Wilm Schumacher

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.

The influence of intracellular storage material on bacterial identification by means of Raman spectroscopy

AbstractPrevious studies dealing with bacterial identification by means of Raman spectroscopy have demonstrated that micro-Raman is a suitable technique for single-cell microbial identification. Raman spectra yield fingerprint-like information about all chemical components within one cell, and combined with multivariate methods, differentiation down to species or even strain level is possible. Many microorganisms may accumulate high amounts of polyhydroxyalkanoates (PHA) as carbon and energy storage materials within the cell and the Raman bands of PHA might impede the identification and differentiation of cells. To date, the identification by means of Raman spectroscopy have never been tested on bacteria which had accumulated PHA. Therefore, the aim of this study is to investigate the effect of intracellular polymer accumulation on the bacterial identification rate. Combining fluorescence imaging and Raman spectroscopy, we identified polyhydroxybutyrate (PHB) as a storage polymer accumulating in the investigated cells. The amount of energy storage material present within the cells was dependent on the physiological status of the microorganisms and strongly influenced the identification results. Bacteria in the stationary phase formed granules of crystalline PHB, which obstructed the Raman spectroscopic identification of bacterial species. The Raman spectra of bacteria in the exponential phase were dominated by signals from the storage material. However, the bands from proteins, lipids, and nucleic acids were not completely obscured by signals from PHB. Cells growing under either oxic or anoxic conditions could also be differentiated, suggesting that changes in Raman spectra can be interpreted as an indicator of different metabolic pathways. Although the presence of PHB induced severe changes in the Raman spectra, our results suggest that Raman spectroscopy can be successfully used for identification as long as the bacteria are not in the stationary phase. FigureStained bacteria with or without PHB within the cells, and the corresponding Raman spectra.

From bulk to single-cell classification of the filamentous growing Streptomyces bacteria by means of Raman spectroscopy.

Classification of Raman spectra recorded from single cells is commonly applied to bacteria that exhibit small sizes of approximately 1 to 2 μm. Here, we study the possibility to adopt this classification approach to filamentous bacteria of the genus Streptomyces. The hyphae can reach extensive lengths of up to 35 μm, which can correspond to a single cell identified in light microscopy. The classification of Raman bulk spectra will be demonstrated. Here, ultraviolet resonance Raman (UV RR) spectroscopy is chosen to classify six Streptomyces species by the application of a tree-like classifier. For each knot of the hierarchical classifier, estimated classification accuracies of over 94% are accomplished. In contrast to the classification of bulk spectra, the classification of single-cell spectra requires a homogenous substance distribution within the cell. Consequently, the bacterial cell chemistry can be represented by one individual spectrum. This requirement is not fulfilled when different spectra are processed from different locations within the cell. Bacteria of the investigated genus Streptomyces exhibit, besides the normal bacterial spectra, lipid-rich spectra. The occurrence of lipid enrichment depends on culture age and nutrition availability. With this study, we investigate the cell substance distribution, especially of lipid-rich fractions. The classification utilizing a tree-like classifier is also applied to the Streptomyces single-cell spectra, resulting in classification accuracies between 80 and 93% for the investigated Streptomyces species.

Identification of water-conditioned Pseudomonas aeruginosa by Raman microspectroscopy on a single cell level.

The identification of Pseudomonas aeruginosa from samples of bottled natural mineral water by the analysis of subcultures is time consuming and other species of the authentic Pseudomonas group can be a problem. Therefore, this study aimed to investigate the influence of different aquatic environmental conditions (pH, mineral content) and growth phases on the cultivation-free differentiation between water-conditioned Pseudomonas spp. by applying Raman microspectroscopy. The final dataset was comprised of over 7500 single-cell Raman spectra, including the species Pseudomonas aeruginosa, P. fluorescens and P. putida, in order to prove the feasibility of the introduced approach. The collection of spectra was standardized by automated measurements of viable stained bacterial cells. The discrimination was influenced by the growth phase at the beginning of the water adaptation period and by the type of mineral water. Different combinations of the parameters were tested and they resulted in accuracies of up to 85% for the identification of P. aeruginosa from independent samples by applying chemometric analysis.

Self-defining tree-like classifiers for interpretation of Raman spectroscopic experiments

In this contribution, a technique is proposed to create a data‐driven interpretation of a given chemometric analysis of a Raman dataset. In real‐world applications, the chemometric analysis is fixed by some external measurement, for example, a legal standard, or a set of fixed goals. Thus, the exact chemometric work flow is fixed because of those goals. However, a further optimization, for example, of the measurement itself relies on an interpretation of the resulting chemometric analysis. For this purpose, a data‐driven analysis of the chemometric analysis itself has to be carried out. This contribution tries to achieve that goal by combining two methods. The first proposed technique is the calculation of the so‐called importance map, which allows the computation of the importance of every channel for a given model and a given dataset. This importance map is constructed after the complete result of an out‐of‐bag (OOB) validation and the decrease of accuracy by randomized channels. The second technique is the growing of the optimal decision tree based on the action of the model used for chemometric analysis. By this way, a clustering is achieved on which by binary classifiers, the optimal decision tree is grown. This tree can be interpreted as dividing the whole dataset into meta clusters. Combining these techniques, a new way of interpreting datasets based on the chosen model is proposed. This combination closes the gap between chemometric analysis and the need for interpretation. Copyright

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.

The multifunctional application of microfluidic lab-on-a-chip surface enhanced Raman spectroscopy (LOC-SERS) within the field of bioanalytics

This contribution will present a variety of applications of lab-on-a-chip surface enhanced Raman spectroscopy in the field of bioanalytic. Beside the quantification and online monitoring of drugs and pharmaceuticals, determination of enzyme activity and discrimination of bacteria are successfully carried out utilizing LOC-SERS. The online-monitoring of drugs using SERS in a microfluidic device is demonstrated for nicotine. The enzyme activity of thiopurine methyltransferase (TPMT) in lysed red blood cells is determined by SERS in a lab-on-a-chip device. To analyse the activity of TPMT the metabolism of 6-mercaptopurine to 6-methylmercaptopurine is investigated. The discrimination of bacteria on strain level is carried out with different E. coli strains. For the investigations, the bacteria are busted by ultra sonic to achieve a high information output. This sample preparation provides the possibility to detect SERS spectra containing information of the bacterial cell walls as well as of the cytoplasm. This contribution demonstrates the great potential of LOC-SERS in the field of bioanalytics.

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.