Susann Meisel

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.

Raman Spectroscopic Detection of Anthrax Endospores in Powder Samples

Polymerase chain reaction (PCR) is becoming the method of choice for detecting microorganisms concealed in complex matrices or “hoax materials” like household dry powders, food, and soil.[1] However, adding samples directly to a PCR reaction is in most cases not possible because of the presence of PCR inhibitors in the sample.[2] Thus, in order to reliably use PCR, one must either enrich the culture prior to the analysis, which is time-consuming for fastidious organisms, or extract the total DNA directly from the sample, which requires an extraction technique capable of processing different sample types. However, since most DNA extraction methods are often not generic in that sense, they lead to unreliable DNA recovery yields.[2, 3]

Identification of meat-associated pathogens via Raman microspectroscopy

The development of fast and reliable sensing techniques to detect food-borne microorganisms is a permanent concern in food industry and health care. For this reason, Raman microspectroscopy was applied to rapidly detect pathogens in meat, which could be a promising supplement to currently established methods. In this context, a spectral database of 19 species of the most important harmful and non-pathogenic bacteria associated with meat and poultry was established. To create a meat-like environment the microbial species were prepared on three different agar types. The whole amount of Raman data was taken as a basis to build up a three level classification model by means of support vector machines. Subsequent to a first classifier that differentiates between Raman spectra of Gram-positive and Gram-negative bacteria, two decision knots regarding bacterial genus and species follow. The different steps of the classification model achieved accuracies in the range of 90.6%-99.5%. This database was then challenged with independently prepared test samples. By doing so, beef and poultry samples were spiked with different pathogens associated with food-borne diseases and then identified. The test samples were correctly assigned to their genus and for the most part down to the species-level i.e. a differentiation from closely-related non-pathogenic members was achieved.

Raman Spectroscopy as a Potential Tool for Detection of Brucella spp. in Milk

ABSTRACT Detection of Brucella, causing brucellosis, is very challenging, since the applied techniques are mostly time-demanding and not standardized. While the common detection system relies on the cultivation of the bacteria, further classical typing up to the biotype level is mostly based on phenotypic or genotypic characteristics. The results of genotyping do not always fit the existing taxonomy, and misidentifications between genetically closely related genera cannot be avoided. This situation gets even worse, when detection from complex matrices, such as milk, is necessary. For these reasons, the availability of a method that allows early and reliable identification of possible Brucella isolates for both clinical and epidemiological reasons would be extremely useful. We evaluated micro-Raman spectroscopy in combination with chemometric analysis to identify Brucella from agar plates and directly from milk: prior to these studies, the samples were inactivated via formaldehyde treatment to ensure a higher working safety. The single-cell Raman spectra of different Brucella, Escherichia, Ochrobactrum, Pseudomonas, and Yersinia spp. were measured to create two independent databases for detection in media and milk. Identification accuracies of 92% for Brucella from medium and 94% for Brucella from milk were obtained while analyzing the single-cell Raman spectra via support vector machine. Even the identification of the other genera yielded sufficient results, with accuracies of >90%. In summary, micro-Raman spectroscopy is a promising alternative for detecting Brucella. The measurements we performed at the single-cell level thus allow fast identification within a few hours without a demanding process for sample preparation.

Raman spectroscopy-compatible inactivation method for pathogenic endospores.

ABSTRACT Micro-Raman spectroscopy is a fast and sensitive tool for the detection, classification, and identification of biological organisms. The vibrational spectrum inherently serves as a fingerprint of the biochemical composition of each bacterium and thus makes identification at the species level, or even the subspecies level, possible. Therefore, microorganisms in areas susceptible to bacterial contamination, e.g., clinical environments or food-processing technology, can be sensed. Within the scope of point-of-care-testing also, detection of intentionally released biosafety level 3 (BSL-3) agents, such as Bacillus anthracis endospores, or their products is attainable. However, no Raman spectroscopy-compatible inactivation method for the notoriously resistant Bacillus endospores has been elaborated so far. In this work we present an inactivation protocol for endospores that permits, on the one hand, sufficient microbial inactivation and, on the other hand, the recording of Raman spectroscopic signatures of single endospores, making species-specific identification by means of highly sophisticated chemometrical methods possible. Several physical and chemical inactivation methods were assessed, and eventually treatment with 20% formaldehyde proved to be superior to the other methods in terms of sporicidal capacity and information conservation in the Raman spectra. The latter fact has been verified by successfully using self-learning machines (such as support vector machines or artificial neural networks) to identify inactivated B. anthracis-related endospores with adequate accuracies within the range of the limited model database employed.

Shedding light on host niches: label‐free in situ detection of Mycobacterium gordonae via carotenoids in macrophages by Raman microspectroscopy

Macrophages are the primary habitat of pathogenic mycobacteria during infections. Current research about the host–pathogen interaction on the cellular level is still going on. The present study proves the potential of Raman microspectroscopy as a label‐free and non‐invasive method to investigate intracellular mycobacteria in situ. Therefore, macrophages were infected with Mycobacterium gordonae, a mycobacterium known to cause inflammation linked to intracellular survival in macrophages. Here, we show that Raman maps provided spatial and spectral information about the position of bacteria within determined cell margins of macrophages in two‐dimensional scans and in three‐dimensional image stacks. Simultaneously, the relative intracellular concentration and distributions of cellular constituents such as DNA, proteins and lipids provided phenotypic information about the infected macrophages. Locations of bacteria outside or close to the outer membrane of the macrophages were notably different in their spectral pattern compared with intracellular once. Furthermore, accumulations of bacteria inside of macrophages exhibit distinct spectral/molecular information because of the chemical composition of the intracellular microenvironment. The data show that the connection of microscopically and chemically gained information provided by Raman microspectroscopy offers a new analytical way to detect and to characterize the mycobacterial infection of macrophages.

Raman spectroscopic identification of Mycobacterium tuberculosis.

In this study, Raman microspectroscopy has been utilized to identify mycobacteria to the species level. Because of the slow growth of mycobacteria, the per se cultivation-independent Raman microspectroscopy emerges as a perfect tool for a rapid on-the-spot mycobacterial diagnostic test. Special focus was laid upon the identification of Mycobacterium tuberculosis complex (MTC) strains, as the main causative agent of pulmonary tuberculosis worldwide, and the differentiation between pathogenic and commensal nontuberculous mycobacteria (NTM). Overall the proposed model considers 26 different mycobacteria species as well as antibiotic susceptible and resistant strains. More than 8800 Raman spectra of single bacterial cells constituted a spectral library, which was the foundation for a two-level classification system including three support vector machines. Our model allowed the discrimination of MTC samples in an independent validation dataset with an accuracy of 94% and could serve as a basis to further improve Raman microscopy as a first-line diagnostic point-of-care tool for the confirmation of tuberculosis disease.

Raman spectroscopic approach for the cultivation-free identification of microbes

In the last years the identification of microorganisms by means of different IR and Raman spectroscopic techniques has become quite popular. Most of the studies however apply the various vibrational spectroscopic methods to bulk samples which require at least a short cultivation time of several hours. Nevertheless, bulk identification methods achieve high classification rates which enable even the discrimination between closely related strains or the distinction between resistance capabilities.

Bacterial identification in real samples by means of Micro-Raman Spectroscopy

Pathogen detection is essential without time delay especially for severe diseases like sepsis. Here, the survival rate is dependent on a prompt antibiosis. For sepsis three hours after the onset of shock the survival rate of the patient drops below 60 %. Unfortunately, the results from standard diagnosis methods like PCR or microbiology can normally be received after 12 or 36 h, respectively. Therefore diagnosis methods which require less cultivation or even no cultivation at all have to be established for medical diagnosis. Here, Raman spectroscopy, as a vibrational spectroscopic method, is a very sensitive and selective approach and monitors the biochemical composition of the investigated sample. Applying micro-Raman spectroscopy allows for a spatial resolution below 1 μm and is therefore in the size range of bacteria. Raman spectra of bacteria depend on the physiological status. Therefore, the databases require the inclusion of the necessary environmental parameters such as temperature, pH, nutrition, etc. Such large databases therefore require a specialized chemometric approach, since the variation between different strains is small. In this contribution we will demonstrate the capability of Raman spectroscopy to identify pathogens without cultivation even from real environmental or medical samples.

Raman spectroscopic characterization of single cells

Here we present our latest results concerning the application of Raman microspectroscopy in combination with innovative chemometrics to characterize biological cells. The first part of this manuscript deals with the application of micro-Raman spectroscopy to identify microbial contaminations while the main focus within the second part of this presentation is concerned with Raman studies on eukaryotic cells where we will report about the development of an algorithm to differentiate between breast cancer cells and normal epithelial cells.