Search Results
Benchmark datasets for 3D MALDI- and DESI-imaging mass spectrometry
2015, Oetjen, Janina, Veselkov, Kirill, Watrous, Jeramie, McKenzie, James S., Becker, Michael, Hauberg-Lotte, Lena, Kobarg, Jan Hendrik, Strittmatter, Nicole, Mróz, Anna K., Hoffmann, Franziska, Trede, Dennis, Palmer, Andrew, Schiffler, Stefan, Steinhorst, Klaus, Aichler, Michaela, Goldin, Robert, Guntinas-Lichius, Orlando, von Eggeling, Ferdinand, Thiele, Herbert, Maedler, Kathrin, Walch, Axel, Maass, Peter, Dorrestein, Pieter C., Takats, Zoltan, Alexandrov, Theodore
Background: Three-dimensional (3D) imaging mass spectrometry (MS) is an analytical chemistry technique for the 3D molecular analysis of a tissue specimen, entire organ, or microbial colonies on an agar plate. 3D-imaging MS has unique advantages over existing 3D imaging techniques, offers novel perspectives for understanding the spatial organization of biological processes, and has growing potential to be introduced into routine use in both biology and medicine. Owing to the sheer quantity of data generated, the visualization, analysis, and interpretation of 3D imaging MS data remain a significant challenge. Bioinformatics research in this field is hampered by the lack of publicly available benchmark datasets needed to evaluate and compare algorithms. Findings: High-quality 3D imaging MS datasets from different biological systems at several labs were acquired, supplied with overview images and scripts demonstrating how to read them, and deposited into MetaboLights, an open repository for metabolomics data. 3D imaging MS data were collected from five samples using two types of 3D imaging MS. 3D matrix-assisted laser desorption/ionization imaging (MALDI) MS data were collected from murine pancreas, murine kidney, human oral squamous cell carcinoma, and interacting microbial colonies cultured in Petri dishes. 3D desorption electrospray ionization (DESI) imaging MS data were collected from a human colorectal adenocarcinoma. Conclusions: With the aim to stimulate computational research in the field of computational 3D imaging MS, selected high-quality 3D imaging MS datasets are provided that could be used by algorithm developers as benchmark datasets.
Comparison of hyperspectral coherent Raman scattering microscopies for biomedical applications
2018, Bocklitz, Thomas W., Meyer, Tobias, Schmitt, Michael, Rimke, Ingo, Hoffmann, Franziska, von Eggeling, Ferdinand, Ernst, G., Guntinas-Lichius, Orlando, Popp, Jürgen
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.