Speaker
Description
Engineered human myocardium (EHMs) provides unique opportunity for modeling cardiac development, disease, and drug testing. EHMs, derived from stem cells and cultured over extended periods, closely mimic the structure and function of matured cardiac tissue. This cardiac 3D model offers an innovative platform for exploring cardiac biology and developing novel therapeutic strategies.
A critical aspect of our research is the integration and automated analysis of multi-omics datasets to elucidate the complex biological processes involved in EHM development, maturation, and response to pharmacological agents. Over a one-year culture period, EHMs are subjected to extensive multi-omics profiling, including proteomics, transcriptomics, and protein turnover studies. High-resolution imaging with SarcAsM, an AI tool, provides detailed structural analysis of sarcomeres, the fundamental contractile units of muscle tissue. Functional performance is regularly assessed using CardioTraceAnalyzer, an AI-driven tool for analyzing time-series data of tissue contractions.
The integration of these diverse data types presents significant challenges. To address this, we are implementing a Data Lake approach in collaboration with the Gesellschaft für wissenschaftliche Datenverarbeitung Göttingen (GWDG). This strategy ensures that multi-modal data will be accessible, organized, and ready for advanced computational analysis. By centralizing data storage and management, we facilitate seamless data integration, enabling robust cross-disciplinary collaboration and deeper insights into cardiac organoid biology.
Automated analysis pipelines will be established to process and interpret complex datasets efficiently. These pipelines utilize machine learning algorithms to uncover patterns and correlations within the multi-omics data, driving novel insights into cardiac development and disease mechanisms. Our integrated approach not only advances the understanding of EHM morphogenesis and maturation but also sets a precedent for utilizing large-scale, multi-omics datasets in drug testing and disease modeling. By fostering collaboration with data scientists and leveraging automated analytical techniques, we aim to accelerate the development of innovative treatments for cardiac diseases, ultimately contributing to improved patient outcomes.
