Confocal raman microspectroscopy : a novel diagnostic tool in medical microbiology

The aim of the research described in this thesis was to develop confocal Raman microspectroscopy techniques for the rapid identification and characterisation of clinically relevant microorganisms. Chapter 2 describes a study in which the accuracy of the identification of Enterococcus spp., based on Raman spectroscopy, is exemplified by a comparison with identification results based on phenotypic and genotypic methods. This chapter clearly shows the high accuracy that can be obtained when applying vibrational spectroscopies for microbial identifications. Chapter 3 reports on the unique method that was developed to analyse microbial microcolonies, directly on the solid culture medium. With this approach it is possible to "record" Raman spectra from very young cultures, where the colonies are typically between 10 and 100 microns in diameter. Culturing times therefore can be decreased to several hours, allowing rapid identification schemes to be developed. Chapter 4 describes the possibilities of this technique to probe the heterogeneity of microbial colonies directly on the solid culture medium. It was shown that microbial colonies after 6 hours incubation were most homogeneous as compared to older (12 and 24 hours) colonies, and consequently these 6 hour old colonies are better suited for the composition of a database. From the results obtained in this study, standardized protocols were derived, based on which reproducible Raman spectra can be obtained. With the methodology for rapidly performing measurements now developed, and the culturing conditions optimized for obtaining reproducible Raman spectra. the potential ofthe technique to identifY microbes by their Raman spectrum was evaluated in chapters. A collection of yeast strains from the genus Candida was used to arrive at the most appropriate manner of analysing large amounts of data. Based on a library of Raman spectra from known Candida strains, an identification method was developed to identify these strains based on their Raman spectrum. Finally, to test the methodology developed in all previous studies, a prospective study of clinical isolates is described in chapter 6. Parallel to the routine analysis of positive blood samples from patients in intensive care units and a random selection of other wards, Raman identification was performed of these samples. A summary of all results is provided in chapter 7

The use of Raman spectroscopy in the epidemiology of methicillin-resistant Staphylococcus aureus of human- and animal-related clonal lineages

AbstractIn order to perform a cost-effective search and destroy policy for methicillin-resistant Staphylococcus aureus (MRSA), a quick and reliable typing method is essential. In an area with a high level of animal-related MRSA ST398, pulsed field gel electrophoresis (PFGE) typing and spa-typing are not sufficient to discriminate between co-incidental findings and true transmission of MRSA. This study is the first to retrospectively show the performance of Raman spectroscopy in 16 well-documented outbreaks. We analysed 525 isolates, 286 MRSA ST398 and 239 from other PFGE clusters with Raman spectroscopy. When epidemiologically linked isolates from the outbreaks were analysed with PFGE as the reference standard, Raman spectroscopy correctly identified 97% of cases that were indistinguishable from the index case. With Raman cluster analysis, the most dominant distinction was between MRSA ST398 and other MRSA of human clonal lineages. Within MRSA ST398, 22 different Raman clusters were identified. Raman typing correctly identified an ST398 (spa type t567) outbreak in a hospital setting. No direct correlation was observed between Raman clusters and spa types. We conclude that Raman spectroscopy is a quick and reliable method of MRSA typing, which can be used in outbreak settings and it is comparable to PFGE, with the added advantage that PFGE non-typeable isolates can also be readily typed using the same sample preparation protocol

Raman spectroscopy-based identification of nosocomial outbreaks of the clonal bacterium Escherichia coli

DNA-based techniques are frequently used to confirm the relatedness of putative outbreak isolates. These techniques often lack the discriminatory power when analyzing closely related microbes such as E. coli. Here the value of Raman spectroscopy as a typing tool for E. coli in a clinical setting was retrospectively evaluated

Raman spectroscopy and advanced mathematical modelling in the discrimination of human thyroid cell lines

Raman spectroscopy could offer non-invasive, rapid and an objective nature to cancer diagnostics. However, much work in this field has focused on resolving differences between cancerous and non-cancerous tissues, and lacks the reproducibility and interpretation to be put into clinical practice. Much work is needed on basic cellular differences between malignancy and normal. This would allow the establishment of a clinically relevant cellular based model to translate to tissue classification. Raman spectroscopy provides a very detailed biochemical analysis of the target material and to 'unlock' this potential requires sophisticated mathematical modelling such as neural networks as an adjunct to data interpretation. Commercially obtained cancerous and non-cancerous cells, cultured in the laboratory were used in Raman spectral measurements. Data trends were visualised through PCA and then subjected to neural network analysis based on self-organising maps; consisting of m maps, where m is the number of classes to be recognised. Each map approximates the statistical distribution of a given class. The neural network analysis provided a 95% accuracy for identification of the cancerous cell line and 92% accuracy for normal cell line. In this preliminay study we have demonstrated th ability to distinguish between "normal" and cancerous commercial cell lines. This encourages future work to establish the reasons underpinning these spectral differences and to move forward to more complex systems involving tissues. We have also shown that the use of sophisticated mathematical modelling allows a high degree of discrimination of 'raw' spectral data

A study of Docetaxel-induced effects in MCF-7 cells by means of Raman microspectroscopy

Chemotherapies feature a low success rate of about 25%, and therefore, the choice of the most effective cytostatic drug for the individual patient and monitoring the efficiency of an ongoing chemotherapy are important steps towards personalized therapy. Thereby, an objective method able to differentiate between treated and untreated cancer cells would be essential. In this study, we provide molecular insights into Docetaxel-induced effects in MCF-7 cells, as a model system for adenocarcinoma, by means of Raman microspectroscopy combined with powerful chemometric methods. The analysis of the Raman data is divided into two steps. In the first part, the morphology of cell organelles, e.g. the cell nucleus has been visualized by analysing the Raman spectra with k-means cluster analysis and artificial neural networks and compared to the histopathologic gold standard method hematoxylin and eosin staining. This comparison showed that Raman microscopy is capable of displaying the cell morphology; however, this is in contrast to hematoxylin and eosin staining label free and can therefore be applied potentially in vivo. Because Docetaxel is a drug acting within the cell nucleus, Raman spectra originating from the cell nucleus region were further investigated in a next step. Thereby we were able to differentiate treated from untreated MCF-7 cells and to quantify the cell–drug response by utilizing linear discriminant analysis models

Rapid Etiological Classification of Meningitis by NMR Spectroscopy Based on Metabolite Profiles and Host Response

Bacterial meningitis is an acute disease with high mortality that is reduced by early treatment. Identification of the causative microorganism by culture is sensitive but slow. Large volumes of cerebrospinal fluid (CSF) are required to maximise sensitivity and establish a provisional diagnosis. We have utilised nuclear magnetic resonance (NMR) spectroscopy to rapidly characterise the biochemical profile of CSF from normal rats and animals with pneumococcal or cryptococcal meningitis. Use of a miniaturised capillary NMR system overcame limitations caused by small CSF volumes and low metabolite concentrations. The analysis of the complex NMR spectroscopic data by a supervised statistical classification strategy included major, minor and unidentified metabolites. Reproducible spectral profiles were generated within less than three minutes, and revealed differences in the relative amounts of glucose, lactate, citrate, amino acid residues, acetate and polyols in the three groups. Contributions from microbial metabolism and inflammatory cells were evident. The computerised statistical classification strategy is based on both major metabolites and minor, partially unidentified metabolites. This data analysis proved highly specific for diagnosis (100% specificity in the final validation set), provided those with visible blood contamination were excluded from analysis; 6-8% of samples were classified as indeterminate. This proof of principle study suggests that a rapid etiologic diagnosis of meningitis is possible without prior culture. The method can be fully automated and avoids delays due to processing and selective identification of specific pathogens that are inherent in DNA-based techniques

Raman micro-spectroscopy can be used to investigate the developmental stage of the mouse oocyte

In recent years, the uptake of assisted reproductive techniques such as in vitro fertilisation has risen exponentially. However, there is much that is still not fully understood about the biochemical modifications that take place during the development and maturation of the oocyte. As such, it is essential to further the understanding of how oocyte manipulation during these procedures ultimately affects its developmental potential; yet, there are few methods currently available which are capable of providing a quantitative measure of oocyte quality. Raman spectroscopy enables investigation of the global biochemical profile of intact cells without the need for labelling. Here, Raman spectra were acquired from the ooplasm of mouse oocytes at various stages of development, from late pre-antral follicles, collected after in vitro maturation within their ovarian follicles and from unstimulated and stimulated ovulatory cycles. Using a combination of univariate and multivariate statistical methods, it was found that ooplasm lipid content could be used to discriminate between different stages of oocyte development. Furthermore, the spectral profiles of mature oocytes revealed that oocytes which have developed in vitro are protein-deficient when compared to in vivo grown oocytes. Finally, the ratio of two Raman peak intensities, namely 1605:1447 cm21, used as a proxy for the protein-to-lipid ratio of the ooplasm, was shown to be indicative of the oocyte’s quality. Together, results indicate that Raman spectroscopy may present an alternative analytical tool fo