Proteomics and Biomarkers

Proteomics and Biomarkers

Research in our laboratory centers on the identification of biosignatures for psychiatric disorders and the antidepressant drug response. Sensitive high throughput proteomics and metabolomics platforms are used for data generation providing a rich source for in silico pathway analyses. Our ultimate goal is to complement imprecise DSM-based clinical parameters with molecular biosignatures to improve patient diagnosis, stratification and treatment.

Current interests are focused on:

  • Discovery of affected molecular pathways in mouse models that represent defined endophenotypes characteristic for human psychiatric disorders including anxiety, posttraumatic stress disorder and schizophrenia.

  • Drugs that target the monoaminergic (SSRI) and glutamatergic (Ketamine) systems studied in mice with the goal to delineate mechanisms relevant for the therapeutic response and novel drug targets.

  • Clinical translation with the help of psychiatric patient specimens collected in our hospital.

Biosignature Discovery Technology

In order to advance our protein analysis capabilities in living organisms we perform methods development. We have implemented the sensitive, comprehensive SILAM proteomics platform by metabolically labeling mice with 15N and 13C stable isotopes. In an extension of this approach we are using partial metabolic labeling. Interrogating partially-labeled tissue proteins by tandem mass spectrometry and ProTurnyzer software allows us to get turnover rates for individual proteins. In collaboration with Claude Lechene of Harvard University Multi Isotope Mass Spectrometry (MIMS) with the help of nano-SIMS technology delineates tissue turnover at sub-cellular resolution.

Spatial, cellular and molecular protein turnover analyses.
Mice are partially metabolically labeled with a 15N diet, mouse brains sectioned and then processed for either Multi Isotope Mass Spectrometry (MIMS) or shotgun tandem mass spectrometry proteome analyses (Zhang et al. 2011; Gormanns et al. 2012).
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