Deciphering shared molecular dysregulation across Parkinson's disease variants using a multi-modal network-based data...

Area of Science:

• Neuroscience
• Systems Biomedicine
• Genetics

Background:

• Parkinson's disease (PD) is a progressive neurodegenerative disorder with limited treatment options.
• Patient-specific in vitro models and computational tools are advancing PD research.
• Understanding PD heterogeneity is crucial for developing effective therapies.

Purpose of the Study:

• To identify common biomedical features across different forms of monogenic Parkinson's disease.
• To integrate multi-omics data from PD patient-specific midbrain organoids.
• To stratify idiopathic PD (IPD) patients by comparing them to monogenic PD models.

Main Methods:

• Developed a knowledge graph (KG) integrating high-content imaging and RNA sequencing data.
• Utilized PD patient-specific midbrain organoids with known mutations (LRRK2, SNCA, GBA, MIRO1).
• Generated single-cell RNA sequencing data from idiopathic PD patient-derived midbrain organoids.

Main Results:

• Identified common transcriptomic dysregulation in glial cells of IPD organoids, mirroring monogenic PD forms.
• Found shared pathophysiology between monogenic and idiopathic PD.
• Highlighted potential involvement of ROBO signaling dysregulation in PD.

Conclusions:

• Despite PD heterogeneity, common molecular pathways exist across different genetic forms.
• Glial cells in IPD organoids exhibit transcriptomic changes similar to monogenic PD.
• ROBO signaling dysregulation may contribute to shared PD pathology.