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 detection and identification of Burkholderia mallei and Burkholderia pseudomallei in feedstuff.

Burkholderia mallei (the etiologic agent of glanders in equines and rarely humans) and Burkholderia pseudomallei, causing melioidosis in humans and animals, are designated category B biothreat agents. The intrinsically high resistance of both agents to many antibiotics, their potential use as bioweapons, and their low infectious dose, necessitate the need for rapid and accurate detection methods. Current methods to identify these organisms may require up to 1 week, as they rely on phenotypic characteristics and an extensive set of biochemical reactions. In this study, Raman microspectroscopy, a cultivation-independent typing technique for single bacterial cells with the potential for being a rapid point-of-care analysis system, is evaluated to identify and differentiate B. mallei and B. pseudomallei within hours. Here, not only broth-cultured microbes but also bacteria isolated out of pelleted animal feedstuff were taken into account. A database of Raman spectra allowed a calculation of classification functions, which were trained to differentiate Raman spectra of not only both pathogens but also of five further Burkholderia spp. and four species of the closely related genus Pseudomonas. The developed two-stage classification system comprising two support vector machine (SVM) classifiers was then challenged by a test set of 11 samples to simulate the case of a real-world-scenario, when "unknown samples" are to be identified. In the end, all test set samples were identified correctly, even if the contained bacterial strains were not incorporated in the database before or were isolated out of animal feedstuff. Specifically, the five test samples bearing B. mallei and B. pseudomallei were correctly identified on species level with accuracies between 93.9 and 98.7%. The sample analysis itself requires no biomass enrichment step prior to the analysis and can be performed under biosafety level 1 (BSL 1) conditions after inactivating the bacteria with formaldehyde.

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.

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.

Assessment of two isolation techniques for bacteria in milk towards their compatibility with Raman spectroscopy.

The identification of single microorganism in food samples by conventional plating techniques or molecular genetic methods requires a time consuming enrichment step. Raman spectroscopy in combination with a suitable extraction method however offers the possibility to rapidly identify bacteria on a single cell level. Here we evaluate the two well-known bacteria extraction methods from milk: "buoyant density centrifugation" and "enzymatic milk clearing" towards their recovery efficiency and their compatibility with Raman spectroscopy for a rapid identification of microorganisms in milk. The achieved recovery yields are slightly better compared to those which are already applied for food investigations, where a loss of one order of magnitude is usually reached. For example, buoyant density centrifugation allows collecting up to 35% of the milk-spiked microorganisms. To prove the suitability of the isolation techniques for use in combination with the spectroscopic approach, a small Raman database has been created by recording Raman spectra of well-known contaminants in dairy products. Two subspecies of Escherichia coli and three different Pseudomonas species, which were inoculated to UHT (ultra-high-temperature processed) milk and afterwards extracted by the two techniques mentioned above, were analysed. At a first glance, grave spectral artefacts caused by the matrix itself or especially by the extraction techniques were not obvious. But via chemometric analysis, it could be shown that these factors noticeably influence the identification rates: while the samples prepared via milk clearing did not provide sufficient identification results, buoyant density centrifugation allows an identification of the investigated species with an overall accuracy of 91% in combination with linear discriminant analysis.

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.

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.