Abstract Organoids have become valuable models for understanding cellular and molecular mechanisms in human development including brains. However, whether developmental gene expression programs are preserved between human organoids and brains, especially in specific cell types, remains unclear. Importantly, there is a lack of effective computational approaches for comparative data analyses between organoids and developing humans. To address this, by considering the public data availability and research significance, we developed a machine learning framework, Brain and Organoid Manifold Alignment (BOMA) for comparative gene expression analysis of brains and organoids, to identify conserved and specific developmental trajectories as well as developmentally expressed genes and functions, especially at cellular resolution. BOMA first performs a global alignment and then uses manifold learning to locally refine the alignment, revealing conserved developmental trajectories between brains and organoids. Using BOMA, we found that human cortical organoids better align with certain brain cortical regions than other non-cortical regions, implying organoid-preserved developmental gene expression programs specific to brain regions. Additionally, our alignment of non-human primate and human brains reveals highly conserved gene expression around birth. Also, we integrated and analyzed developmental scRNA-seq data of human brains and organoids, showing conserved and specific cell trajectories and clusters. Further identification of expressed genes of such clusters and enrichment analyses reveal brain- or organoid-specific developmental functions and pathways. Finally, we experimentally validated important specific expressed genes using immunofluorescence. BOMA is open-source available as a web tool for general community use.

Brain and Organoid Manifold Alignment (BOMA), a machine learning framework for comparative gene expression analysis across brains and organoids