Whole-organism Fingerprinting Research Articles

Establishment of real-time viral pathogen detection method-surface-enhanced Raman scattering system for porcine circovirus type II

The transition towards large-scale intensive pig farming methods has resulted in an increase in cases of complex, mixed infections, and secondary infections. Porcine circovirus (PCV), the causative agent of PCV disease (PCVD), is a small nonenveloped DNA virus containing a single-stranded circular genome. Previously, PCV was even considered nonpathogenic to pigs. However, a novel strain of PCV, designated PCV-2, has been associated with various disease syndromes in pigs over the last 5 years. PCV-2 primarily induces post-weaning multisystemic wasting syndrome, characterized by clinical signs such as debility, dyspnea, palpable lymphadenopathy, diarrhea, and pallor or icterus. PCV-2 is now recognized as an important emerging pathogen. Early diagnosis and disease prevention have become crucial for effective pig farm management. However, clinical diagnosis may be complicated by antibiotic treatment and atypical disease symptoms. To ensure accurate pathology diagnosis, it is essential to integrate robust laboratory diagnostics with traditional methods. Surface-enhanced Raman Scattering (SERS) spectroscopy has emerged as a potentially powerful technique for whole-organism fingerprinting, enabling rapid identification of bacteria. Biosensors utilizing SERS offer promising capabilities for sensitive and quick detection of bacterial pathogens, thereby reducing diagnosis time. In this study, our aim was to characterize and evaluate a SERS-based diagnostic system for detecting and identifying PCV-2 present in pooled swine sera and feces. We compared the spectra of PCV-2 recovered from the specimens to those of pure PCV-2 to determine the PCV-2 molecular fingerprint. Our results demonstrated successful detection, identification, and classification of PCV-2 in swine sera and feces using SERS. SERS provided reproducible molecular spectroscopic signatures suitable for analytical applications. This approach presents a new and potent tool for real-time surveillance of animal viral pathogens in clinical settings.

Real-time surveillance of zoonotic and pig pathogens using surface-enhanced Raman scattering system

The shift towards large-scale intensive pig farming methods has led to an increase in cases of complex, mixed infections, and secondary infections. Early diagnosis and disease prevention have become crucial for effective pig farm management. However, clinical diagnosis may be complicated by antibiotic treatment and atypical disease symptoms. To ensure accurate pathology diagnosis, it is essential to integrate robust laboratory diagnostics with traditional methods. Surface-enhanced Raman scattering (SERS) spectroscopy has emerged as a potentially powerful technique for whole-organism fingerprinting, enabling rapid identification of bacteria. Biosensors utilizing SERS offer promising capabilities for sensitive and quick detection of bacterial pathogens, thus reducing diagnosis time. In this study, we aimed to characterize and evaluate a SERS-based diagnostic system for detecting and identifying bacteria in specific pathogen free (SPF) mice, focusing on two bacterial zoonoses and swine bacteria present in pooled swine sera, feces, and meat. We compared the spectra of bacteria recovered from the specimens to those of pure cultured bacteria and conducted principal component analysis to determine the bacterial molecular fingerprint. Our results demonstrated successful detection, identification, and classification of these bacteria in mice specimens (sera and feces) and swine specimens (sera, feces, and meat) using SERS. SERS provided reproducible molecular spectroscopic signatures suitable for analytical applications. This approach presents a new and potent tool for real-time surveillance of animal bacterial pathogens in clinical settings.

Characterization and Discrimination of Gram-Positive Bacteria Using Raman Spectroscopy with the Aid of Principal Component Analysis.

Raman scattering and its particular effect, surface-enhanced Raman scattering (SERS), are whole-organism fingerprinting spectroscopic techniques that gain more and more popularity in bacterial detection. In this work, two relevant Gram-positive bacteria species, Lactobacillus casei (L. casei) and Listeria monocytogenes (L. monocytogenes) were characterized based on their Raman and SERS spectral fingerprints. The SERS spectra were used to identify the biochemical structures of the bacterial cell wall. Two synthesis methods of the SERS-active nanomaterials were used and the recorded spectra were analyzed. L. casei and L. monocytogenes were successfully discriminated by applying Principal Component Analysis (PCA) to their specific spectral data.

Nanoarchitecture Based SERS for Biomolecular Fingerprinting and Label-Free Disease Markers Diagnosis.

ConspectusSurface-enhanced Raman spectroscopy (SERS) fingerprinting is highly promising for identifying disease markers from complex mixtures of clinical sample, which has the capability to take medical diagnoses to the next level. Although vibrational frequency in Raman spectra is unique for each biomolecule, which can be used as fingerprint identification, it has not been considered to be used routinely for biosensing due to the fact that the Raman signal is very weak. Contemporary SERS has been demonstrated to be an excellent analytical tool for practical label-free sensing applications due its ability to enhance Raman signals by factors of up to 108–1014 orders of magnitude. Although SERS was discovered more than 40 years ago, its applications are still rare outside the spectroscopy community and it is mainly due to the fact that how to control, manipulate and amplify light on the “hot spots” near the metal surface is in the infancy stage.In this Account, we describe our contribution to develop nanoachitecture based highly reproducible and ultrasensitive detection capability SERS platform via low-cost synthetic routes. Using one-dimensional (1D) carbon nanotube (CNT), two-dimensional (2D) graphene oxide (GO), and zero-dimensional (0D) plasmonic nanoparticle, 0D to 3D SERS substrates have been designed, which represent highly powerful platform for biological diagnosis. We discuss the major design criteria we have used to develop robust SERS substrate to possess high density “hot spots” with very good reproducibility. SERS enhancement factor for 3D SERS substrate is about 5 orders of magnitude higher than only plasmonic nanoparticle and more than 9 orders of magnitude higher than 2D GO. Theoretical finite-difference time-domain (FDTD) stimulation data show that the electric field enhancement |E|2 can be more than 2 orders of magnitude in “hot spots”, which suggests that SERS enhancement factors can be greater than 104 due to the formation of high density “hot spots” in 3D substrate. Next, we discuss the utilization of nanoachitecture based SERS substrate for ultrasensitive and selective diagnosis of infectious disease organisms such as drug resistance bacteria and mosquito-borne flavi-viruses that cause significant health problems worldwide. SERS based “whole-organism fingerprints” has been used to identify infectious disease organisms even when they are so closely related that they are difficult to distinguish. The detection capability can be as low as 10 CFU/mL for methicillin-resistant Staphylococcus aureus (MRSA) and 10 PFU/mL for Dengue virus (DENV) and West Nile virus (WNV). After that, we introduce exciting research findings by our group on the applications of nanoachitecture based SERS substrate for the capture and fingerprint detection of rotavirus from water and Alzheimer’s disease biomarkers from whole blood sample. The SERS detection limit for β-amyloid (Aβ proteins) and tau protein using 3D SERS platform is several orders of magnitude higher than the currently used technology in clinics. Finally, we highlight the promises, major challenges and prospect of nanoachitecture based SERS in biomedical diagnosis field.

Evaluation of the Doodlebug: A Biochip for Detecting Waterborne Pathogens

Doodlebug is a new biochip technology that uses surface-enhanced Raman scattering (SERS) microscopy for label- and reagent-free transduction. The biochip itself comprises pixels of capture biomolecules immobilized on a SERS-active metal surface. Once the biochip has been exposed to the sample and the capture biomolecules have selectively bound their ligands, a Raman microscope is used to collect SERS fingerprints from the pixels on the chip. Because imaging Raman microscopes offer spatial resolution as fine as 250 nm, spectra can be collected from individual microorganisms captured by the pixels. This exquisite sensitivity eliminates the need for labels such as fluors or enzymes, or reagents such as those used in PCR; and, thereby, eliminates the potential for false responses from environmental constituents that either mimic or mask the signals from labels or reagents. The WERF program showed that SERS, like other whole-organism fingerprinting techniques, is very specific. Studies with representative strains of six Legionella species, recently-passaged oocysts from six C. parvum genotype 2 strains, three genotype 1 strains, and a C. meleagridis strain, plus a Giardia sample, indicated that SERS could identify the bacteria and oocysts at the species and subspecies levels. Therefore, Doodlebug holds promise for individually identifying pathogens, even when organisms that cross-react with the capture biomolecules are present in a sample. The fingerprints that were used in oocyst identification were routinely collected from a single oocyst within 60 seconds. Experiments involving the impact of environmental and water treatment conditions suggest that the Doodlebug approach will be able to differentiate between viable and nonviable, and possibly injured, organisms. It appears that the SERS fingerprint may even be useful in determining the ‘age’ of an oocyst; e.g., whether it is too old to be infectious. The WERF program culminated in a demonstration of the simultaneous detection of two very different microorganisms (i.e., C. parvum oocysts and Bacillus spores) present in a mixture. Although oocyst capture was selective, some spores adhered nonspecifically to the anti-oocyst MAb surface. This showcased Doodlebug's ability to eliminate false responses due to nonspecific adherence or cross-reactivity, since the spores were readily differentiated from the oocysts on the basis of their SERS fingerprints. This title belongs to WERF Research Report Series ISBN: 9781843396680 (Print) ISBN: 9781780403175 (eBook)

Exploring the Potential of Stable Isotope (Resonance) Raman Microspectroscopy and Surface-Enhanced Raman Scattering for the Analysis of Microorganisms at Single Cell Level.

Raman microspectroscopy is a prime tool to characterize the molecular and isotopic composition of microbial cells. However, low sensitivity and long acquisition times limit a broad applicability of the method in environmental analysis. In this study, we explore the potential, the applicability, and the limitations of stable isotope Raman microspectroscopy (SIRM), resonance SIRM, and SIRM in combination with surface-enhanced Raman scattering (SERS) for the characterization of single bacterial cells. The latter two techniques have the potential to significantly increase sensitivity and decrease measurement times in SIRM, but to date, there are no (SERS-SIRM) or only a limited number (resonance SIRM) of studies in environmental microbiology. The analyzed microorganisms were grown with substrates fully labeled with the stable isotopes (13)C or (2)H and compounds with natural abundance of atomic isotopes ((12)C 98.89% or (1)H 99.9844%, designated as (12)C or (1)H, respectively). Raman bands of bacterial cell compounds in stable isotope-labeled microorganisms exhibited a characteristic red-shift in the spectra. In particular, the sharp phenylalanine band was found to be an applicable marker band for SIRM analysis of the Deltaproteobacterium strain N47 growing anaerobically on (13)C-naphthalene. The study of G. metallireducens grown with (13)C- and (2)H-acetate showed that the information on the chromophore cytochrome c obtained by resonance SIRM at 532 nm excitation wavelength can be successfully complemented by whole-organism fingerprints of bacteria cells achieved by regular SIRM after photobleaching. Furthermore, we present here for the first time the reproducible SERS analysis of microbial cells labeled with stable isotopes. Escherichia coli strain DSM 1116 cultivated with (12)C- or (13)C-glucose was used as a model organism. Silver nanoparticles synthesized in situ were applied as SERS media. We observed a reproducible red-shift of an adenine-related marker band from 733 to 720 cm(-1) in SERS spectra for (13)C-labeled cells. Additionally, Raman measurements of (12)C/(13)C-glucose and -phenylalanine mixtures were performed to elucidate the feasibility of SIRM for nondestructive quantitative and spatially resolved analysis. The performed analysis of isotopically labeled microbial cells with SERS-SIRM and resonance SIRM paves the way toward novel approaches to apply Raman microspectroscopy in environmental process studies.

Rapid Discrimination of Bacteria and Bacteriophages by Raman Spectroscopy

An accurate, high sensitivity and rapid identification assay of bacterial and virus is extremely important in areas such as medical diagnosis, biological research, and environmental monitoring. Raman spectra generates from the molecular species often referred to as ‘whole-organism fingerprinting’ being able to give comprehensive quantitative information about the overall biochemical composition of a microbial sample. Rapid discrimination of bacteria and bacteriaphage can be great important for food, environment and clinical diagnosis. Raman spectra of bacteria stains (Salmonella, Acinetobacter baumannii, Klebsiella pneumoniae) and their related bacteriaphages were analyzed. Raman pattern indicated the characteristic peaks and intensity difference between either bacteria nor phages. Barcode analysis, the intensity of 21 majority peaks summarized from the spectral interval 400–1200 nm showed the distinguishable data code for those cells. The present results suggest that both bacteria (micron) and phages (sub micron) can be directly discriminated by their Raman fingerprint and the barcode analysis method may help us transfer the complex spectra into easily and readability data

High-throughput phenotyping of uropathogenic E. coli isolates with Fourier transform infrared spectroscopy

Fourier transform infrared (FT-IR) spectroscopy is an established rapid whole-organism fingerprinting method that generates metabolic fingerprints from bacteria that reflect the phenotype of the microorganism under investigation. However, whilst FT-IR spectroscopy is fast (typically 10 s to 1 min per sample), the approaches for microbial sample preparation can be time consuming as plate culture or shake flasks are used for growth of the organism. We report a new approach that allows micro-cultivation of bacteria from low volumes (typically 200 μL) to be coupled with FT-IR spectroscopy. This approach is fast and easy to perform and gives equivalent data to the lengthier and more expensive shake flask cultivations (sample volume = 20 mL). With this micro-culture approach we also demonstrate high reproducibility of the metabolic fingerprints. The approach allowed separation of different isolates of Escherichia coli involved in urinary tract infection, including members of the globally disseminated ST131 clone, with respect to both genotype and resistance or otherwise to the antibiotic Ciprofloxacin.

Comparison of near infrared and mid infrared spectroscopy to discriminate between wines produced by different Oenococcus Oeni strains after malolactic fermentation: A feasibility study

The wine industry requires rapid, comprehensive methods and techniques to answer the new challenges driven by the market demands. Recent advances in spectroscopy technologies have brought about a revolution in the manner in which biological systems are visualised and analysed. The measurement of numerous small molecules (metabolites) metabolised by microorganisms during growth in wine, will benefit from techniques that require minimal sample preparation, permit the automatic analysis of many samples with negligible reagent costs, allow their rapid characterization against a stable database, and are easy to use. With recent developments in analytical instrumentation, these requirements are being fulfilled by vibrational spectroscopic methods, often referred to as “whole-organism fingerprinting” and more recently “metabolic fingerprinting”. The objective of this study was to evaluate and compare the use of near (NIR) and mid infrared (MIR) spectroscopy as rapid methods to distinguish red and white wines obtained by using different strains of Oenococcus oeni following malolactic fermentation. Using NIR and MIR wines produced with different O. oeni strains could be distinguished both in red and white wines yielding correct classification rates between 67 and 100% depending on the strain.

Fourier transform infrared spectroscopy as a metabolite fingerprinting tool for monitoring the phenotypic changes in complex bacterial communities capable of degrading phenol

The coking process produces great volumes of wastewater contaminated with pollutants such as cyanides, sulfides and phenolics. Chemical and physical remediation of this wastewater removes the majority of these pollutants; however, these processes do not remove phenol and thiocyanate. The removal of these compounds has been effected during bioremediation with activated sludge containing a complex microbial community. In this investigation we acquired activated sludge from an industrial bioreactor capable of degrading phenol. The sludge was incubated in our laboratory and monitored for its ability to degrade phenol over a 48 h period. Multiple samples were taken across the time-course and analysed by Fourier transform infrared (FT-IR) spectroscopy. FT-IR was used as a whole-organism fingerprinting approach to monitor biochemical changes in the bacterial cells during the degradation of phenol. We also investigated the ability of the activated sludge to degrade phenol following extended periods (2-131 days) of storage in the absence of phenol. A reduction was observed in the ability of the microbial community to degrade phenol and this was accompanied by a detectable biochemical change in the FT-IR fingerprint related to cellular phenotype of the microbial community. In the absence of phenol a decrease in thiocyanate vibrations was observed, reflecting the ability of these communities to degrade this substrate. Actively degrading communities showed an additional new band in their FT-IR spectra that could be attributed to phenol degradation products from the ortho- and meta-cleavage of the aromatic ring. This study demonstrates that FT-IR spectroscopy when combined with chemometric analysis is a very powerful high throughput screening approach for assessing the metabolic capability of complex microbial communities.

Raman microscopy and surface‐enhanced Raman scattering (SERS) for in situ analysis of biofilms

Biofilms are communities of micro-organisms enclosed in a matrix of extracellular polymeric substances (EPS). They represent a ubiquitous form of microbial life on Earth. Detailed information on chemical composition and structure of the EPS matrix is relevant in medicine, industry and technological processes. Raman microscopy (RM) provides whole-organism fingerprints for biological samples with spatial resolution in the microm range and enables correlations between optical and chemical images to be made. Low water background makes RM beneficial for in situ studies of biofilms, since water is the major component of the biofilm matrix. In this paper we discuss the feasibility of RM for chemical characterization of different structures in a multispecies biofilm matrix, including microbial constituents and EPS. We show that by improving the sensitivity of RM with surface-enhanced Raman scattering (SERS) one can perform rapid biofilm analysis. In particular, by choosing appropriate SERS substrates and solving the problem of SERS measurement reproducibility one can carry out in situ study of different components in the complex biofilm matrix.

Characterisation and identification of bacteria using SERS

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.

Two-dimensional low resolution Raman spectroscopy applied to fast discrimination of microorganisms that cause pharyngitis: A whole-organism fingerprinting approach

The discrimination of the bacteria that cause pharyngitis through classical–microbiological methods is very efficient in the great majority of the cases. However, the high cost of chemicals and the time spent for such identifications, about four days, could generate serious consequences for the patients. Thus, the search for low cost spectroscopic methods which would allow a fast and reagentless discrimination of these microorganisms is extremely relevant. In this work, the main microorganisms that cause pharyngitis: S. aureus, S. pyogenes and Neisseria gonorrhoeae were studied. For each of the microorganisms 60 different dispersions were prepared using physiological solution as solvent and its Raman spectra were recorded. The 1D spectra obtained were similar, making it very difficult to differentiate the microorganisms. However, applying the 2D correlation method, it was possible to identify the microorganisms evaluated using the synchronous spectrum as “whole-organism fingerprinting” in a reduced time interval (∼10 h).

Differentiation of Group I and Group II Strains of Clostridium botulinum by Focal Plane Array Fourier Transform Infrared Spectroscopy

A method was developed for whole-organism fingerprinting of Clostridium botulinum isolates by focal plane array Fourier transform infrared (FPA-FTIR) spectroscopy. A database of 150,000 infrared spectra of 44 strains of C. botulinum was acquired using a FPA-FTIR imaging spectrometer equipped with a 16 × 16 array detector to evaluate the ability of FTIR spectroscopy to differentiate the 44 strains. The database contained strains from C. botulinum groups I and II producing botulinum neurotoxin of serotypes A, B, E, and F. All strains were grown on each of three agar media (brain heart infusion, McClung Toabe agar base, and universal) prior to spectral acquisition. Given the dependence of the infrared spectra of microorganisms on the composition of the growth medium, the spectra were initially separated into three subsets corresponding to the three growth media employed. However, the replicate spectra of all strains, regardless of growth medium, were properly clustered by hierarchical cluster analysis based on differences in their infrared spectral profiles in three narrow spectral regions (1,428 to 1,412, 1,296 to 1,284, and 1,112 to 1,100 cm−1). The dendrogram generated from the FTIR data revealed complete separation between group I and group II strains. The spectral differences between group I and group II strains allowed accurate classification of C. botulinum strains at the group level in two blind validation studies (n = 40). These results demonstrate that FPA-FTIR spectroscopy has the potential for rapid discrimination of group I and group II C. botulinum strains in less than 3 min per sample.

Two-dimensional low resolution raman spectroscopy applied to fast discrimination of clinically relevant microorganisms: a whole-organism fingerprinting approach

The discrimination of the bacteria that cause gastroenteritis through classical microbiological methods is very efficient in the great majority of the cases. However, the high cost of chemicals and the time spent for such identifications, about four days, could generate serious consequences for the patients. Thus, the search for low cost spectroscopic methods which would allow a fast and reagentless discrimination of these microorganisms is extremely relevant. In this work the main microorganisms that cause gastroenteritis: E. coli, S. chroleraesuis, S. flexneri were studied. For each of the microorganisms sixty different dispersions were prepared using physiological solution as solvent and its Raman spectra recorded. The 1D spectra obtained were similar, making it very difficult to differentiate the microorganisms. However, applying the 2D correlation method, it was possible to identify the microorganisms evaluated using the synchronous spectrum as "whole-organism fingerprinting" in a reduced time interval (~10 h).

Surface-enhanced Raman scattering for the rapid discrimination of bacteria

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.

The rapid differentiation of Streptomyces isolates using Fourier transform infrared spectroscopy

Fifteen putative Streptomyces spp. isolated from soil were selected to be analysed using Fourier transform infrared (FT-IR) spectroscopy and 16S rRNA gene sequencing. Four colour groupings (groups 1–4) were obtained and described according to the colour of their substrate mycelia, aerial mycelia, spore mass and pigmentation. The dendrogram constructed using unsupervised cluster analysis of the FT-IR data was in good congruence with the four colour groups and the neighbour-joining phylogenetic tree for 16S rDNA sequencing. In particular, those isolates having 100% 16S rDNA similarities which are supposed to be from the same species can be separated from each other using FT-IR analysis. This high throughput method only takes 1–10 s to collect a FT-IR spectrum from each sample, and both 96- and 384-well microplates are available for automated analysis. FT-IR therefore presents itself as a rapid, whole-organism fingerprinting approach which can be used for preliminary differentiation of Streptomyces spp. at sub-species or strain level.

The rapid identification of Acinetobacter species using Fourier transform infrared spectroscopy.

Fourier transform infrared (FT-IR) was used to analyse a selection of Acinetobacter isolates in order to determine if this approach could discriminate readily between the known genomic species of this genus and environmental isolates from activated sludge. FT-IR spectroscopy is a rapid whole-organism fingerprinting method, typically taking only 10 s per sample, and generates 'holistic' biochemical profiles (or 'fingerprints') from biological materials. The cluster analysis produced by FT-IR was compared with previous polyphasic taxonomic studies on these isolates and with 16S-23S rDNA intergenic spacer region (ISR) fingerprinting presented in this paper. FT-IR and 16S-23S rDNA ISR analyses together indicate that some of the Acinetobacter genomic species are particularly heterogeneous and poorly defined, making characterization of the unknown environmental isolates with the genomic species difficult. Whilst the characterization of the isolates from activated sludge revealed by FT-IR and 16S-23S rDNA ISR were not directly comparable, the dendrogram produced from FT-IR data did correlate well with the outcomes of the other polyphasic taxonomic work. We believe it would be advantageous to pursue this approach further and establish a comprehensive database of taxonomically well-defined Acinetobacter species to aid the identification of unknown strains. In this instance, FT-IR may provide the rapid identification method eagerly sought for the routine identification of Acinetobacter isolates from a wide range of environmental sources.

Discrimination of bacteria using surface-enhanced Raman spectroscopy.

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.

Rapid identification of urinary tract infection bacteria using hyperspectral whole-organism fingerprinting and artificial neural networks.

Three rapid spectroscopic approaches for whole-organism fingerprinting-pyrolysis mass spectrometry (PyMS), Fourier transform infra-red spectroscopy (FT-IR) and dispersive Raman microscopy--were used to analyse a group of 59 clinical bacterial isolates associated with urinary tract infection. Direct visual analysis of these spectra was not possible, highlighting the need to use methods to reduce the dimensionality of these hyperspectral data. The unsupervised methods of discriminant function and hierarchical cluster analyses were employed to group these organisms based on their spectral fingerprints, but none produced wholly satisfactory groupings which were characteristic for each of the five bacterial types. In contrast, for PyMS and FT-IR, the artificial neural network (ANN) approaches exploiting multi-layer perceptrons or radial basis functions could be trained with representative spectra of the five bacterial groups so that isolates from clinical bacteriuria in an independent unseen test set could be correctly identified. Comparable ANNs trained with Raman spectra correctly identified some 80% of the same test set. PyMS and FT-IR have often been exploited within microbial systematics, but these are believed to be the first published data showing the ability of dispersive Raman microscopy to discriminate clinically significant intact bacterial species. These results demonstrate that modern analytical spectroscopies of high intrinsic dimensionality can provide rapid accurate microbial characterization techniques, but only when combined with appropriate chemometrics.