A Technology to Model Brain Neural Tube Folding in vitro for Precision Medicine

This project aims to develop an integrated, mechanobiology-driven anterior brain organoid platform that combines stiffness-controlled substrates and AI-powered analysis to non-invasively track morphogenesis, maturation, and disease phenotypes. Current organoid models fail to replicate the in vivo mechanical microenvironment, rely on endpoint assays, and lack scalable methods for structural quantification. Our preliminary data show that substrate stiffness profoundly influences cortical morphogenesis: organoids on soft (8 MPa) substrates reproducibly formed multiple neural folds by day 4, whereas stiffer conditions (~16 MPa, 1.5 GPa) produced fewer or none. This represents, to our knowledge, the first demonstration of self-organized folding in brain organoids without external constraints. The proposed approach integrates three innovations: (i) mechanically tuned substrates validated by AFM, (ii) AI-based pipelines to extract features such as size, folding index, and surface complexity, and (iii) predictive models linking mechanics to developmental outcomes. The platform will generate novel mechanobiological insights into cortical folding, enable scalable non-invasive phenotyping, and advance patient-specific modeling of neurodevelopmental disorders such as neural tube defects, autism, microcephaly, and lissencephaly.

Hamad Bin Khalifa University (HBKU)