An dieser Stelle werden wir die wissenschaftlichen Veröffentlichungen zu den Forschungsergebnissen im Rahmen von M2Aind nennen.
Jan-Hinrich Rabe1,2, Denis A. Sammour1,2, Sandra Schulz1,2, Bogdan Munteanu1,2, Martina
Ott3, Katharina Ochs3,5, Peter Hohenberger4, Alexander Marx4, Michael Platten3,4,
Christiane A. Opitz5,6, Daniel S. Ory7 & Carsten Hopf1,2
1Center for Applied Research in Applied Biomedical Mass Spectrometry (ABIMAS), Mannheim University of Applied Sciences, Mannheim, Germany
2Institute of Medical Technology, Heidelberg University and Mannheim University of Applied Sciences, Mannheim, Germany
3German Cancer Consortium (DKTK) CCU Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center (DKFZ), Heidelberg, Germany
4University Medical Center Mannheim of Heidelberg University, Mannheim, Germany
5Brain Cancer Metabolism Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
6Department of Neurology and National Center of Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
7Diabetic Cardiovascular Disease Center and Department of Medicine, Washington University School of Medicine
In: Scientific Reports 8, Article number: 313 (2018)
Abstract: Multimodal imaging combines complementary platforms for spatially resolved tissue analysis that are poised for application in life science and personalized medicine. Unlike established clinical in vivo multimodality imaging, automated workflows for in-depth multimodal molecular ex vivo tissue analysis that combine the speed and ease of spectroscopic imaging with molecular details provided by mass spectrometry imaging (MSI) are lagging behind. Here, we present an integrated approach that utilizes non-destructive Fourier transform infrared (FTIR) microscopy and matrix assisted laser desorption/ionization (MALDI) MSI for analysing single-slide tissue specimen. We show that FTIR microscopy can automatically guide high-resolution MSI data acquisition and interpretation without requiring prior histopathological tissue annotation, thus circumventing potential human-annotation-bias while achieving >90% reductions of data load and acquisition time. We apply FTIR imaging as an upstream modality to improve accuracy of tissue-morphology detection and to retrieve diagnostic molecular signatures in an automated, unbiased and spatially aware manner. We show the general applicability of multimodal FTIR-guided MALDI-MSI by demonstrating precise tumor localization in mouse brain bearing glioma xenografts and in human primary gastrointestinal stromal tumors. Finally, the presented multimodal tissue analysis method allows for morphology-sensitive lipid signature retrieval from brains of mice suffering from lipidosis caused by Niemann-Pick type C disease.