Discrimination of natural gas-related bacteria by means of micro-Raman spectroscopy

Methane-oxidizing bacteria (MOB) are a unique group of gram-negative bacteria that are proved to be biological indicator for gas prospecting since they utilize methane as a sole source of carbon and energy. Herein the feasibility of a novel and efficient gas prospecting method using Raman spectroscopy is studied. Confocal Raman spectroscopy is utilized to establish a Raman database of 11 species of methanotrophs and other closely related bacteria with similar morphology that generally coexist in the upper soil of natural gas. After strict and consistent spectral preprocessing, Raman spectra from the whole cell area are analyzed using the combination of principal component analysis (PCA) and Mahalanobis distance (MD) that allow unambiguous classification of the different cell types with an accuracy of 95.91%. The discrimination model based on multivariate analysis is further evaluated by classifying Raman spectra from independently cultivated bacteria, and achieves an overall accuracy of 94.04% on species level. Our approach using Raman spectroscopy in combination with statistical analysis of various gas reservoirs related bacteria provides rapid distinction that can potentially play a vital role in gas exploration.     

The influence of intracellular storage material on bacterial identification by means of Raman spectroscopy

Previous studies dealing with bacterial identification by means of Raman spectroscopy have demonstrated that micro-Raman is a suitable technique for single-cell microbial identification. Raman spectra yield fingerprint-like information about all chemical components within one cell, and combined with multivariate methods, differentiation down to species or even strain level is possible. Many microorganisms may accumulate high amounts of polyhydroxyalkanoates (PHA) as carbon and energy storage materials within the cell and the Raman bands of PHA might impede the identification and differentiation of cells. To date, the identification by means of Raman spectroscopy have never been tested on bacteria which had accumulated PHA. Therefore, the aim of this study is to investigate the effect of intracellular polymer accumulation on the bacterial identification rate. Combining fluorescence imaging and Raman spectroscopy, we identified polyhydroxybutyrate (PHB) as a storage polymer accumulating in the investigated cells. The amount of energy storage material present within the cells was dependent on the physiological status of the microorganisms and strongly influenced the identification results. Bacteria in the stationary phase formed granules of crystalline PHB, which obstructed the Raman spectroscopic identification of bacterial species. The Raman spectra of bacteria in the exponential phase were dominated by signals from the storage material. However, the bands from proteins, lipids, and nucleic acids were not completely obscured by signals from PHB. Cells growing under either oxic or anoxic conditions could also be differentiated, suggesting that changes in Raman spectra can be interpreted as an indicator of different metabolic pathways. Although the presence of PHB induced severe changes in the Raman spectra, our results suggest that Raman spectroscopy can be successfully used for identification as long as the bacteria are not in the stationary phase.     

Towards a detailed understanding of bacterial metabolism--spectroscopic characterization of Staphylococcus epidermidis.

Bacteria are a major cause of infection. To fight disease and growing resistance, research interest is focused on understanding bacterial metabolism. For a detailed evaluation of the involved mechanisms, a precise knowledge of the molecular composition of the bacteria is required. In this article, various vibrational spectroscopic techniques are applied to comprehensively characterize, on a molecular level, bacteria of the strain Staphylococcus epidermidis, an opportunistic pathogen which has evolved to become a major cause of nosocomial infections. IR absorption spectroscopy reflects the overall chemical composition of the cells, with major focus on the protein vibrations. Smaller sample volumes-down to a single cell-are sufficient to probe the overall chemical composition by means of micro-Raman spectroscopy. The nucleic-acid and aromatic amino-acid moieties are almost exclusively explored by UV resonance Raman spectroscopy. In combination with statistical evaluation methods [hierarchical cluster analysis (HCA), principal component analysis (PCA), linear discriminant analysis (LDA)], the protein and nucleic-acid components that change during the different bacterial growth phases can be identified from the in vivo vibrational spectra. Furthermore, tip-enhanced Raman spectroscopy (TERS) provides insight into the surface structures and follows the dynamics of the polysaccharide and peptide components on the bacterial cells with a spatial resolution below the diffraction limit. This might open new ways for the elucidation of host-bacteria and drug-bacteria interactions.     

Raman spectroscopy applied to the horizontal methods ISO 6579:2002 to identify Salmonella spp. in the food industry

Food safety is a major concern for suppliers in the food chain to ensure the safety of their products. The identification procedure requested by norms is tedious, and it often requires systematic controls and qualified staff to perform the necessary analyses. Raman spectroscopy offers new opportunities to rapidly and efficiently ascertain the presence of pathogens in samples. Nevertheless, this technique requires a standardized procedure to be applied in the industrial context. Our study shows that the variability between spectral fingerprints is related to the physiological state of the microbial species and the growth phase of the bacteria plays a crucial role in its identification by Raman spectroscopy. To improve the discrimination between closely related bacterial species, a procedure based on the selection of bacterial spectra in the exponential growth phase was proposed. Different ways to introduce Raman spectroscopy in the ISO 6579:2002 standards are also proposed from the entire process to a shorter protocol. In the latter case, the identification of bacterial colonies after the selective enrichment step was proposed with the advantages of this path in terms of simplicity and rapidity (analysis time is reduced up to 50 h from the 100 h required by the standard). The protocol validated using six food categories from industrial partners have presented a good correlation by confirmation with other laboratory classical methods. In the future, this procedure could be introduced to the control system of the food production chain with a reliable database for various microorganisms encountered in this field.     

Simultaneous isolation and label‐free identification of bacteria using contactless dielectrophoresis and Raman spectroscopy

The traditional bacterial identification method of growing colonies on agar plates can take several days to weeks to complete depending on the growth rate of the bacteria. Successfully decreasing this analysis time requires cell isolation followed by identification. One way to decrease analysis time is by combining dielectrophoresis (DEP), a common technique used for cell sorting and isolation, and Raman spectroscopy for cell identification. DEP‐Raman devices have been used for bacterial analysis, however, these devices have a number of drawbacks including sample heating, cell‐to‐electrode proximity that limits throughput and separation efficiency, electrode fouling, or inability to address sample debris. Presented here is a contactless DEP‐Raman device to simultaneously isolate and identify particles from a mixed sample while avoiding common drawbacks associated with other DEP designs. Using the device, a mixed sample of bacteria and 3 μm polystyrene spheres were isolated from each other and a Raman spectrum of the trapped bacteria was acquired, indicating the potential for cDEP‐Raman devices to decrease the analysis time of bacteria.     

Chemometric analysis of multiple species of Bacillus bacterial endospores using infrared spectroscopy: Discrimination to the strain level

Previous work using infrared spectroscopy has shown potential for rapid discrimination between bacteria in either their sporulated or vegetative states, as well as between bacteria and other common interferents. For species within one physiological state, however, distinction is far more challenging, and requires chemometrics. In the current study, we have narrowed the field of study by eliminating the confounding issues of vegetative cells as well as growth media and focused on using IR spectra to distinguish only between different species all in the sporulated state. Using principal component analysis (PCA) and a classification method based upon similarity measurements, we demonstrate a successful identification rate to the species level of 85% for Bacillus spores grown and sporulated in a glucose broth medium.     

Surface enhanced Raman spectroscopy (SERS) for the discrimination of Arthrobacter strains based on variations in cell surface composition

Surface enhanced Raman spectroscopy (SERS) is a rapid and highly sensitive spectroscopic technique that has the potential to measure chemical changes in bacterial cell surface in response to environmental changes. The objective of this study was to determine whether SERS had sufficient resolution to differentiate closely related bacteria within a genus grown on solid and liquid medium, and a single Arthrobacter strain grown in multiple chromate concentrations. Fourteen closely related Arthrobacter strains, based on their 16S rRNA gene sequences, were used in this study. After performing principal component analysis in conjunction with Linear Discriminant Analysis, we used a novel, adapted cross-validation method, which more faithfully models the classification of spectra. All fourteen strains could be classified with up to 97% accuracy. The hierarchical trees comparing SERS spectra from the liquid and solid media datasets were different. Additionally, hierarchical trees created from the Raman data were different from those obtained using 16S rRNA gene sequences (a phylogenetic measure). A single bacterial strain grown on solid media culture with three different chromate levels also showed significant spectral distinction at discrete points identified by the new Elastic Net regularized regression method demonstrating the ability of SERS to detect environmentally induced changes in cell surface composition. This study demonstrates that SERS is effective in distinguishing between a large number of very closely related Arthrobacter strains and could be a valuable tool for rapid monitoring and characterization of phenotypic variations in a single population in response to environmental conditions.     

Assessment of two isolation techniques for bacteria in milk towards their compatibility with Raman spectroscopy

The identification of single microorganism in food samples by conventional plating techniques or molecular genetic methods requires a time consuming enrichment step. Raman spectroscopy in combination with a suitable extraction method however offers the possibility to rapidly identify bacteria on a single cell level. Here we evaluate the two well-known bacteria extraction methods from milk: "buoyant density centrifugation" and "enzymatic milk clearing" towards their recovery efficiency and their compatibility with Raman spectroscopy for a rapid identification of microorganisms in milk. The achieved recovery yields are slightly better compared to those which are already applied for food investigations, where a loss of one order of magnitude is usually reached. For example, buoyant density centrifugation allows collecting up to 35% of the milk-spiked microorganisms. To prove the suitability of the isolation techniques for use in combination with the spectroscopic approach, a small Raman database has been created by recording Raman spectra of well-known contaminants in dairy products. Two subspecies of Escherichia coli and three different Pseudomonas species, which were inoculated to UHT (ultra-high-temperature processed) milk and afterwards extracted by the two techniques mentioned above, were analysed. At a first glance, grave spectral artefacts caused by the matrix itself or especially by the extraction techniques were not obvious. But via chemometric analysis, it could be shown that these factors noticeably influence the identification rates: while the samples prepared via milk clearing did not provide sufficient identification results, buoyant density centrifugation allows an identification of the investigated species with an overall accuracy of 91% in combination with linear discriminant analysis.     

Towards a receptor-free immobilization and SERS detection of urinary tract infections causative pathogens

Based on molecular-specific surface-enhanced Raman scattering (SERS) spectroscopy we were able to discriminate between rough and smooth strains of Escherichia coli and Proteus mirabilis bacteria. For this purpose, bacteria have been immobilized through electrostatic forces by inducing a positive charge on the glass slide. This way, SERS spectra on bacterial biomass and also on single bacteria could be recorded in less than 2 h, by using concentrated silver nanoparticles as SERS-active substrate. Single-bacterium SERS spectral fingerprints showed to be sensitive to the presence of the O-antigen at strain level and to the microorganisms growth phase. By using principal component analysis (PCA) on the SERS spectra recorded from E. coli and P. mirabilis, these two uropathogens could be fairly discriminated.     

基于显微共聚焦拉曼光谱技术快速检测3种梭菌中肉毒梭菌

提出了一种基于显微共聚焦拉曼光谱技术的肉毒梭菌快速鉴别方法.利用共聚焦显微拉曼光谱技术(CRM)采集了肉毒梭菌、艰难梭菌和产气荚膜梭菌的拉曼光谱,比较了3种梭菌的平均拉曼光谱,采用基线校正、标准正态变换、Savitzky-Golay 5点平滑和最大最小值归一化预处理后,借助主成分分析(PCA)降维并提取特征变量,对样本...     

Raman spectroscopic identification of single bacterial cells under antibiotic influence

The identification of pathogenic bacteria is a frequently required task. Current identification procedures are usually either time-consuming due to necessary cultivation steps or expensive and demanding in their application. Furthermore, previous treatment of a patient with antibiotics often renders routine analysis by culturing difficult. Since Raman microspectroscopy allows for the identification of single bacterial cells, it can be used to identify such difficult to culture bacteria. Yet until now, there have been no investigations whether antibiotic treatment of the bacteria influences the Raman spectroscopic identification. This study aims to rapidly identify bacteria that have been subjected to antibiotic treatment on single cell level with Raman microspectroscopy. Two strains of Escherichia coli and two species of Pseudomonas have been treated with four antibiotics, all targeting different sites of the bacteria. With Raman spectra from untreated bacteria, a linear discriminant analysis (LDA) model is built, which successfully identifies the species of independent untreated bacteria. Upon treatment of the bacteria with subinhibitory concentrations of ampicillin, ciprofloxacin, gentamicin, and sulfamethoxazole, the LDA model achieves species identification accuracies of 85.4, 95.3, 89.9, and 97.3 %, respectively. Increasing the antibiotic concentrations has no effect on the identification performance. An ampicillin-resistant strain of E. coli and a sample of P. aeruginosa are successfully identified as well. General representation of antibiotic stress in the training data improves species identification performance, while representation of a specific antibiotic improves strain distinction capability. In conclusion, the identification of antibiotically treated bacteria is possible with Raman microspectroscopy for diverse antibiotics on single cell level.

Fingerprinting bacterial strains using ion mobility spectrometry

Ion mobility spectrometry (IMS) is currently in widespread use for the detection and identification of narcotic and explosive compounds without prior sample clean-up or concentration steps. IMS analysis is rapid, less than a minute, and sensitive, with detection limits in the nanogram to picogram range, depending on the target analyte. Our studies indicate that this technique has potential for detection of specific components of bacterial cells and for identification and differentiation of bacterial strains and species within a minute, and with no specialized test kits or reagents required. When microgram quantities of whole bacterial cells are thermally desorbed, complex positive or negative ion patterns (plasmagrams) are obtained. These plasmagrams differ reproducibly for different strains and species and for different conditions of growth, and can be used for the classification and differentiation of specific strains and species of bacteria, including pathogens. Methods for improved ion peak detection, most notably sequential sample desorption at stepped increases in temperature (programmed temperature ramping), are described.     

Characterization of the surface enhanced raman scattering (SERS) of bacteria

The surface enhanced Raman scattering (SERS) of a number of species and strains of bacteria obtained on novel gold nanoparticle (approximately 80 nm) covered SiO(2) substrates excited at 785 nm is reported. Raman cross-section enhancements of >10(4) per bacterium are found for both Gram-positive and Gram-negative bacteria on these SERS active substrates. The SERS spectra of bacteria are spectrally less congested and exhibit greater species differentiation than their corresponding non-SERS (bulk) Raman spectra at this excitation wavelength. Fluorescence observed in the bulk Raman emission of Bacillus species is not apparent in the corresponding SERS spectra. Despite the field enhancement effects arising from the nanostructured metal surface, this fluorescence component appears "quenched" due to an energy transfer process which does not diminish the Raman emission. The surface enhancement effect allows the observation of Raman spectra of single bacterial cells excited at low incident powers and short data acquisition times. SERS spectra of B. anthracis Sterne illustrate this single cell level capability. Comparison with previous SERS studies reveals how the SERS vibrational signatures are strongly dependent on the morphology and nature of the SERS active substrates. The potential of SERS for detection and identification of bacterial pathogens with species and strain specificity on these gold particle covered glassy substrates is demonstrated by these results.     

Surface-enhanced Raman scattering imaging of single living lymphocytes with multivariate evaluation

This paper is aimed to show the possibility to determine individual organic compounds introduced into single living cells with surface-enhanced Raman spectroscopy (SERS). Surface enhancement was achieved with gold colloids that were allowed to diffuse into lymphocytes. An introduced analyte, rhodamine 6G, could be imaged together with for example nucleotides and amino acids of the cell. Multivariate evaluation of surface-enhanced Raman images proved to be a powerful tool for the separation of spectral information of various intracellular components. The principal component analysis (PCA) enabled identification of spectra containing different chemical information and separation of the spectral contribution of rhodamine 6G from the complex cellular matrix.     

Identification and classification of respiratory syncytial virus (RSV) strains by surface-enhanced Raman spectroscopy and multivariate statistical techniques

There is a critical need for a rapid and sensitive means of detecting viruses. Recent reports from our laboratory have shown that surface-enhanced Raman spectroscopy (SERS) can meet these needs. In this study, SERS was used to obtain the Raman spectra of respiratory syncytial virus (RSV) strains A/Long, B1, and A2. SERS-active substrates composed of silver nanorods were fabricated using an oblique angle vapor deposition method. The SERS spectra obtained for each virus were shown to posses a high degree of reproducibility. Based on their intrinsic SERS spectra, the four virus strains were readily detected and classified using the multivariate statistical methods principal component analysis (PCA) and hierarchical cluster analysis (HCA). The chemometric results show that PCA is able to separate the three virus strains unambiguously, whereas the HCA method was able to readily distinguish an A2 strain-related G gene mutant virus (ΔG) from the A2 strain. The results described here demonstrate that SERS, in combination with multivariate statistical methods, can be utilized as a highly sensitive and rapid viral identification and classification method.     

Discrimination of herbal medicines by molecular spectroscopy and chemical pattern recognition

The molecular spectroscopy (including near infrared diffuse reflection spectroscopy, Raman spectroscopy and infrared spectroscopy) with OPUS/Ident software was applied to clustering ginsengs according to species and processing methods. The results demonstrate that molecular spectroscopic analysis could provide a rapid, nondestructive and reliable method for identification of Chinese traditional medicine. It's found that the result of Raman spectroscopic analysis was the best one among these three methods. Comparing with traditional methods, which are laborious and time consuming, the molecular spectroscopic analysis is more effective.     

Raman spectroscopic detection of physiology changes in plasmid-bearing <Emphasis Type="Italic">Escherichia coli</Emphasis> with and without antibiotic treatment

Bacterial resistances against antibiotics are increasingly problematic for medical treatment of pathogenic bacteria, e.g., in hospitals. Resistances are, among other genes, often encoded on plasmids which can be transmitted between bacteria not only within one species, but also between different species, genera, and families. The plasmid pDrive is transformed into bacteria of the model strain Escherichia coli DH5α. Within this investigation, we applied micro-Raman spectroscopy with two different excitation wavelengths in combination with support vector machine (SVM) and linear discriminant analysis (LDA) to differentiate between bacterial cultures according to their cultural plasmid content. Recognition rates of about 92% and 90% are achieved by Raman excitation at 532 and 244 nm, respectively. The SVM loadings reveal that the pDrive transformed bacterial cultures exhibit a higher DNA content compared to the untransformed cultures. To elucidate the influence of the antibiotic, ampicillin-treated cultures are also comprised within this study and are classified with rates of about 97% and 100% for 532 and 244 nm Raman excitation, respectively. The Raman spectra recorded with 532 nm excitation wavelength show differences of the secondary protein structure and enhanced stress-related respiration rates for the ampicillin-treated cultures. Independent cultural replicates of either ampicillin-challenged or non-challenged cultures are successfully identified with identification rates of over 90%.     

Methods for extracting biochemical information from bacterial Raman spectra: focus on a group of structurally similar biomolecules--fatty acids

In this paper we explore the possibilities of Raman spectroscopy in order to deduce information on the fatty acid composition of bacterial cells. Therefore, representative strains of two bacterial taxa were each cultured in different conditions and in parallel analyzed by Raman spectroscopy and gaschromatographic FAME analysis. Raman spectra of pure fatty acids were recorded and used as reference spectra. The culturing conditions for each strain could be easily distinguished by the fatty acid information retrieved from bacterial Raman spectra. Chemometric techniques such as EMSC and PCA allowed to extract information about groups of fatty acids, that was consistent with the results from FAME analysis. Although the information retrieved from Raman spectroscopy is not as refined as that from FAME analysis, the presented methods could be useful to obtain basic information on the fatty acid present in bacteria when performing Raman spectroscopic analysis for fast whole cell profiling, which provides information for different types of cell components (fatty acids, amino acids, primary metabolites, etc.).     

Application of vibrational spectroscopy techniques for material identification from fire debris

The primary goal of this research is to demonstrate the use of vibrational spectroscopy techniques as a tool for the identification of materials post fire. This paper discusses the use micro-Raman spectroscopy and ATR-FTIR to identify materials found in fire debris. The polymeric materials under study were high density and low density polyethylene (HDPE and LDPE), polyvinyl chloride (PVC), polymethyl methacrylate (PMMA) and cotton. These are commonly materials found in households around the world, their identification from the debris provides useful forensic information on the spatial distribution of fuels in a fire compartment, thus allowing for accurate analysis and modelling. Earlier work has established Raman spectroscopy to be a very good tool for material identification post fire. The addition of ATR-FTIR spectroscopy as a technique in developing this novel tool for identification of materials post fire has established vibrational spectroscopy in the area of fire investigation. This study indicated that the limitations associated with Raman spectroscopy in post fire identification, could be made insignificant by the strengths of ATR-FTIR spectroscopy and vice versa. To further establish the validity of this identification process principal component analysis was used to discriminate between the spectrum of the burnt materials and an error analysis computed. Both techniques demonstrated that identification could be done with very minimal confusion between the materials studied.     

Implementation of Random Bacterial Genomic DNA Microarray Chip (RBGDMC) for Screening of Dominant Bacteria in Complex Cultures

The random bacterial genomic DNA microarray chip (RBGDMC), which was fabricated using random genomic DNA fragments obtained from the fragmentation of bacterial genome by using four different pairs of restriction enzymes, was found to discriminate bacterial species in the same genus and resulted in the determination of dominant bacteria in enriched cultures. The identification of a dominant bacterial species was successfully conducted in the co-culture of three different bacteria using the RBGDMC. In addition, the analysis of the chip data could confirm if any of the selected bacteria is the most abundant or if some bacteria were enriched and became the dominant species within the consortium after the samples were prepared from the repeated cultures of real sludge in a complex medium. This study shows the successful implementation of the RBGDMC for the identification and monitoring of dominant bacteria in complex environmental bacterial communities simply without any PCR amplification of the target nucleic acids.