Sensitive and specific discrimination of pathogenic and nonpathogenic Escherichia coli using Raman spectroscopy-a comparison of two multivariate analysis techniques

Specific discrimination of pathogenic bacteria causing septic arthritis using Raman spectroscopy: In-vitro study

In this study we performed preliminary experiments using Raman spectroscopy as an evolving technology in biofluid and microbial characterization, to explore its potential for rapid diagnosis of pathogenic bacteria in an in-vitro synovial fluid infection model. Normal human synovial fluids samples were collected from patients undergoing knee surgery and the three most common pathogenic bacteria introduced in-vitro into the samples. The bacterial growth was systematically monitored using a Raman spectroscopy. Multivariate regression analysis of acquired spectra showed bacterial characteristic Raman bands related to bacterial cell membranes and DNA structures to increase continuously as the incubation period was increased. Spectra signature recorded from cultured synovial fluid samples showed a significant loss in synovial quality and protein morphology over time compared to control samples. In this study, Raman spectroscopy shows promise for rapid pathogenic bacteria identification in synovial fluid. Marker peaks distinguished inoculated bacteria, while chemical changes reveal infection dynamics.

Surface-enhanced Raman spectroscopy for characterization of supernatant samples of biofilm forming bacterial strains

Staphylococcus epidermidis is considered major cause of nosocomial infections. Its pathogenicity is mainly due to the ability to form biofilms on different surfaces, particularly indwelling medical devices. This bacterium consists of different strains consisting of non, medium and strong biofilm forming ones. Surface-enhanced Raman spectroscopy (SERS) is a powerful analytical technique that can be used to detect and analyze biochemical composition of the supernatant samples of different strains of bacteria including non, medium and strong biofilm forming bacterial strains. SERS is a powerful technique for the robust, reliable, rapid detection and discrimination of bacteria in the form of characteristic SERS spectral features which can be used for detection and classification. SERS is used to differentiate three classes of bacteria with respect to their biofilm forming ability. Silver nanoparticles (Ag NPs) are used as SERS substrate and synthesized with chemical reduction method. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are used to discriminate SERS spectral data sets of non, medium and strong biofilm forming bacteria. PLS-DA analysis is a multivariate statistical technique that can be used to analyze data from bacterial sets.

Fast Detection of Different Water Contaminants by Raman Spectroscopy and Surface-Enhanced Raman Spectroscopy

Fast monitoring of water quality is a fundamental part of environmental management and protection, in particular, the possibility of qualitatively and quantitatively determining its contamination at levels that are dangerous for human health, fauna and flora. Among the techniques currently available, Raman spectroscopy and its variant, Surface-Enhanced Raman Spectroscopy (SERS), have several advantages, including no need for sample preparation, quick and easy operation and the ability to operate on the field. This article describes the application of the Raman and SERS technique to liquid samples contaminated with different classes of substances, including nitrates, phosphates, pesticides and their metabolites. The technique was also used for the detection of the air pollutant polycyclic aromatic hydrocarbons and, in particular, benzo(a)pyrene, considered as a reference for the carcinogenicity of the whole class of these compounds. To pre-concentrate the analytes, we applied a methodology based on the well-known coffee-ring effect, which ensures preconcentration of the analytes without any pretreatment of the sample, providing a versatile approach for fast and in-situ detection of water pollutants. The obtained results allowed us to reveal these analytes at low concentrations, close to or lower than their regulatory limits.

Characterization of siderophores from Escherichia coli strains through genome mining tools: an antiSMASH study

Although urinary tract infections (UTIs) affect many people, they are usually a disease observed in women. UTIs happen when exogenous and endogenous bacteria enter the urinary tract and colonize there. Cystitis and pyelonephritis occur when bacteria infect the bladder and the kidneys, respectively. UTIs become much serious if the bacteria causing the infection are antibiotic resistant. Since the pathogenic microorganisms have been adopted to current antibiotics via genetic variations, UTIs have become an even more severe health problem. Therefore, there is a great need for the discovery of novel antibiotics. Genome mining of nonpathogenic and pathogenic Escherichia coli strains for investigating secondary metabolites were conducted by the antiSMASH analysis. When the resulting secondary metabolites were examined, it was found that some of the siderophores are effective in UTIs. In conclusion, since the siderophore production in E. coli is directly related to UTIs, these molecules can be a good target for development of future pharmaceutical approaches and compounds. Siderophores can also be used in industrial studies due to their higher chelating affinity for iron.

Genome mining on nonpathogenic and pathogenic E. coli was studied.

Comprehensive and comparative analysis of siderophores were investigated.

The results may open a new gate on the development of new drugs on pathogenic E. coli-based diseases.

High-Speed Diagnosis of Bacterial Pathogens at the Single Cell Level by Raman Microspectroscopy with Machine Learning Filters and Denoising Autoencoders

Accurate and rapid identification of infectious bacteria is important in medicine. Raman microspectroscopy holds great promise in performing label-free identification at the single-cell level. However, due to the naturally weak Raman signal, it is a challenge to build extensive databases and achieve both accurate and fast identification. Here, we used signal-to-noise ratio (SNR) as a standard indicator for Raman data quality and performed bacterial identification using 11, 141 single-cell Raman spectra from nine bacterial strains. Subsequently, using two machine learning methods, a simple filter, and a neural network-based denoising autoencoder (DAE), we demonstrated 92% (simple filter using 1 s/cell spectra) and 84% (DAE using 0.1 s/cell spectra) identification accuracy. Our machine learning-aided Raman analysis paves the way for high-speed Raman microspectroscopic clinical diagnostics.

Investigating the Growth of Pseudomonas aeruginosa and Its Influence on Osteolysis in Human Bone: An In Vitro Study

Background

Isolation of the causal microorganisms in osteomyelitis presents a major challenge for treating clinicians. Several methods have been proposed to rapidly and accurately identify microorganisms. There has been an increasing interest in using Raman spectroscopy in the field of microbial detection and characterisation. This paper explores the use of Raman spectroscopy identification as one of the most difficult-to-isolate microorganisms causing osteomyelitis.

Methods and results

Fresh healthy human bone samples were collected from patients undergoing a total knee replacement. These samples were then inoculated with fresh overnight Pseudomonas aeruginosa (PAO) cultures. Bacteria growth and bone ultrastructural changes were monitored over a period of 6 weeks. The experiment demonstrated ultrastructural bony destruction caused by osteolytic PAO secretions. Raman-specific spectral signatures related to the cellular membranes of PAO structures were spotted indicating survival of bacteria on the bone surface.

Conclusion

This study showed the promising ability of Raman spectroscopy to identify the presence of bacteria on the surface of inoculated bone samples over time. It was able to detect the osteolytic activity of the bacteria as well as ultrastructure specific to PAO virulence. This method may have a role as an aid to existing diagnostic methods for fast and accurate bacterial identification in bone infection.

How to cite this article

Al Ghaithi A, Al Bimani A, Al Maskari S. Investigating the Growth of Pseudomonas aeruginosa and Its Influence on Osteolysis in Human Bone: An In Vitro Study. Strategies Trauma Limb Reconstr 2021;16(3):127–131.

Hypothesis of macroscopic quantum state in biological systems and discussion on the possibility of its experimental verification

Earlier it was noted that the functioning of biological systems is accompanied by a very low level of energy dissipation, and it was assumed that a physical mechanism similar to that which works in superconductivity can operate here. The paper proposes a hypothesis that the phenomenon of life is not based on superconductivity, but on some so far unexplored macroscopic quantum state of organic structures making up the cell. It is assumed that this state is also characterized by the presence of an energy gap in the electronic spectrum, which makes the state stable and provides a low level of energy dissipation. The possibility of using optical spectroscopy methods for identifying the energy gap in biological objects is analyzed. It is assumed that the virus is alive inside the host cell, but not alive outside the host cell. It is proposed to use Raman spectroscopy of the process of bacterial infection with phages to search for the energy gap. This should confirm or refute the main hypothesis, as well as provide an opportunity to answer the question: "Are viruses alive?"

Surface-enhanced Raman spectroscopy for the identification of tigecycline-resistant E. coli strains

Tigecycline (TGC) is recognised as last resort of drugs against several antibiotic-resistant bacteria. Bacterial resistance to tigecycline due to presence of plasmid-mediated mobile TGC resistance genes (tet X3/X4) has broken another defense line. Therefore, rapid and reproducible detection of tigecycline-resistant E. coli (TREC) is required. The current study is designed for the identification and differentiation of TREC from tigecycline-sensitive E. coli (TSEC) by employing SERS by using Ag NPs as a SERS substrate. The SERS spectral fingerprints of E. coli strains associated directly or indirectly with the development of resistance against tigecycline have been distinguished by comparing SERS spectral data of TSEC strains with each TREC strain. Moreover, the statistical analysis including Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were employed to check the diagnostic potential of SERS for the differentiation among TREC and TSEC strains. The qualitative identification and differentiation between resistant and sensitive strains and among individual strains have been efficiently done by performing both PCA and HCA. The successful discrimination among TREC and TSEC at the strain level is performed by PLS-DA with 98% area under ROC curve, 100% sensitivity, 98.7% specificity and 100% accuracy.

Rapid and sensitive discrimination among carbapenem resistant and susceptible E. coli strains using Surface Enhanced Raman Spectroscopy combined with chemometric tools

BACKGROUND

Raman spectroscopy is a powerful technique for the robust, reliable and rapid detection and discrimination of bacteria.

OBJECTIVES

To develop a rapid and sensitive technique based on surface-enhanced Raman spectroscopy (SERS) with multivariate data analysis tools for discrimination among carbapenem resistant and susceptible E. coli strains.

METHODS

SERS was employed to differentiate different strains of carbapenem resistant and susceptible E. coli by using silver nanoparticles (Ag NPs) as a SERS substrate. For this purpose, four strains of carbapenem resistant and three strains of carbapenem susceptible E. coli were analyzed by comparing their SERS spectral signatures. Furthermore, multivariate data analysis techniques including Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were performed over the spectral range of 400-1800 cm^-1 (fingerprint region) for the identification and differentiation of different E. coli strains.

RESULTS

The SERS spectral features associated with resistant development against carbapenem antibiotics were separated by comparing each spectrum of susceptible strains with each resistant strain. PCA and HCA were found effective for the qualitative differentiation of all the strains analysed. PLS-DA successfully discriminated the carbapenem resistant and susceptible E. coli pellets on the strain level with 99.8 % sensitivity, 100 % specificity, 100 % accuracy and 86 % area under receiver operating characteristic (AUROC) curve.

CONCLUSION

SERS can be employed for the rapid discrimination among carbapenem resistant and susceptible strains of E. coil.

A Dual Immunological Raman-Enabled Crosschecking Test (DIRECT) for Detection of Bacteria in Low Moisture Food

Among the physical, chemical and biological hazards that could arise with respect to food safety, bacterial contamination has been one of the main concerns in recent years. Bacterial contamination in low moisture foods (LMFs) was an emerging threat which used to draw less attention as LMFs were considered at low risk of such a hazard. Bacteria can survive in low moisture environments and cause foodborne diseases once they enter the digestive system. Common detection methods such as ELISA and PCR are not well suited to LMFs, as most of them operate under aqueous environments. In this study, a Dual Immunological Raman-Enabled Crosschecking Test (DIRECT) was developed for LMFs using a nano-scaled surface enhanced Raman scattering (SERS) biosensor platform and multivariate discriminant analysis with a portable Raman spectrometer. It could provide a limit of detection (LOD) of 102 CFU/g of bacteria in model LMFs, with a detection time of 30-45 min. It has the potential to become a quick screening method for on-site bacteria detection for LMFs to identify food safety risks in real time.

Extracellular Vesicle Identification Using Label-Free Surface-Enhanced Raman Spectroscopy: Detection and Signal Analysis Strategies

Extracellular vesicles (EVs) have been widely investigated as promising biomarkers for the liquid biopsy of diseases, owing to their countless roles in biological systems. Furthermore, with the notable progress of exosome research, the use of label-free surface-enhanced Raman spectroscopy (SERS) to identify and distinguish disease-related EVs has emerged. Even in the absence of specific markers for disease-related EVs, label-free SERS enables the identification of unique patterns of disease-related EVs through their molecular fingerprints. In this review, we describe label-free SERS approaches for disease-related EV pattern identification in terms of substrate design and signal analysis strategies. We first describe the general characteristics of EVs and their SERS signals. We then present recent works on applied plasmonic nanostructures to sensitively detect EVs and notable methods to interpret complex spectral data. This review also discusses current challenges and future prospects of label-free SERS-based disease-related EV pattern identification.

Klebsiella pneumoniae: Prevalence, Reservoirs, Antimicrobial Resistance, Pathogenicity, and Infection: A Hitherto Unrecognized Zoonotic Bacterium

Klebsiella pneumoniae is considered an opportunistic pathogen, constituting an ongoing health concern for immunocompromised patients, the elderly, and neonates. Reports on the isolation of K. pneumoniae from other sources are increasing, many of which express multidrug-resistant (MDR) phenotypes. Three phylogroups were identified based on nucleotide differences. Niche environments, including plants, animals, and humans appear to be colonized by different phylogroups, among which KpI (K. pneumoniae) is commonly associated with human infection. Infections with K. pneumoniae can be transmitted through contaminated food or water and can be associated with community-acquired infections or between persons and animals involved in hospital-acquired infections. Increasing reports are describing detections along the food chain, suggesting the possibility exists that this could be a hitherto unexplored reservoir for this opportunistic bacterial pathogen. Expression of MDR phenotypes elaborated by these bacteria is due to the nature of various plasmids carrying antimicrobial resistance (AMR)-encoding genes, and is a challenge to animal, environmental, and human health alike. Raman spectroscopy has the potential to provide for the rapid identification and screening of antimicrobial susceptibility of Klebsiella isolates. Moreover, hypervirulent isolates linked with extraintestinal infections express phenotypes that may support their niche adaptation. In this review, the prevalence, reservoirs, AMR, Raman spectroscopy detection, and pathogenicity of K. pneumoniae are summarized and various extraintestinal infection pathways are further narrated to extend our understanding of its adaptation and survival ability in reservoirs, and associated disease risks.

Discrimination between pathogenic and non-pathogenic E. coli strains by means of Raman microspectroscopy

Bacteria can be harmless commensals, beneficial probiotics, or harmful pathogens. Therefore, mankind is challenged to detect and identify bacteria in order to prevent or treat bacterial infections. Examples are identification of species for treatment of infection in clinics and E. coli cell counting for water quality monitoring. Finally, in some instances, the pathogenicity of a species is of interest. The main strategies to investigate pathogenicity are detection of target genes which encode virulence factors. Another strategy could be based on phenotypic identification. Raman spectroscopy is a promising phenotypic method, which offers high sensitivities and specificities for the identification of bacteria species. In this study, we evaluated whether Raman microspectroscopy could be used to determine the pathogenicity of E. coli strains. We used Raman spectra of seven non-pathogenic and seven pathogenic E. coli strains to train a PCA-SVM model. Then, the obtained model was tested by identifying the pathogenicity of three additional E. coli strains. The pathogenicity of these three strains could be correctly identified with a mean sensitivity of 77%, which is suitable for a fast screening of pathogenicity of single bacterial cells.

Biochemical characterization of pathogenic bacterial species using Raman spectroscopy and discrimination model based on selected spectral features

This study aimed to evaluate the differences in the Raman spectra of nine clinical species of bacteria isolated from infections (three Gram-positive and six Gram-negative species), correlating the spectra with the chemical composition of each species and to develop a classification model through discriminant analysis to categorize each bacterial strain using the peaks with the most significant differences. Bacteria were cultured in Mueller Hinton agar and a sample of biomass was harvested and placed in an aluminum sample holder. A total of 475 spectra from 115 different strains were obtained through a dispersive Raman spectrometer (830 nm) with exposure time of 50 s. The intensities of the peaks were evaluated by one-way analysis of variance (ANOVA) and the peaks with significant differences were related to the differences in the biochemical composition of the strains. Discriminant analysis based on quadratic distance applied to the peaks with the most significant differences and partial least squares applied to the whole spectrum showed 89.5% and 90.1% of global accuracy, respectively, for classification of the spectra in all the groups. Raman spectroscopy could be a promising technique to identify spectral differences related to the biochemical content of pathogenic microorganisms and to provide a faster diagnosis of infectious diseases.

Pathogenic Escherichia coli (E. coli) detection through tuned nanoparticles enhancement study

OBJECTIVE

This study aims to detect pathogenic Escherichia coli (E. coli) bacteria using non-destructive fluorescence microscopy and micro-Raman spectroscopy.

RESULTS

Raman vibrational spectroscopy provides additional information regarding biochemical changes at the cellular level. We have used two nanomaterials zinc oxide nanoparticles (ZnO-NPs) and gold nanoparticles (Au-NPs) to detect pathogenic E. coli. The scanning electron microscope (SEM) with energy dispersive X-ray (EDAX) spectroscopy exhibit surface morphology and the elemental composition of the synthesized NPs. The metal NPs are useful contrast agents due to the surface plasmon resonance (SPR) to detect the signal intensity and hence the bacterial cells. The changes due to the interaction between cells and NPs are further correlated to the change in the surface charge and stiffness of the cell surface with the help of the fluorescence microscopic assay.

CONCLUSIONS

We conclude that when two E. coli strains (MTCC723 and MTCC443) and NPs are respectively mixed and kept overnight, the growth of bacteria are inhibited by ZnO-NPs due to changes in cell membrane permeability and intracellular metabolic system under fluorescence microscopy. However, SPR possessed Au-NPs result in enhanced fluorescence of both pathogens. In addition, with the help of Raman microscopy and element analysis, significant changes are observed when Au-NPs are added with the two strains as compared to ZnO-NPs due to protein, lipid and DNA/RNA induced conformational changes.

An Improved Background-Correction Algorithm for Raman Spectroscopy Based on the Wavelet Transform

In the traditional background correction algorithm based on the wavelet transform, approximation coefficients considered as frequency responses of background signal are usually set to zero. However, there are many meaningless negative values generated in the background-corrected spectrum because of the calibration errors of this algorithm. Intensities of some weak peaks even become negative and these peaks will disappear after the calibration of negative values. To solve these problems for the background correction of Raman spectrum, an improved intelligent algorithm which utilizes a suppression coefficient to modify approximation coefficients is proposed in this paper. A series of simulation analyses, as well as experimental investigations, are made to test the performance of this algorithm. It is proved that the use of the suppression coefficient could increase the background correction accuracy and decrease the number of meaningless negative values in the reconstructed spectra, which will prevent the disappearance of weak Raman peaks after the calibration of negative values and increase the sensitivity of Raman spectral analysis.

Plasmonic Nanotrough Networks for Scalable Bacterial Raman Biosensing

We demonstrate a novel approach for fabricating surface enhanced Raman scattering (SERS) substrates for single bacterial biosensing based on Ag cylindrical nanotrough networks (CNNs). This approach is developed with large scalability by leveraging a cellulose nanofiber template fabrication via facile electrospinning. Specifically, a concave nanotrough structure consisting of interconnected concave Ag nanoshells is demonstrated by depositing a thin layer of Ag atop a sacrificial electrospun nanofiber template and then completely removing the cellulose core in water. Our investigations of the scattering properties and SERS performances of single isolated Ag nanotroughs of different diameters reveal that nanotrough-based substrates provide tunable optical responses and enhanced SERS intensities. Further, Ag CNNs are fabricated in highly interconnected networks that yield reproducible SERS signals for molecular monolayers and whole bacterial cells, enabling rapid spectral discrimination between different bacterial strains. Finally, by performing principal component analysis on a large number of measured SERS spectra (40 spectra per bacterium), we demonstrate successful spectral discrimination between two types of Escherichia coli ( E. coli) bacteria, that is, E. coli K12 with its derivative E. coli DH 5α and E. coli BL21(DE3). The demonstrated cost-effective substrates feature several advantages over conventional SERS substrates including environmentally friendly and scalable fabrication compatible with versatile devices and provide an alternative approach to rapid SERS detection and screening of biochemicals.

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

To be able to predict antibiotic resistance in bacteria from fast label-free microscopic observations would benefit a broad range of applications in the biological and biomedical fields. Here, we demonstrate the utility of label-free Raman spectroscopy in monitoring the type of resistance and the mode of action of acquired resistance in a bacterial population of Escherichia coli, in the absence of antibiotics. Our findings are reproducible. Moreover, we identified spectral regions that best predicted the modes of action and explored whether the Raman signatures could be linked to the genetic basis of acquired resistance. Spectral peak intensities significantly correlated (False Discovery Rate, p < 0.05) with the gene expression of some genes contributing to antibiotic resistance genes. These results suggest that the acquisition of antibiotic resistance leads to broad metabolic effects reflected through Raman spectral signatures and gene expression changes, hinting at a possible relation between these two layers of complementary information.

Techniques for characterizing the mode of action of antibiotic resistance are crucial for developing new antimicrobial drugs. Arno Germond et al. have used Raman spectroscopy combined with gene expression to investigate large metabolic changes that occur when bacteria acquire antibiotic resistance.

Detection of molecular changes induced by antibiotics in Escherichia coli using vibrational spectroscopy

This study aimed to test Raman (400-1800cm^-1) and Infra-red (1900-500cm^-1) spectroscopies followed by statistical analysis (principal component analysis) to detect molecular changes induced by antibiotics (ampicillin, cefotaxime - cell wall synthesis inhibitors, tetracycline - protein synthesis inhibitor, ciprofloxacin - DNA synthesis inhibitor) against Escherichia coli TOP10. In case of ampicillin and cefotaxime, a decrease in protein bands in both Raman (1240, 1660cm^-1), and IR spectra (1230, 1530, 1630cm^-1), and an increase in carbohydrate bands (1150, 1020cm^-1) in IR spectra were observed. Tetracycline addition caused an increase in nucleic acid bands (775, 1478, 1578cm^-1), a sharp decrease in phenylalanine (995cm^-1) in Raman spectra and the amide I and amide II bands (1630, 1530cm^-1) in IR spectra, an increase in DNA in both Raman (1083cm^-1) and IR spectra (1080cm^-1). Regarding ciprofloxacin, an increase in nucleic acids (775, 1478, 1578cm^-1) in Raman spectra and in protein bands (1230, 1520, 1630cm^-1), in DNA (1080cm^-1) in IR spectra were detected. Clear discrimination of antibiotic-treated samples compared to the control was recorded, showing that Raman and IR spectroscopies, coupled to principal component analysis for data, could be used to detect molecular modifications in bacteria exposed to different classes of antibiotics. These findings contribute to the understanding of the mechanisms of action of antibiotics in bacteria.

Rapid label-free identification of Klebsiella pneumoniae antibiotic resistant strains by the drop-coating deposition surface-enhanced Raman scattering method

Although many methodologies have been developed to identify unknown bacteria, bacterial identification in clinical microbiology remains a complex and time-consuming procedure. To address this problem, we developed a label-free method for rapidly identifying clinically relevant multilocus sequencing typing-verified quinolone-resistant Klebsiella pneumoniae strains. We also applied the method to identify three strains from colony samples, ATCC70063 (control), ST11 and ST15; these are the prevalent quinolone-resistant K. pneumoniae strains in East Asia. The colonies were identified using a drop-coating deposition surface-enhanced Raman scattering (DCD-SERS) procedure coupled with a multivariate statistical method. Our workflow exhibited an enhancement factor of 11.3×10⁶ to Raman intensities, high reproducibility (relative standard deviation of 7.4%), and a sensitive limit of detection (100 pM rhodamine 6G), with a correlation coefficient of 0.98. All quinolone-resistant K. pneumoniae strains showed similar spectral Raman shifts (high correlations) regardless of bacterial type, as well as different Raman vibrational modes compared to Escherichia coli strains. Our proposed DCD-SERS procedure coupled with the multivariate statistics-based identification method achieved excellent performance in discriminating similar microbes from one another and also in subtyping of K. pneumoniae strains. Therefore, our label-free DCD-SERS procedure coupled with the computational decision supporting method is a potentially useful method for the rapid identification of clinically relevant K. pneumoniae strains.

Wavelength-normalized spectroscopic analysis of Staphylococcus aureus and Pseudomonas aeruginosa growth rates

Optical density (OD) measurements are the standard approach used in microbiology for characterizing bacteria concentrations in culture media. OD is based on measuring the optical absorbance of a sample at a single wavelength, and any error will propagate through all calculations, leading to reproducibility issues. Here, we use the conventional OD technique to measure the growth rates of two different species of bacteria, Pseudomonas aeruginosa and Staphylococcus aureus. The same samples are also analyzed over the entire UV-Vis wavelength spectrum, allowing a distinctly different strategy for data analysis to be performed. Specifically, instead of only analyzing a single wavelength, a multi-wavelength normalization process is implemented. When the OD method is used, the detected signal does not follow the log growth curve. In contrast, the multi-wavelength normalization process minimizes the impact of bacteria byproducts and environmental noise on the signal, thereby accurately quantifying growth rates with high fidelity at low concentrations.

Antimicrobial susceptibility testing of Gram-positive and -negative bacterial isolates directly from spiked blood culture media with Raman spectroscopy

Patients suffering from bacterial bloodstream infections have an increased risk of developing systematic inflammatory response syndrome (SIRS), which can result in rapid deterioration of the patients' health. Diagnostic methods for bacterial identification and antimicrobial susceptibility tests are time-consuming. The aim of this study was to investigate whether Raman spectroscopy would be able to rapidly provide an antimicrobial susceptibility profile from bacteria isolated directly from positive blood cultures. First, bacterial strains (n = 133) were inoculated in tryptic soy broth and incubated in the presence or absence of antibiotics for 5 h. Antimicrobial susceptibility profiles were analyzed by Raman spectroscopy. Subsequently, a selection of strains was isolated from blood cultures and analyzed similarly. VITEK®2 technology and broth dilution were used as the reference methods. Raman spectra from 67 antibiotic-susceptible strains showed discriminatory spectra in the absence or at low concentrations of antibiotics as compared to high antibiotic concentrations. For 66 antibiotic-resistant strains, no antimicrobial effect was observed on the bacterial Raman spectra. Full concordance with VITEK®2 data and broth dilution was obtained for the antibiotic-susceptible strains, 68 % and 98 %, respectively, for the resistant strains. Discriminative antimicrobial susceptibility testing (AST) profiles were obtained for all bacterial strains isolated from blood cultures, resulting in full concordance with the VITEK®2 data. It can be concluded that Raman spectroscopy is able to detect the antimicrobial susceptibility of bacterial species isolated from a positive blood culture bottle within 5 h. Although Raman spectroscopy is cheap and rapid, further optimization is required, to fulfill a great promise for future AST profiling technology development.

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.

Pathogenesis of acute hepatopancreatic necrosis disease (AHPND) in shrimp

Acute hepatopancreatic necrosis disease (AHPND), also called early mortality syndrome (EMS), is a recently emergent shrimp bacterial disease that has resulted in substantial economic losses since 2009. AHPND is known to be caused by strains of Vibrio parahaemolyticus that contain a unique virulence plasmid, but the pathology of the disease is still unclear. In this study, we show that AHPND-causing strains of V. parahaemolyticus secrete the plasmid-encoded binary toxin PirAB(vp) into the culture medium. We further determined that, after shrimp were challenged with AHPND-causing bacteria, the bacteria initially colonized the stomach, where they started to produce PirAB(vp) toxin. At the same early time point (6 hpi), PirB(vp) toxin, but not PirA(vp) toxin, was detected in the hepatopancreas, and the characteristic histopathological signs of AHPND, including sloughing of the epithelial cells of the hepatopancreatic tubules, were also seen. Although some previous studies have found that both components of the binary PirAB(vp) toxin are necessary to induce a toxic effect, our present results are consistent with other studies which have suggested that PirB(vp) alone may be sufficient to cause cellular damage. At later time points, the bacteria and PirA(vp) and PirB(vp) toxins were all detected in the hepatopancreas. We also show that Raman spectroscopy "Whole organism fingerprints" were unable to distinguish between AHPND-causing and non-AHPND causing strains. Lastly, by using minimum inhibitory concentrations, we found that both virulent and non-virulent V. parahaemolyticus strains were resistant to several antibiotics, suggesting that the use of antibiotics in shrimp culture should be more strictly regulated.

Spontaneous and coherent anti-Stokes Raman spectroscopy of human gastrocnemius muscle biopsies in CH-stretching region for discrimination of peripheral artery disease

Peripheral Artery Disease (PAD) is a common manifestation of atherosclerosis, characterized by lower leg ischemia and myopathy in association with leg dysfunction. In the present study, Spontaneous and coherent anti-Stokes Raman scattering (CARS) spectroscopic techniques in CH-stretching spectral region were evaluated for discriminating healthy and diseased tissues of human gastrocnemius biopsies of control and PAD patients. Since Raman signatures of the tissues in the fingerprint region are highly complex and CH containing moieties are dense, CH-stretching limited spectral range was used to classify the diseased tissues. A total of 181 Raman spectra from 9 patients and 122 CARS spectra from 12 patients were acquired. Due to the high dimensionality of the data in Raman and CARS measurements, principal component analysis (PCA) was first performed to reduce the dimensionality of the data (6 and 9 principal scores for Raman and CARS, respectively) in the CH-stretching region, followed by a discriminant function analysis (DFA) to classify the samples into different categories based on disease severity. The CH2 and CH3 vibrational signatures were observed in the Raman and CARS spectroscopy. Raman and CARS data in conjunction with PCA-DFA analysis were capable of differentiating healthy and PAD gastrocnemius with an accuracy of 85.6% and 78.7%, respectively.

Raman spectroscopy for physiological investigations of tissues and cells

Raman micro-spectroscopy provides a convenient non-destructive and location-specific means of probing cellular physiology and tissue physiology at sub-micron length scales. By probing the vibrational signature of molecules and molecular groups, the distribution and metabolic products of small molecules that cannot be labeled with fluorescent dyes can be analyzed. This method works well for molecular concentrations in the micro-molar range and has been demonstrated as a valuable tool for monitoring drug-cell interactions. If the small molecule of interest does not contain groups that would allow for a discrimination against cytoplasmic background signals, "labeling" of the molecule by isotope substitution or by incorporating other unique small groups, e.g. alkynes provides a stable signal even for time-lapse imaging such compounds in living cells. In this review we highlight recent progress in assessing the physiology of cells and tissue by Raman spectroscopy and imaging.

Modulated Raman Spectroscopy for Enhanced Cancer Diagnosis at the Cellular Level

Raman spectroscopy is emerging as a promising and novel biophotonics tool for non-invasive, real-time diagnosis of tissue and cell abnormalities. However, the presence of a strong fluorescence background is a key issue that can detract from the use of Raman spectroscopy in routine clinical care. The review summarizes the state-of-the-art methods to remove the fluorescence background and explores recent achievements to address this issue obtained with modulated Raman spectroscopy. This innovative approach can be used to extract the Raman spectral component from the fluorescence background and improve the quality of the Raman signal. We describe the potential of modulated Raman spectroscopy as a rapid, inexpensive and accurate clinical tool to detect the presence of bladder cancer cells. Finally, in a broader context, we show how this approach can greatly enhance the sensitivity of integrated Raman spectroscopy and microfluidic systems, opening new prospects for portable higher throughput Raman cell sorting.

Detection and classification of live and dead Escherichia coli by laser-induced breakdown spectroscopy

A common goal for astrobiology is to detect organic materials that may indicate the presence of life. However, organic materials alone may not be representative of currently living systems. Thus, it would be valuable to have a method with which to determine the health of living materials. Here, we present progress toward this goal by reporting on the application of laser-induced breakdown spectroscopy (LIBS) to study characteristics of live and dead cells using Escherichia coli (E. coli) strain K12 cells as a model organism since its growth and death in the laboratory are well understood. Our goal is to determine whether LIBS, in its femto- and/or nanosecond forms, could ascertain the state of a living organism. E. coli strain K12 cells were grown, collected, and exposed to one of two types of inactivation treatments: autoclaving and sonication. Cells were also kept alive as a control. We found that LIBS yields key information that allows for the discrimination of live and dead E. coli bacteria based on ionic shifts reflective of cell membrane integrity.

A facile method for fabrication of buckled PDMS silver nanorod arrays as active 3D SERS cages for bacterial sensing

We have successfully demonstrated a simple and facile method to increase the SERS signal of bacteria due to the formation of high density hotspots among the AgNRs and the increase in the area for better interaction of bacteria with the metal surface.

Rapid (<5 min) identification of pathogen in human blood by electrokinetic concentration and surface-enhanced Raman spectroscopy

This study reports a novel microfluidic platform for rapid and long-ranged concentration of rare-pathogen from human blood for subsequent on-chip surface-enhanced Raman spectroscopy (SERS) identification/discrimination of bacteria based on their detected fingerprints. Using a hybrid electrokinetic mechanism, bacteria can be concentrated at the stagnation area on the SERS-active roughened electrode, while blood cells were excluded away from this region at the center of concentric circular electrodes. This electrokinetic approach performs isolation and concentration of bacteria in about three minutes; the density factor is increased approximately a thousand fold in a local area of ~5000 μm² from a low bacteria concentration of 5 × 10³ CFU/ml. Besides, three genera of bacteria, S. aureus, E. coli, and P. aeruginosa that are found in most of the isolated infections in bacteremia were successfully identified in less than one minute on-chip without the use of any antibody/chemical immobilization and reaction processes.