Translational research combines knowledge and technology from basic scientific areas to answer patient orientated clinical questions. Since diseased tissue contains all information on genetic and proteomic changes, it represents the best possible sample material for molecular research. Traditionally, morphology, histochemistry and molecular pathological methods such as in-situ hybridization and sequencing technologies have been used for the detailed investigation of neoplastic and non-neoplastic disease. Recently, several imaging technologies became available to complement these analyses. In this regard, digital image analysis and imaging mass spectrometry hold great promise for spatially resolved investigation of tissue specimen at different molecular levels.
Digital Image Analysis
The combination of machine learning algorithms and data visualization allows automated and semi-automated analysis of scanned tissue slides. We apply this technology to aid recognition of specific cell types (e.g. tumor or immune cells), to count cells and to evaluate immunohistochemical stainings objectively.
Imaging Mass Spectrometry
Mass spectrometry has been successfully applied to study microbacterial colonies, plants, insects, vertebrates, human cells and tissues. Imaging mass spectrometry links molecular evaluation of numerous analytes such as peptides, proteins, lipids, carbohydrate compounds and exogenous or endogenous small molecules with morphological information about their spatial distribution in cells or tissues. Thus, this technology provides unbiased visualization of these molecules and allows to study biological processes in-situ. We investigate peptides on tissue microarrays constructed from formalin-fixed paraffin-embedded tissue material by imaging mass spectrometry. This comprehensive approach enables us to analyze hundreds of potential biomarkers on large, clinically characterized patient cohorts, while preserving tissue integrity. The aim of our research is to correlate molecular pattern and clinico-pathological data in order to create algorithms that allow to complement current molecular techniques regarding diagnosis, prognosis and prediction of response to therapy.
Mark Kriegsmann, MD
Katharina Kriegsmann, MD, MBA
Christiane Zgorzelski, technician