Stephanie Watermann

The identification of metabolites is an extremely relevant factor in metabolomics studies. After the detection of specific biomarkers, which for example allow the differentiation of plant samples of different geographical origin or in the case of animal samples the targeted detection of diseases, their identification is essential for a deeper understanding of the underlying metabolic pathways.

Since a large number of different metabolites occur in both plant and animal samples, direct identification from one-dimensional NMR spectra is hardly possible due to considerable signal overlap. In addition, the individual metabolites show large differences in concentration, which makes the detection of low concentrated metabolites more difficult.

For this reason, the goal is to develop a strategic approach for the simple, reliable and efficient identification of metabolites from complex mixtures without the need to develop a complex and individual isolation strategy for single metabolites. This approach shall be based on a correlation of NMR and MS data over the time course of a chromatographic separation. This will allow the detection of related signals in the NMR spectra as well as related mass-to-charge ratios from the mass spectra. In order to obtain additional direct information about the associated 13C shifts, 2D HSQC spectra are recorded for the correlation of the data. Finally, this approach shall enable a reliable identification of already known as well as so far unknown natural compounds.

since 07/2020: research associate at the University of Hamburg

10/2017 - 03/2020: Master studies in chemistry at the University of Hamburg, completed with the master thesis on “Optimization and comparison of one- and two-dimensional NMR experiments for the determination of the geographical origin of walnuts” in the research group of Dr. T. Hackl

10/2014 - 01/2018: Bachelor studies in chemistry at the University of Hamburg, completed with the bachelor thesis on “Synthesis of LpxA inhibitors” in the research group of Prof. Dr. R. Holl