05). This optofluidic platform has advantages over traditional genotyping for ultrafast, automated, and reliable detection and epidemiological surveillance of bacterial infections.

Point-of-care assays for tuberculosis: role of nanotechnology/microfluidics

Tuberculosis (TB) remains one of the most devastating infectious diseases and its eradication is still unattainable given the limitations of current technologies for diagnosis, treatment and prevention. The World Health Organization's goal to eliminate TB globally by 2050 remains an ongoing challenge as delayed diagnosis and misdiagnosis of TB continue to fuel the worldwide epidemic. Despite considerable improvements in diagnostics for the last few decades, a simple and effective point-of-care TB diagnostic test is yet not available. Here, we review the current assays used for TB diagnosis, and highlight the recent advances in nanotechnology and microfluidics that potentially enable new approaches for TB diagnosis in resource-constrained settings.

Evidence for phenotypic plasticity among multihost Campylobacter jejuni and C. coli lineages, obtained using ribosomal multilocus sequence typing and Raman spectroscopy

Closely related bacterial isolates can display divergent phenotypes. This can limit the usefulness of phylogenetic studies for understanding bacterial ecology and evolution. Here, we compare phenotyping based on Raman spectrometric analysis of cellular composition to phylogenetic classification by ribosomal multilocus sequence typing (rMLST) in 108 isolates of the zoonotic pathogens Campylobacter jejuni and C. coli. Automatic relevance determination (ARD) was used to identify informative peaks in the Raman spectra that could be used to distinguish strains in taxonomic and host source groups (species, clade, clonal complex, and isolate source/host). Phenotypic characterization based on Raman spectra showed a degree of agreement with genotypic classification using rMLST, with segregation accuracy between species (83.95%), clade (in C. coli, 98.41%), and, to some extent, clonal complex (86.89% C. jejuni ST-21 and ST-45 complexes) being achieved. This confirmed the utility of Raman spectroscopy for lineage classification and the correlation between genotypic and phenotypic classification. In parallel analysis, relatively distantly related isolates (different clonal complexes) were assigned the correct host origin irrespective of the clonal origin (74.07 to 96.97% accuracy) based upon different Raman peaks. This suggests that the phenotypic characteristics, from which the phenotypic signal is derived, are not fixed by clonal descent but are influenced by the host environment and change as strains move between hosts.

Spatially resolved investigation of the oil composition in single intact hyphae of Mortierella spp. with micro-Raman spectroscopy

Zygomycetes are well known for their ability to produce various secondary metabolites. Fungi of the genus Mortierella can accumulate highly unsaturated lipids in large amounts as lipid droplets. However, no information about the spatial distribution or homogeneity of the oil inside the fungi is obtainable to date due to the invasive and destructive analytical techniques applied so far. Raman spectroscopy has been demonstrated to be well suited to investigate biological samples on a micrometre scale. It also has been shown that the degree of unsaturation of lipids can be determined from Raman spectra. We applied micro-Raman spectroscopy to investigate the spatial distribution and composition of lipid vesicles inside intact hyphae. For Mortierella alpina and Mortierella elongata distinct differences in the degree of unsaturation and even the impact of growth conditions are determined from the Raman spectra. In both species we found that the fatty acid saturation in the vesicles is highly variable in the first 600 μm of the growing hyphal tip and fluctuates towards a constant composition and saturation ratio in all of the remaining mycelium. Our approach facilitates in vivo monitoring of the lipid production and allows us to investigate the impact of cultivation parameters on the oil composition directly in the growing hyphae without the need for extensive extraction procedures.

Clinical manifestations, diagnosis, and treatment of Mycobacterium haemophilum infections

Mycobacterium haemophilum is a slowly growing acid-fast bacillus (AFB) belonging to the group of nontuberculous mycobacteria (NTM) frequently found in environmental habitats, which can colonize and occasionally infect humans and animals. Several findings suggest that water reservoirs are a likely source of M. haemophilum infections. M. haemophilum causes mainly ulcerating skin infections and arthritis in persons who are severely immunocompromised. Disseminated and pulmonary infections occasionally occur. The second at-risk group is otherwise healthy children, who typically develop cervical and perihilar lymphadenitis. A full diagnostic regimen for the optimal detection of M. haemophilum includes acid-fast staining, culturing at two temperatures with iron-supplemented media, and molecular detection. The most preferable molecular assay is a real-time PCR targeting an M. haemophilum-specific internal transcribed spacer (ITS), but another approach is the application of a generic PCR for a mycobacterium-specific fragment with subsequent sequencing to identify M. haemophilum. No standard treatment guidelines are available, but published literature agrees that immunocompromised patients should be treated with multiple antibiotics, tailored to the disease presentation and underlying degree of immune suppression. The outcome of M. haemophilum cervicofacial lymphadenitis in immunocompetent patients favors surgical intervention rather than antibiotic treatment.