No abstract

Routine clinical microbiological identification of pathogenic microorganisms is largely based on nutritional and biochemical tests. In the case of severely ill patients, the unavoidable time delay associated with such identification procedures can be fatal. We present a novel identification method based on confocal Raman microspectroscopy. With this approach it is possible to obtain Raman spectra directly from microbial microcolonies on the solid culture medium, which have developed after only 6 h of culturing for the most commonly encountered organisms. Due to the limited thickness of microcolonies, some of the underlying culture medium is sampled together with the bacteria. Spectra measured at different depths in a microcolony contain different amounts of the medium signal. A mathematical routine, involving vector algebra, is described for the nonsubjective correction of spectra for variable signal contributions of the medium. To illustrate the possibilities of our approach for the identification of microorganisms, Raman spectra were collected from 6-h microcolonies of five bacterial strains on solid culture medium. The classification results show that confocal Raman microspectroscopy has great potential as a powerful new tool in clinical diagnostic microbiology.

Rapid identification of microbial pathogens reduces infection-related morbidity and mortality of hospitalized patients. Raman spectra and Fourier transform infrared (IR) spectra constitute highly specific spectroscopic fingerprints of microorganisms by which they can be identified. Little biomass is required, so that spectra of microcolonies can be obtained. A prospective clinical study was carried out in which the causative pathogens of bloodstream infections in hospitalized patients were identified. Reference libraries of Raman and IR spectra of bacterial and yeast pathogens highly prevalent in bloodstream infections were created. They were used to develop identification models based on linear discriminant analysis and artificial neural networks. These models were tested by carrying out vibrational spectroscopic identification in parallel with routine diagnostic phenotypic identification. Whereas routine identification has a typical turnaround time of 1 to 2 days, Raman and IR spectra of microcolonies were collected 6 to 8 h after microbial growth was detected by an automated blood culture system. One hundred fifteen samples were analyzed by Raman spectroscopy, of which 109 contained bacteria and 6 contained yeasts. One hundred twenty-one samples were analyzed by IR spectroscopy. Of these, 114 yielded bacteria and 7 were positive for yeasts. High identification accuracy was achieved in both the Raman (92.2%, 106 of 115) and IR (98.3%, 119 of 121) studies. Vibrational spectroscopic techniques enable simple, rapid, and accurate microbial identification. These advantages can be easily transferred to other applications in diagnostic microbiology, e.g., to accelerate identification of fastidious microorganisms.

Fourier transform infrared and Raman microspectroscopy are currently being developed as new methods for the rapid identification of clinically relevant microorganisms. These methods involve measuring spectra from microcolonies which have been cultured for as little as 6 h, followed by the nonsubjective identification of microorganisms through the use of multivariate statistical analyses. To examine the biological heterogeneity of microorganism growth which is reflected in the spectra, measurements were acquired from various positions within (micro)colonies cultured for 6, 12, and 24 h. The studies reveal that there is little spectral variance in 6-h microcolonies. In contrast, the 12-and 24-h cultures exhibited a significant amount of heterogeneity. Hierarchical cluster analysis of the spectra from the various positions and depths reveals the presence of different layers in the colonies. Further analysis indicates that spectra acquired from the surface of the colonies exhibit higher levels of glycogen than do the deeper layers of the colony. Additionally, the spectra from the deeper layers present with higher RNA levels than the surface layers. Therefore, the 6-h colonies with their limited heterogeneity are more suitable for inclusion in a spectral database to be used for classification purposes. These results also demonstrate that vibrational spectroscopic techniques can be useful tools for studying the nature of colony development and biofilm formation.In recent years, there has been much effort invested into the development of new techniques for the identification of microorganisms. Many of these methods are aimed at providing the clinician with more rapid identification of the microorganism responsible for infection in order to begin the appropriate course of antimicrobial treatment (1,9,15,21,27,31,44,51). The emergence of these novel methods reflects the rise in drug-resistant microorganisms, which requires that antimicrobial treatment be more effectively managed (2, 12, 28, 52). Among the new methods are those based on vibrational spectroscopic techniques, namely Fourier transform infrared (FT-IR) and Raman spectroscopies. Vibrational spectroscopic methods are reagentless procedures in which there is no need to add dyes or labels for spectral measurement. These nondestructive techniques are based on the absorption (FT-IR) or scattering (Raman) of light directed onto a sample. The amount of light absorbed or scattered depends on the molecules found within the sample and the environment in which these molecules are found. With these highly sensitive techniques, the frequency of light in the resulting spectrum provides biochemical information regarding the molecular composition and molecular structure of and molecular interaction in cells and tissues (24,55). Raman and infrared spectroscopies are complementary techniques which together can provide a more complete impression of the biochemical information within a sample. Furthermore, these two methods differ such that each is capable of providing informatio...

Rapid and accurate identification of enterococci at the species level is an essential task in clinical microbiology since these organisms have emerged as one of the leading causes of nosocomial infections worldwide. Vibrational spectroscopic techniques (infrared [IR] and Raman) could provide potential alternatives to conventional typing methods, because they are fast, easy to perform, and economical. We present a comparative study using phenotypic, genotypic, and vibrational spectroscopic techniques for typing a collection of 18 Enterococcus strains comprising six different species. Classification of the bacteria by Fourier transform (FT)-IR spectroscopy in combination with hierarchical cluster analysis revealed discrepancies for certain strains when compared with results obtained from automated phenotypic systems, such as API and MicroScan. Further diagnostic evaluation using genotypic methods-i.e., PCR of the species-specific ligase and glycopeptide resistance genes, which is limited to the identification of only four Enterococcus species and 16S RNA sequencing, the "gold standard" for identification of enterococci-confirmed the results obtained by the FT-IR classification. These results were later reproduced by three different laboratories, using confocal Raman microspectroscopy, FT-IR attenuated total reflectance spectroscopy, and FT-IR microspectroscopy, demonstrating the discriminative capacity and the reproducibility of the technique. It is concluded that vibrational spectroscopic techniques have great potential as routine methods in clinical microbiology.

An expanding body of literature suggests Raman spectroscopy is a promising tool for skin cancer diagnosis and in-vivo tumor border demarcation. The development of an in-vivo diagnostic tool is, however, hampered by the fact that construction of fiber optic probes suitable for Raman spectroscopy in the so-called fingerprint region is complicated. In contrast, the use of the high wave-number region allows for fiber optic probes with a very simple design. We investigate whether high wave-number Raman spectroscopy (2800 to 3125 cm(-1)) is able to provide sufficient information for noninvasive discrimination between basal cell carcinoma (BCC) and noninvolved skin. Using a simple fiber optic probe, Raman spectra are obtained from 19 BCC biopsy specimens and 9 biopsy specimens of perilesional skin. A linear discriminant analysis (LDA)-based tissue classification model is developed, which discriminates between BCC and noninvolved skin with high accuracy. This is a crucial step in the development of clinical dermatological applications based on fiber optic Raman spectroscopy.

Candida species are important nosocomial pathogens associated with high mortality rates. Rapid detection and identification of Candida species can guide a clinician at an early stage to prescribe antifungal drugs or to adjust empirical therapy when resistant species are isolated. Confocal Raman microspectroscopy is highly suitable for the rapid identification of Candida species, since Raman spectra can be directly obtained from microcolonies on a solid culture medium after only 6 h of culturing. In this study, we have used a set of 42 Candida strains comprising five species that are frequently encountered in clinical microbiology to test the feasibility of the technique for the rapid identification of Candida species. The procedure was started either from a culture on Sabouraud medium or from a positive vial of an automated blood culture system. Prior to Raman measurements, strains were subcultured on Sabouraud medium for 6 h to form microcolonies. Using multivariate statistical analyses, a high prediction accuracy (97 to 100%) was obtained with the Raman method. Identification with Raman microspectroscopy may therefore be significantly faster than identification with commercial identification systems that allow various species to be identified and that often require 24 to 48 h before a reliable identification is obtained. We conclude that confocal Raman microspectroscopy offers a rapid, accurate, and easy-to-use alternative for the identification of clinically relevant Candida species.

We have applied Raman spectroscopy to discriminate between nontumor and tumor bladder tissue and to determine the biochemical differences therein. Tissue samples from 15 patients were collected, and frozen sections were made for Raman spectroscopy and histology. Twenty-five pseudocolor Raman maps were created in which each color represents a cluster of spectra measured on tissue areas of similar biochemical composition. For each cluster, the cluster-averaged spectrum (CAS) was calculated and classified as tumor and nontumor in accordance to pathohistology. Unguided hierarchical clustering was applied to display heterogeneity between and within groups of nontumor and tumor CAS. A linear discriminant analysis model was developed to discriminate between CAS from tumor and nontumor. The model was tested by a leave-one-patient-out validation, 84 of the 90 CAS (93%) were correctly classified with 94% sensitivity and 92% specificity. Biochemical differences between tumor and nontumor CAS areas were analyzed by fitting spectra of pure compounds to the CAS. Nontumor CAS showed higher collagen content while tumor CAS were characterized by higher lipid, nucleic acid, protein, and glycogen content. Raman spectroscopy enabled effective discrimination between tumor and nontumor bladder tissue based on characterized biochemical differences, despite heterogeneity expressed in both tumor and nontumor CAS.