Mannheim University of Applied Sciences, DE
Mannheim University of Applied Sciences, Applied Biomedical Mass Spectrometry Center (ABIMAS). ABIMAS, affiliated with the Institute of Medical Technology of University of Heidelberg and Mannheim University of Applied Sciences, is an applied research center that leverages the mass spectrometry (MS) platforms “MS imaging” (Fülöp et al., 2013; Ruh et al., 2013; Schwartz et al., 2015) and “MS cell/tissue biotyping” (Munteanu et al., 2014) for pharmaceutical and diagnostic applications. ABIMAS features several MALDI-TOF/TOF-, and ESI-mass spectrometers. With established platforms for the classification of cells and tissues by mass spectrometry fingerprinting, and for MALDI MS imaging, and with extensive experience in drug discovery (e.g. tyrosine kinase inhibitors etc. and their targets), Mannheim University of Applied Sciences will analyze the dissociated GIST biopsy material prepared by partner FHI. Furthermore, they will classify dissociated GIST biopsies according to expression of targets pursued by partners UHEI and AAA to develop new targeted radio-diagnostics and therapeutics. ABIMAS will implement a database of MS fingerprints for biopsies/tumor tissue containing known drug targets and use this data base for target definition in GIST biopsies.
Fülöp A, Porada MB, Marsching C, Blott H, Meyer B, Tambe S, Sandhoff R, Junker HD, Hopf C (2013). 4-Phenyl-α-cyanocinnamic acid amide: screening for a negative ion matrix for MALDI-MS imaging of multiple lipid classes. Anal Chem. 85(19):9156-63.
Munteanu B, Meyer B, von Reitzenstein C, Burgermeister E, Bog S, Pahl A, Ebert MP, Hopf C (2014). Label-free in situ monitoring of histone deacetylase drug target engagement by matrix-assisted laser desorption ionization-mass spectrometry biotyping and imaging. Anal Chem. 86(10):4642-7.Ruh H, Salonikios T, Fuchser J, Schwartz M, Sticht C, Hochheim C, Wirnitzer B, Gretz N, Hopf C (2013). MALDI imaging MS reveals candidate lipid markers of polycystic kidney disease. J Lipid Res. 54(10):2785-94.
Schwartz M, Meyer B, Wirnitzer B, Hopf C (2015). Standardized processing of MALDI imaging raw data for enhancement of weak analyte signals in mouse models of gastric cancer and Alzheimer’s disease. Anal Bioanal Chem. 2014 Dec 27. [Epub ahead of print]
Carsten Hopf is currently professor of biochemistry at Mannheim University of Applied Sciences (MUAS). He heads the Applied Research Center for Biomedical Mass Spectrometry (ABIMAS) of MUAS, University of Heidelberg and the German Cancer Research Center (DKFZ). Carsten is also an associate member of the HBIGS international graduate school of University of Heidelberg.
Based on neurochemistry research performed at the Max-Planck-Institute for Developmental Biology, Tübingen, Germany, he obtained his PhD in biochemisty from University of Tübingen in 1998. As an EMBO fellow, he continued his research at the Johns Hopkins University School of Medicine, Department of Neuroscience, in Baltimore, USA. In 2001 he joined Cellzome, a leading proteomics-based drug discovery company, and contributed in roles of increasing responsibility until 2014.
Dipl. Ing. Matthias Schwartz studied Electrical Engineering at Darmstadt University of Technology. After temporary employments abroad at the University Paris Dauphine and the University of Bologna he developed neural network based systems for industrial applications at ZN Vision Technologies (Bochum, 1996-2000) and designed industrial
image processing systems at ISRA Vision AG (Darmstadt, 2000-2007).
In 2008 he joined the Mannheim University of Applied Sciences where
he was involved in different research projects concerning image
processing algorithms for object recognition in service robotics
and evaluation of MALDI MS imaging data of tissue samples for
the identification of potential biomarkers.
Matthias joined the MITIGATE project end of 2014, where he is
responsible for the analysis and classification of GIST biopsy
material based on MS spectral signatures. His main focus is the
GIST database design and development of suitable classification