Raman spectroscopy has recently been shown to be a potentially powerful whole-organism fingerprinting technique and is attracting interest within microbial systematics for the rapid identification of bacteria and fungi. However, while the Raman effect is so weak that only approximately 1 in 10(8) incident photons are Raman scattered (so that collection times are in the order of minutes), it can be greatly enhanced (by some 10(3)-10(6)-fold) if the molecules are attached to, or microscopically close to, a suitably roughened surface, a technique known as surface-enhanced Raman scattering (SERS). In this study, SERS, employing an aggregated silver colloid substrate, was used to analyze a collection of clinical bacterial isolates associated with urinary tract infections. While each spectrum took 10 s to collect, to acquire reproducible data, 50 spectra were collected making the spectral acquisition times per bacterium approximately 8 min. The multivariate statistical techniques of discriminant function analysis (DFA) and hierarchical cluster analysis (HCA) were applied in order to group these organisms based on their spectral fingerprints. The resultant ordination plots and dendrograms showed correct groupings for these organisms, including discrimination to strain level for a sample group of Escherichia coli, which was validated by projection of test spectra into DFA and HCA space. We believe this to be the first report showing bacterial discrimination using SERS.

Metabolomics and systems biology require the acquisition of reproducible, robust, reliable, and homogeneous biological data sets. Therefore, we developed and validated standard operating procedures (SOPs) for quenching and efficient extraction of metabolites from Escherichia coli to determine the best methods to approach global analysis of the metabolome. E. coli was grown in chemostat culture so that cellular metabolism could be held in reproducible, steady-state conditions under a range of precisely defined growth conditions, thus enabling sufficient replication of samples. The metabolome profiles were generated using gas chromatography/time-of-flight mass spectrometry (GC/TOF-MS). We employed univariate and multivariate statistical analyses to determine the most suitable method. This investigation indicates that 60% cold (-48 degrees C) methanol solution is the most appropriate method to quench metabolism, and we recommend 100% methanol, also at -48 degrees C, with multiple freeze-thaw cycles for the extraction of metabolites. However, complementary extractions would be necessary for coverage of the entire complement of metabolites as detected by GC/TOF-MS. Finally, the observation that metabolite leakage was significant and measurable whichever quenching method is used indicates that methods should be incorporated into the experiment to facilitate the accurate quantification of intracellular metabolites.

Within microbiology Raman spectroscopy is considered as a very important whole-organism fingerprinting technique, which is used to characterise, discriminate and identify microorganisms and assess how they respond to abiotic or biotic stress. Enhancing the sensitivity of Raman spectroscopy is very beneficial for the rapid analysis of bacteria (and indeed biological systems in general), where the ultimate goal is to achieve this without the need for lengthy cell culture. Bypassing this step would provide significant benefits in many areas such as medical, environmental and industrial microbiology, microbial systems biology, biological warfare countermeasures and bioprocess monitoring. In this tutorial review we will report on the advances made in bacterial studies, a relatively new and exciting application area for SERS.

Raman spectroscopy is attracting interest for the rapid identification of bacteria and fungi and is now becoming accepted as a potentially powerful whole-organism fingerprinting technique. However, the Raman effect is so weak that collection times are lengthy, and this insensitivity means that bacteria must be cultured to gain enough biomass, which therefore limits its usefulness in clinical laboratories where high-throughput analyses are needed. The Raman effect can fortunately be greatly enhanced (by some 10(3)-10(6)-fold) if the molecules are attached to, or microscopically close to, a suitably roughened surface; a technique known as surface-enhanced Raman scattering (SERS). In this study we investigated SERS, employing an aggregated silver colloid substrate, for the analysis of a closely related group of bacteria belonging to the genus Bacillus. Each spectrum took only 20 s to collect and highly reproducible data were generated. The multivariate statistical technique of principal components-discriminant function analysis (PC-DFA) was used to group these bacteria based on their SERS fingerprints. The resultant ordination plots showed that the SERS spectra were highly discriminatory and gave accurate identification at the strain level. In addition, Bacillus species also undergo sporulation, and we demonstrate that SERS peaks that could be attributed to the dipicolinic acid biomarker, could be readily generated from Bacillus spores.

Surface-enhanced Raman scattering (SERS) utilizing colloidal silver has already been shown to provide a rapid means of generating "whole-organism fingerprints" for use in bacterial identification and discrimination. However, one of the main drawbacks of the technique for the analysis of microbiological samples with optical Raman microspectroscopy has been the inability to acquire pre-emptively a region of the sample matrix where both the SERS substrate and biomass are both present. In this study, we introduce a Raman interface for scanning electron microscopy (SEM) and demonstrate the application of this technology to the reproducible and targeted collection of bacterial SERS spectra. In secondary electron mode, the SEM images clearly reveal regions of the sample matrix where the sodium borohydride-reduced silver colloidal particles are present, Stokes spectra collected from these regions are rich in vibrational bands, whereas spectra taken from other areas of the sample elicit a strong fluorescence response. Replicate SERS spectra were collected from two bacterial strains and show excellent reproducibility both by visual inspection and as demonstrated by principal components analysis on the whole SERS spectra.

The labelling of target biomolecules followed by detection using some form of optical spectroscopy has become common practice to aid in their detection. This approach has allowed the field of bioanalysis to dramatically expand; however, most methods suffer from the lack of the ability to discriminate between the components of a complex mixture. Currently, fluorescence spectroscopy is the method of choice but its ability to multiplex is greatly hampered by the broad overlapping spectra which are obtained. Surface enhanced resonance Raman scattering (SERRS) holds many advantages over fluorescence both in sensitivity and, more importantly here, in its ability to identify components in a mixture without separation due to the sharp fingerprint spectra obtained. Here the first multiplexed simultaneous detection of six different DNA sequences, corresponding to different strains of the Escherichia coli bacterium, each labelled with a different commercially available dye label (ROX, HEX, FAM, TET, Cy3, or TAMRA) is reported. This was achieved with the aid of multivariate analysis, also known as chemometrics, which can involve the application of a wide range of statistical and data analysis methods. In this study, both exploratory discriminant analysis and supervised learning, by partial least squares (PLS) regression, were used and the ability to discriminate whether a particular labelled oligonucleotide was present or absent in a mixture was achieved using PLS with very high sensitivity (0.98-1), specificity (0.98-1), accuracy (range 0.99-1), and precision (0.98-1).

While surface-enhanced Raman scattering (SERS) can increase the Raman cross-section by 4-6 orders of magnitude, for SERS to be effective it is necessary for the analyte to be either chemically bonded or within close proximity to the metal surface used. Therefore most studies investigating the biochemical constituents of microorganisms have introduced an external supply of gold or silver nanoparticles. As a consequence, the study of bacteria by SERS has to date been focused almost exclusively on the extracellular analysis of the Gram-negative outer cell membrane. Bacterial cells typically measure as little as 0.5 by 1 mum, and it is difficult to introduce a nanometer sized colloidal metal particle into this tiny environment. However, dissimilatory metal-reducing bacteria, including Shewanella and Geobacter species, can reduce a wide range of high valence metal ions, often within the cell, and for Ag(I) and Au(III) this can result in the formation of colloidal zero-valent particles. Here we report, for the first time, SERS of the bacterium Geobacter sulfurreducens facilitated by colloidal gold particles precipitated within the cell. In addition, we show SERS from the same organism following reduction of ionic silver, which results in colloidal silver depositions on the cell surface.

The rapid, accurate and non-invasive diagnosis of respiratory disease represents a challenge to clinicians, and the development of new treatments can be confounded by insufficient knowledge of lung disease phenotypes. Exhaled breath contains a complex mixture of volatile organic compounds (VOCs), some of which could potentially represent biomarkers for lung diseases. We have developed an adaptive sampling methodology for collecting concentrated samples of exhaled air from participants with impaired respiratory function, against which we employed two-stage thermal desorption gas chromatography-differential mobility spectrometry (GC-DMS) analysis, and showed that it was possible to discriminate between participants with and without chronic obstructive pulmonary disease (COPD). A 2.5 dm(3) volume of end tidal breath was collected onto adsorbent traps (Tenax TA/Carbotrap), from participants with severe COPD and healthy volunteers. Samples were thermally desorbed and analysed by GC-DMS, and the chromatograms analysed by univariate and multivariate analyses. Kruskal-Wallis ANOVA indicated several discriminatory (p < 0.01) signals, with good classification performance (receiver operator characteristic area up to 0.82). Partial least squares discriminant analysis using the full DMS chromatograms also gave excellent discrimination between groups (alpha = 19% and beta = 12.4%).