2020, Bottek, Jenny, Soun, Camille, Lill, Julia K., Dixit, Akanksha, Thiebes, Stephanie, Beerlage, Anna-Lena, Horstmann, Marius, Urbanek, Annett, Heuer, Heike, Uszkoreit, Julian, Eisenacher, Martin, Bracht, Thilo, Sitek, Barbara, Hoffmann, Franziska, Vijitha, Nirojah, von Eggeling, Ferdinand, Engel, Daniel R.

The urothelium of the urinary bladder represents the first line of defense. However, uropathogenic E. coli (UPEC) damage the urothelium and cause acute bacterial infection. Here, we demonstrate the crosstalk between macrophages and the urothelium stimulating macrophage migration into the urothelium. Using spatial proteomics by MALDI-MSI and LC-MS/MS, a novel algorithm revealed the spatial activation and migration of macrophages. Analysis of the spatial proteome unravelled the coexpression of Myo9b and F4/80 in the infected urothelium, indicating that macrophages have entered the urothelium upon infection. Immunofluorescence microscopy additionally indicated that intraurothelial macrophages phagocytosed UPEC and eliminated neutrophils. Further analysis of the spatial proteome by MALDI-MSI showed strong expression of IL-6 in the urothelium and local inhibition of this molecule reduced macrophage migration into the urothelium and aggravated the infection. After IL-6 inhibition, the expression of matrix metalloproteinases and chemokines, such as CX3CL1 was reduced in the urothelium. Accordingly, macrophage migration into the urothelium was diminished in the absence of CX3CL1 signaling in Cx3cr1gfp/gfp mice. Conclusively, this study describes the crosstalk between the infected urothelium and macrophages through IL-6-induced CX3CL1 expression. Such crosstalk facilitates the relocation of macrophages into the urothelium and reduces bacterial burden in the urinary bladder. © 2020, The Author(s).

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.

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.