Michaela Harz

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).

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.

Minimal invasive gender determination of birds by means of UV-resonance Raman spectroscopy.

The identification of avian gender is important for prosperous breeding of birds. Since birds do not possess external genital organs, endoscopic investigations, blood analysis, and molecular biological methods are applied to determine the gender in monomorphic species. However, anesthesia and blood sampling impose stress on the examined bird and should be avoided in terms of animal protection. Here we report on the application of UV-resonance Raman spectroscopy as a minimal invasive method for gender determination of birds via an evaluation of feather pulp samples. Sample preparation for this investigation method is simple and facilitates a quick and easy analysis. The UV-resonance Raman spectra of the feather pulp sample extracts are dominated by DNA and protein signals. The different DNA content in male and female chicken allows for gender differentiation via its characteristic Raman fingerprint. The classification either to male or female chicken is ideally accomplished by support vector machines due to the fact that no unknown classes are involved. Recognition rates of about 95% were compared to less effective results of the unsupervised hierarchical cluster analysis. Within the scope of our investigations, principal component analysis was also applied to determine the important spectral regions for the classification of chickens feather pulp samples.

Fast and reliable identification of microorganisms by means of Raman spectroscopy

The identification of bacteria is necessary as fast as possible e.g. to provide an appropriate therapy for patients. Here the cultivation time should be kept to a minimum. Beside microbiological identification methods Raman spectroscopy is a valuable tool for bacteria identification. UV-resonance Raman spectroscopy enables selective monitoring of the cellular DNA/RNA content and allows for a genotaxonomic classification of the bacteria. Since UV excitation may lead to sample destruction the measurements are performed on rotated bacterial films. For a faster identification avoiding the cultivation step single bacteria analysis is necessary. Using micro-Raman spectroscopy a spatial resolution in the size range of the bacteria can be achieved. With this Raman excitation the chemical components of the whole cell are measured which leads to a phenotypic classification. For localization of bacteria inside complex matrices fluorescence labeling is achieved.

Raman-Spectroscopy for a rapid identification of single microorganisms

A rapid analysis of microorganisms is necessary for medical, pharmaceutical or food technology applications to identify harmful bacteria. Conventional identification methods require pure cultures from isolates and are often time demanding. Raman spectroscopy offers an alternative approach to identify microorganisms. With Raman microscopy it is possible to measure structures in the sub micrometer range, and therefore single bacteria cells are accessible. Micro-Raman mapping experiments proof that the bacterium shows a spatial homogeneity, since bacteria normally exhibit no compartments, therefore one spectrum of a single vegetative bacterial cell is sufficient to identify the strain. In contrary bacterial spores and yeast cells exhibit a high spatial dependency of the observed Raman spectra. For heterogeneous samples like single spores or yeast cells a mean spectrum from up to ten different positions is required to describe the complete cell. Using micro-Raman spectra of single bacterial cells and average spectra of yeast cells it is possible to create a database and identify microorganisms on species or even strain level.

Applications of Raman Spectroscopy to Virology and Microbial Analysis

This chapter reports from the utilization of Raman spectroscopic techniques like Raman microscopy, Raman optical activity (ROA), UV-resonance Raman (UVRR)-spectroscopy, surface enhanced Raman spectroscopy (SERS), and tip-enhanced Raman spectroscopy (TERS) for the investigation of viruses and microorganisms, especially bacteria and yeasts for medical and pharmaceutical applications. The application of these Raman techniques allows for the analysis of chemical components of cells and subcellular regions, as well as the monitoring of chemical differences occurring as a result of the growth of microorganisms. In addition, the interaction of microorganisms with active pharmaceutical agents can be investigated. In combination with chemometric methods Raman spectroscopy can also be applied to identify microorganisms both in micro colonies and even on single cells.

Rapid identification of single microbes by various Raman spectroscopic techniques

A fast and unambiguous identification of microorganisms is necessary not only for medical purposes but also in technical processes such as the production of pharmaceuticals. Conventional microbiological identification methods are based on the morphology and the ability of microbes to grow under different conditions on various cultivation media depending on their biochemical properties. These methods require pure cultures which need cultivation of at least 6 h but normally much longer. Recently also additional methods to identify bacteria are established e.g. mass spectroscopy, polymerase chain reaction (PCR), flow cytometry or fluorescence spectroscopy. Alternative approaches for the identification of microorganisms are vibrational spectroscopic techniques. With Raman spectroscopy a spectroscopic fingerprint of the microorganisms can be achieved. Using UV-resonance Raman spectroscopy (UVRR) macromolecules like DNA/RNA and proteins are resonantly enhanced. With an excitation wavelength of e.g. 244 nm it is possible to determine the ratio of guanine/cytosine to all DNA bases which allows a genotypic identification of microorganisms. The application of UVRR requires a large amount of microorganisms (> 106 cells) e.g. at least a micro colony. For the analysis of single cells micro-Raman spectroscopy with an excitation wavelength of 532 nm can be used. Here, the obtained information is from all type of molecules inside the cells which lead to a chemotaxonomic identification. In this contribution we show how wavelength dependent Raman spectroscopy yields significant molecular information applicable for the identification of microorganisms on a single cell level.

Identification of active fluorescence stained bacteria by Raman spectroscopy

Microorganisms can be found everywhere e.g. in food both as useful ingredients or harmful contaminations causing food spoilage. Therefore, a fast and easy to handle analysis method is needed to detect bacteria in different kinds of samples like meat, juice or air to decide if the sample is contaminated by harmful microorganisms. Conventional identification methods in microbiology require always cultivation and therefore are time consuming. In this contribution we present an analysis approach to identify fluorescence stained bacteria on strain level by means of Raman spectroscopy. The stained bacteria are highlighted and can be localized easier against a complex sample environment e.g. in food. The use of Raman spectroscopy in combination with chemometrical methods allows the identification of single bacteria within minutes.