Petra Rösch

Chemotaxonomic Identification of Single Bacteria by Micro-Raman Spectroscopy: Application to Clean-Room-Relevant Biological Contaminations

ABSTRACT Microorganisms, such as bacteria, which might be present as contamination inside an industrial food or pharmaceutical clean room process need to be identified on short time scales in order to minimize possible health hazards as well as production downtimes causing financial deficits. Here we describe the first results of single-particle micro-Raman measurements in combination with a classification method, the so-called support vector machine technique, allowing for a fast, reliable, and nondestructive online identification method for single bacteria.

Vibrational spectroscopy—A powerful tool for the rapid identification of microbial cells at the single-cell level

Rapid microbial detection and identification with a high grade of sensitivity and selectivity is a great and challenging issue in many fields, primarily in clinical diagnosis, pharmaceutical, or food processing technology. The tedious and time‐consuming processes of current microbiological approaches call for faster ideally on‐line identification techniques. The vibrational spectroscopic techniques IR absorption and Raman spectroscopy are noninvasive methods yielding molecular fingerprint information; thus, allowing for a fast and reliable analysis of complex biological systems such as bacterial or yeast cells. In this short review, we discuss recent vibrational spectroscopic advances in microbial identification of yeast and bacterial cells for bulk environment and single‐cell analysis. IR absorption spectroscopy enables a bulk analysis whereas micro‐Raman‐spectroscopy with excitation in the near infrared or visible range has the potential for the analysis of single bacterial and yeast cells. The inherently weak Raman signal can be increased up to several orders of magnitude by applying Raman signal enhancement methods such as UV‐resonance Raman spectroscopy with excitation in the deep UV region, surface enhanced Raman scattering, or tip‐enhanced Raman scattering.

Micro-Raman spectroscopic identification of bacterial cells of the genus Staphylococcus and dependence on their cultivation conditions

Microbial contamination is not only a medical problem, but also plays a large role in pharmaceutical clean room production and food processing technology. Therefore many techniques were developed to achieve differentiation and identification of microorganisms. Among these methods vibrational spectroscopic techniques (IR, Raman and SERS) are useful tools because of their rapidity and sensitivity. Recently we have shown that micro-Raman spectroscopy in combination with a support vector machine is an extremely capable approach for a fast and reliable, non-destructive online identification of single bacteria belonging to different genera. In order to simulate different environmental conditions we analyzed in this contribution different Staphylococcus strains with varying cultivation conditions in order to evaluate our method with a reliable dataset. First, micro-Raman spectra of the bulk material and single bacterial cells that were grown under the same conditions were recorded and used separately for a distinct chemotaxonomic classification of the strains. Furthermore Raman spectra were recorded from single bacterial cells that were cultured under various conditions to study the influence of cultivation on the discrimination ability. This dataset was analyzed both with a hierarchical cluster analysis (HCA) and a support vector machine (SVM).

How to pre-process Raman spectra for reliable and stable models?

Raman spectroscopy in combination with chemometrics is gaining more and more importance for answering biological questions. This results from the fact that Raman spectroscopy is non-invasive, marker-free and water is not corrupting Raman spectra significantly. However, Raman spectra contain despite Raman fingerprint information other contributions like fluorescence background, Gaussian noise, cosmic spikes and other effects dependent on experimental parameters, which have to be removed prior to the analysis, in order to ensure that the analysis is based on the Raman measurements and not on other effects. Here we present a comprehensive study of the influence of pre-processing procedures on statistical models. We will show that a large amount of possible and physically meaningful pre-processing procedures leads to bad results. Furthermore a method based on genetic algorithms (GAs) is introduced, which chooses the spectral pre-processing according to the carried out analysis task without trying all possible pre-processing approaches (grid-search). This was demonstrated for the two most common tasks, namely for a multivariate calibration model and for two classification models. However, the presented approach can be applied in general, if there is a computational measure, which can be optimized. The suggested GA procedure results in models, which have a higher precision and are more stable against corrupting effects.

A comparative Raman and CARS imaging study of colon tissue

An experimental evaluation of the information content of two complimentary techniques, linear Raman and coherent anti-Stokes Raman scattering (CARS) microscopy, is presented. CARS is a nonlinear variant of Raman spectroscopy that enables rapid acquisition of images within seconds in combination with laser scanning microscopes. CARS images were recorded from thin colon tissue sections at 2850, 1660, 1450 and 1000 cm(-1) and compared with Raman images. Raman images were obtained from univariate and multivariate (k-means clustering) methods, whereas all CARS images represent univariate results. Variances within tissue sections could be visualized in chemical maps of CARS and Raman images. However, identification of tissue types and characterization of variances between different tissue sections were only possible by analysis of cluster mean spectra, obtained from k-means cluster analysis. This first comparison establishes the foundation for further development of the CARS technology to assess tissue.

Identification of secondary metabolites in medicinal and spice plants by NIR-FT-Raman microspectroscopic mapping

This paper demonstrates the special potential of vibrational NIR FT Raman microspectroscopy for the study of fennel fruits, chamomile inflorescence and curcuma roots to obtain detailed information about their microstructure and chemical composition. Microscopic Raman maps of fennel fruits demonstrate that anethole, which is the main essential oil component, is present in the whole mericarp with highest concentration at the top of the fruit. In situ measurements obtained of the essential oil cells are dominated by two bands observed at 1657 cm(-1) and 1609 cm(-1) which are characteristic for anethole. Raman images of chamomile inflorescence show that spiroethers, identified by significant bands between 2150 and 2250 cm(-1), are accumulated in the middle part of the flower head. Due to the intense curcumin bands in the Raman spectrum of curcuma root, the distribution of this dyeing substance can be clearly determined; highest concentration of curcumin was observed on the core of the root.

Direct analysis of clinical relevant single bacterial cells from cerebrospinal fluid during bacterial meningitis by means of micro-Raman spectroscopy

Bacterial meningitis is a relevant public health concern. Despite the availability of modern treatment strategies it is still a life-threatening disease that causes significant morbidity and mortality. Therefore, an initial treatment approach plays an important role. For in-time identification of specific bacterial pathogens of the cerebrospinal fluid (CSF) and emerged antimicrobial and adjunctive treatment, microbiological examination is of major importance. This contribution spotlights the potential of micro-Raman spectroscopy as a biomedical assay for direct analysis of bacteria in cerebrospinal fluid of patients with bacterial meningitis. The influence of miscellaneous artificial environments on several bacterial species present during bacterial meningitis was studied by means of Raman spectroscopy. The application of chemometric data interpretation via hierarchical cluster analysis (HCA) allows for the differentiation of in vitro cultured bacterial cells and can also be achieved on a single cell level. Moreover as proof of principle the investigation of a CSF sample obtained from a patient with meningococcal meningitis showed that the cerebrospinal fluid matrix does not mask the Raman spectrum of a bacterial cell notably since via chemometric analysis with HCA an identification of N. meningitidis cells from patients with bacterial meningitis could be achieved.

Isolation and identification of bacteria by means of Raman spectroscopy

Bacterial detection is a highly topical research area, because various fields of application will benefit from the progress being made. Consequently, new and innovative strategies which enable the investigation of complex samples, like body fluids or food stuff, and improvements regarding the limit of detection are of general interest. Within this review the prospects of Raman spectroscopy as a reliable tool for identifying bacteria in complex samples are discussed. The main emphasis of this work is on important aspects of applying Raman spectroscopy for the detection of bacteria like sample preparation and the identification process. Several approaches for a Raman compatible isolation of bacterial cells have been developed and applied to different matrices. Here, an overview of the limitations and possibilities of these methods is provided. Furthermore, the utilization of Raman spectroscopy for diagnostic purposes, food safety and environmental issues is discussed under a critical view.

Localizing and identifying living bacteria in an abiotic environment by a combination of Raman and fluorescence microscopy.

A fast, easy, and reliable identification of microorganisms is indispensable in many fields such as medicine, food production, or the pharmaceutical industry. However, in native samples, biotic particles often appear together with abiotic particles. Therefore, it is a prerequisite that biotic particles can be differentiated from abiotic particles appearing in the identification setup. In addition, for many applications, not all microorganisms are of interest but only the living ones. Therefore, in this contribution, different bacteria species were stained with a live/dead staining kit (SYTO 9 and propidium iodide) prior to Raman spectroscopic identification. Since only living and dead microorganisms are getting stained by SYTO 9 or PI, biotic particles can easily be spotted and localized in-between abiotic particles. By using a Raman laser excitation wavelength outside the absorption band of the dye, fluorescence-free Raman spectra were obtained. The living cells were identified by means of Raman spectroscopy in combination with a support vector machine. Furthermore, the localization of bacterial cells in a mix of abiotic particles is demonstrated.

The application of a SERS fiber probe for the investigation of sensitive biological samples

The applicability of an etched and silver or gold coated SERS fiber probe in combination with a commercially available laboratory micro-Raman setup or a home built mobile micro-Raman setup to perform on-site field measurements was evaluated and successfully tested on different biological samples. The SERS fiber probe allows one to perform measurements with high spatial resolution. Simultaneously, the laser power used for Raman spectroscopy on biological samples as compared with conventional Raman experiments can be reduced by more than two orders of magnitude. This experimental arrangement was tested to investigate sensitive biological samples like mint plants (Bergamot mint, spear mint) and citrus fruits (kumquat). Furthermore, traces of fungicides on wine leaves were detected by means of such a SERS fiber probe setup.