Rapid detection of the aspergillosis biomarker triacetylfusarinine C using interference-enhanced Raman spectroscopy

Triacetylfusarinine C (TAFC) is a siderophore produced by certain fungal species and might serve as a highly useful biomarker for the fast diagnosis of invasive aspergillosis. Due to its renal elimination, the biomarker is found in urine samples of patients suffering from Aspergillus infections. Accordingly, non-invasive diagnosis from this easily obtainable body fluid is possible. Within our contribution, we demonstrate how Raman microspectroscopy enables a sensitive and specific detection of TAFC. We characterized the TAFC iron complex and its iron-free form using conventional and interference-enhanced Raman spectroscopy (IERS) and compared the spectra with the related compound ferrioxamine B, which is produced by bacterial species. Even though IERS only offers a moderate enhancement of the Raman signal, the employment of respective substrates allowed lowering the detection limit to reach the clinically relevant range. The achieved limit of detection using IERS was 0.5 ng of TAFC, which is already well within the clinically relevant range. By using an extraction protocol, we were able to detect 1.4 μg/mL TAFC via IERS from urine within less than 3 h including sample preparation and data analysis. We could further show that TAFC and ferrioxamine B can be clearly distinguished by means of their Raman spectra even in very low concentrations. © 2020, The Author(s)

Discrimination between pathogenic and non-pathogenic E. coli strains by means of Raman microspectroscopy

Bacteria can be harmless commensals, beneficial probiotics, or harmful pathogens. Therefore, mankind is challenged to detect and identify bacteria in order to prevent or treat bacterial infections. Examples are identification of species for treatment of infection in clinics and E. coli cell counting for water quality monitoring. Finally, in some instances, the pathogenicity of a species is of interest. The main strategies to investigate pathogenicity are detection of target genes which encode virulence factors. Another strategy could be based on phenotypic identification. Raman spectroscopy is a promising phenotypic method, which offers high sensitivities and specificities for the identification of bacteria species. In this study, we evaluated whether Raman microspectroscopy could be used to determine the pathogenicity of E. coli strains. We used Raman spectra of seven non-pathogenic and seven pathogenic E. coli strains to train a PCA-SVM model. Then, the obtained model was tested by identifying the pathogenicity of three additional E. coli strains. The pathogenicity of these three strains could be correctly identified with a mean sensitivity of 77%, which is suitable for a fast screening of pathogenicity of single bacterial cells. [Figure not available: see fulltext.]. © 2020, The Author(s)

Raman-spectroscopy based cell identification on a microhole array chip

Circulating tumor cells (CTCs) from blood of cancer patients are valuable prognostic markers and enable monitoring responses to therapy. The extremely low number of CTCs makes their isolation and characterization a major technological challenge. For label-free cell identification a novel combination of Raman spectroscopy with a microhole array platform is described that is expected to support high-throughput and multiplex analyses. Raman spectra were registered from regularly arranged cells on the chip with low background noise from the silicon nitride chip membrane. A classification model was trained to distinguish leukocytes from myeloblasts (OCI-AML3) and breast cancer cells (MCF-7 and BT-20). The model was validated by Raman spectra of a mixed cell population. The high spectral quality, low destructivity and high classification accuracy suggests that this approach is promising for Raman activated cell sorting

A study of Docetaxel-induced effects in MCF-7 cells by means of Raman microspectroscopy

Chemotherapies feature a low success rate of about 25%, and therefore, the choice of the most effective cytostatic drug for the individual patient and monitoring the efficiency of an ongoing chemotherapy are important steps towards personalized therapy. Thereby, an objective method able to differentiate between treated and untreated cancer cells would be essential. In this study, we provide molecular insights into Docetaxel-induced effects in MCF-7 cells, as a model system for adenocarcinoma, by means of Raman microspectroscopy combined with powerful chemometric methods. The analysis of the Raman data is divided into two steps. In the first part, the morphology of cell organelles, e.g. the cell nucleus has been visualized by analysing the Raman spectra with k-means cluster analysis and artificial neural networks and compared to the histopathologic gold standard method hematoxylin and eosin staining. This comparison showed that Raman microscopy is capable of displaying the cell morphology; however, this is in contrast to hematoxylin and eosin staining label free and can therefore be applied potentially in vivo. Because Docetaxel is a drug acting within the cell nucleus, Raman spectra originating from the cell nucleus region were further investigated in a next step. Thereby we were able to differentiate treated from untreated MCF-7 cells and to quantify the cell–drug response by utilizing linear discriminant analysis models

Comparability of Raman Spectroscopic Configurations: A Large Scale Cross-Laboratory Study

This is the final version. Available on open access from the American Chemical Society via the DOI in this recordThe variable configuration of Raman spectroscopic platforms is one of the major obstacles in establishing Raman spectroscopy as a valuable physicochemical method within real-world scenarios such as clinical diagnostics. For such real world applications like diagnostic classification, the models should ideally be usable to predict data from different setups. Whether it is done by training a rugged model with data from many setups or by a primary-replica strategy where models are developed on a 'primary' setup and the test data are generated on 'replicate' setups, this is only possible if the Raman spectra from different setups are consistent, reproducible, and comparable. However, Raman spectra can be highly sensitive to the measurement conditions, and they change from setup to setup even if the same samples are measured. Although increasingly recognized as an issue, the dependence of the Raman spectra on the instrumental configuration is far from being fully understood and great effort is needed to address the resulting spectral variations and to correct for them. To make the severity of the situation clear, we present a round robin experiment investigating the comparability of 35 Raman spectroscopic devices with different configurations in 15 institutes within seven European countries from the COST (European Cooperation in Science and Technology) action Raman4clinics. The experiment was developed in a fashion that allows various instrumental configurations ranging from highly confocal setups to fibre-optic based systems with different excitation wavelengths. We illustrate the spectral variations caused by the instrumental configurations from the perspectives of peak shifts, intensity variations, peak widths, and noise levels. We conclude this contribution with recommendations that may help to improve the inter-laboratory studies.COST (European Cooperation in Science and Technology)Portuguese Foundation for Science and TechnologyNational Research Fund of Luxembourg (FNR)China Scholarship Council (CSC)BOKU Core Facilities Multiscale ImagingDeutsche Forschungsgemeinschaft (DFG, German Research Foundation

Raman Spectroscopy to Characterize Bladder Tissue for Multidimensional Diagnostics of Cancer in Urology

Fiber optic Raman spectroscopy offers labelfree identification of cancer in the bladder under in vivo conditions. However, state-of-the-art Raman technology does not enable to scan the entire bladder wall. Our multidimensional approach within the project Uro-MDD combines panoramic 3D-image reconstruction of white light cystoscopy and fluorescence lifetime imaging to define regions of interest for Raman-assisted diagnostics. First Raman results are presented from human control and cancer bladder specimens that demonstrated how to obtain specific molecular information. Such Raman images can be used in a clinical setting to determine cancer margins and the resection status. Fiber probes are under development to translate the technique to in vivo screening

Raman-spectroscopy based cell identification on a microhole array chip

Circulating tumor cells (CTCs) from blood of cancer patients are valuable prognostic markers and enable monitoring responses to therapy. The extremely low number of CTCs makes their isolation and characterization a major technological challenge. For label-free cell identification a novel combination of Raman spectroscopy with a microhole array platform is described that is expected to support high-throughput and multiplex analyses. Raman spectra were registered from regularly arranged cells on the chip with low background noise from the silicon nitride chip membrane. A classification model was trained to distinguish leukocytes from myeloblasts (OCI-AML3) and breast cancer cells (MCF-7 and BT-20). The model was validated by Raman spectra of a mixed cell population. The high spectral quality, low destructivity and high classification accuracy suggests that this approach is promising for Raman activated cell sorting

Invited Article: Comparison of hyperspectral coherent Raman scattering microscopies for biomedical applications

Raman scattering based imaging represents a very powerful optical tool for biomedical diagnostics. Different Raman signatures obtained by distinct tissue structures and disease induced changes provoke sophisticated analysis of the hyperspectral Raman datasets. While the analysis of linear Raman spectroscopic tissue data is quite established, the evaluation of hyperspectral nonlinear Raman data has not yet been evaluated in great detail. The two most common nonlinear Raman methods are CARS (coherent anti-Stokes Raman scattering) and SRS (stimulated Raman scattering) spectroscopy. Specifically the linear concentration dependence of SRS as compared to the quadratic dependence of CARS has fostered the application of SRS tissue imaging. Here, we applied spectral processing to hyperspectral SRS and CARS data for tissue characterization. We could demonstrate for the first time that similar cluster distributions can be obtained for multispectral CARS and SRS data but that clustering is based on different spectral features due to interference effects in CARS and the different concentration dependence of CARS and SRS. It is shown that a direct combination of CARS and SRS data does not improve the clustering results