Research Interest of the Schmidlin Laboratory

The Metabolomics Research Group is interested in studying the various roles of metabolism in health and disease. By using state-of-the-art LC-MS-based metabolomics technologies, we are able to monitor hundreds of metabolites and their interplay in a variety of biological samples ranging from cell lines up to primary tissue and plasma. Within the context of the BMBF-funded research core DIASyM (embedded in the MSCoreSys consortium) we collaborate with local and national partners to combine the expertise of metabolomics and other MS-based OMICS technologies with (bio)informatics and systems medicine. This enables us to gain a deeper understanding of pathomechanisms that can be used for clinical translation.

Main Projects in our lab focus on:

1. Establishing novel LC-MS-based metabolomics technologies: To facilitate the transition of LC-MS-based OMICS technologies into the clinics we are developing novel robust and highly standardized metabolomics protocols enabling for the rapid phenotyping of large sample cohorts. We focus on the development of high-throughput, automated, and highly parallelized sample preparation protocols for human plasma in combination with data-independent acquisition (DIA) LC-MS methods. With this we obtain detailed information about the different metabolic landscapes within large cohorts of patients.
2. Molecular Signatures of the Heart Failure Syndrome: The heart failure syndrome (HF) is one of the leading causes of hospitalization for people over the age of 65 in Europe and is associated with poor long-term survival. The underlying mechanisms leading to HF are still largely unknown and treatment options are limited. By using large prospective cohorts of patients with heart failure and population-representative individuals (provided through the MyoVasc cohort and the Gutenberg Health Study) we investigate molecular fingerprints of HF aiming to find novel pathophysiological subphenotypes. In collaboration with local partners focusing on knowledge mining, agnostic and supervised learning approaches we jointly work towards a coordinated translation of these data into clinical applications.

Kontakt

Jun.-Prof. Dr. Thierry Schmidlin

 schmidlt@uni-mainz.de