Multi-Region Brain Organoid - Fusion Organoid with cerebral, Endothelial and Mid-Hindbrain Components
Brain organoid technology has revolutionized our ability to model human neurodevelopment in vitro. However, current techniques remain limited by their reliance on simplified endothelial cell populations rather than a complete endothelial system. We engineered Multi-Region Brain Organoids (MRBOs) that integrate cerebral, mid/hindbrain, and complex endothelial organoids into one structure. Different from the earlier approaches based on isolated HUVECs, our endothelial organoids contain diverse vascular cell types, including vascular progenitors, mature endothelial cells, pericytes, proliferating angiogenic cells and stromal cells. Our strategy employs a sequential modulation of key developmental pathways to generate individual organoids, followed by optimized fusion conditions that maintain regional identities while supporting cellular integration. Single-nucleus RNA sequencing shows that MRBOs develop discrete neural populations specific to each brain region alongside specialized endothelial populations that establish paracrine signalling networks. Integration analysis with human fetal brain data shows that MRBOs contribute to 80% of cellular clusters found in human fetal brain tissue (Carnegie stages 12-16), whereas CellChat analysis identifies 13 previously uncharacterized endothelial-neural signalling interactions. Notably, we uncover endothelial-derived factors that support the persistence of intermediate progenitor populations during mid/hindbrain development, thereby revealing a new role for complex endothelial populations in regional brain patterning. This effect was not observed in cerebral development. This platform enables matching multiple developmental regions, fully incorporating the endothelial nature at the same time. There are opportunities for studying neurodevelopmental disorders in which neural-endothelial interactions are disrupted. Our engineered MRBO system establishes a foundation for investigating complex neurodevelopmental processes, providing an enabling context closer to physiological relevance. Overall design: The single-nucleus RNA sequencing (scRNA-seq) data were analyzed using the Seurat package (version 5.0) in R. Quality control measures were applied to filter low-quality cells, and cells were clustered based on their gene expression profiles. Dimensionality reduction was performed using Uniform Manifold Approximation and Projection (UMAP) to visualize cellular heterogeneity. Cell type annotations were assigned by identifying cluster-specific marker genes and comparing them to established brain cell type signatures, facilitated by the sc-type package.