Raman spectroscopic detection of physiology changes in plasmid-bearing Escherichia coli with and without antibiotic treatment.

Bacterial resistances against antibiotics are increasingly problematic for medical treatment of pathogenic bacteria, e.g., in hospitals. Resistances are, among other genes, often encoded on plasmids which can be transmitted between bacteria not only within one species, but also between different species, genera, and families. The plasmid pDrive is transformed into bacteria of the model strain Escherichia coli DH5α. Within this investigation, we applied micro-Raman spectroscopy with two different excitation wavelengths in combination with support vector machine (SVM) and linear discriminant analysis (LDA) to differentiate between bacterial cultures according to their cultural plasmid content. Recognition rates of about 92% and 90% are achieved by Raman excitation at 532 and 244 nm, respectively. The SVM loadings reveal that the pDrive transformed bacterial cultures exhibit a higher DNA content compared to the untransformed cultures. To elucidate the influence of the antibiotic, ampicillin-treated cultures are also comprised within this study and are classified with rates of about 97% and 100% for 532 and 244 nm Raman excitation, respectively. The Raman spectra recorded with 532 nm excitation wavelength show differences of the secondary protein structure and enhanced stress-related respiration rates for the ampicillin-treated cultures. Independent cultural replicates of either ampicillin-challenged or non-challenged cultures are successfully identified with identification rates of over 90%.

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

Previous 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.

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.

Evaluation of Colloids and Activation Agents for Determination of Melamine Using UV-SERS.

UV-SERS measurements offer a great potential for environmental or food (detection of food contaminats) analytics. Here, the UV-SERS enhancement potential of various kinds of metal colloids, such as Pd, Pt, Au, Ag, Au-Ag core-shell, and Ag-Au core-shell with different shapes and sizes, were studied using melamine as a test molecule. The influence of different activation (KF, KCl, KBr, K(2)SO(4)) agents onto the SERS activity of the nanomaterials was investigated, showing that the combination of a particular nanoparticle with a special activation agent is extremely crucial for the observed SERS enhancement. In particular, the size dependence of spherical nanoparticles of one particular metal on the activator has been exploited. By doing so, it could be shown that the SERS enhancement increases or decreases for increasing or decreasing size of a nanoparticle, respectively. Overall, the presented results demonstrate the necessity to adjust the nanoparticle size and the activation agent for different experiments in order to achieve the best possible UV-SERS results.

Towards a fast, high specific and reliable discrimination of bacteria on strain level by means of SERS in a microfluidic device.

The interest in a fast, high specific and reliable detection method for bacteria identification is increasing. We will show that the application of vibrational spectroscopy is feasible for the validation of bacteria in microfluidic devices. For this purpose, reproducible and specific spectral pattern as well as the establishment of large databases are essential for statistical analysis. Therefore, short recording times are beneficial concerning the time aspect of fast identification. We will demonstrate that the requirements can be fulfilled by measuring ultrasonic busted bacteria by means of microfluidic lab-on-a-chip based SERS. With the applied sample preparation, high specificity and reproducibility of the spectra are achieved. Taking advantage of the SERS enhancement, the spectral recording time is reduced to 1 s and a database of 11,200 spectra is established for a model system E. coli including nine different strains. The validation of the bacteria on strain level is achieved accomplishing SVM accuracies of 92%. Within this contribution the potential of our approach of bacterial identification for future application is discussed, focusing on the time-benefit and the combination with other microfluidic applications.

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.