Deciphering shared molecular dysregulation across Parkinson's Disease variants using a multi-modal network-based data integration and analysis
Parkinson's disease (PD) is a progressive neurodegenerative disorder with no effective treatment. Advances in neuroscience and systems biomedicine now enable the use of complex patient-specific in vitro disease models and cutting-edge computational tools for data integration, enhancing our understanding of complex PD mechanisms. To explore common biomedical features across monogenic PD forms, we developed a knowledge graph (KG) by integrating previously published high-content imaging and RNA sequencing data of PD patient-specific midbrain organoids harbouring LRRK2-G2019S, SNCA triplication, GBA-N370S or MIRO1-R272Q mutation with publicly available biological data. Furthermore, we generated a single-cell RNA sequencing dataset of midbrain organoids derived from idiopathic PD patients (IPD) to stratify IPD patients towards genetic forms of PD. Overall design: Single cell RNA sequencing of midbrain organoids generated from iPSC of idiopathic Parkinson's disease patients and healthy controls.