The DIASyM research core will develop, optimize and standardize especially data independent acquisition (DIA) mass spectrometric workflows to enable a detailed characterisation of patients suffering from heart failure syndrome. Highly standardized biosamples from the MyoVasc cohort study and the Gutenberg Health Study (GHS) will be further analyzed on the proteomic, lipidomic and metabolomic level. In combination with deep phenotyping of these biosamples, biostatistics and bioinformatic tools including AI, machine learning and knowledge integration will be employed to generate multi-layered comprehensive data sets. Stored in an integrative data platform, these multi OMICs data sets will be used to improve the characterization of novel heart failure endotypes. Our findings will, thus, facilitate the identification of diagnostic and therapeutic targets in complex patient-derived biosamples. Highly standardized workflows encompassing all steps from sample preparation to data evaluation will enable the identification of biomarkers to improve patient stratification and treatment modalities and will pave the way towards personalized medicine.
