Development of techniques for measurement, manipulation, and theoretical analysis of mechanical self-organizatio
B02-1
Understanding the mechanisms of neural crest emergence with neural tube reconstruction in vitro
In this research project, we aim to reconstitute the neural tube in vitro by employing a platform for body axis formation. Subsequently, we will investigate the mechanism of the appearance of Neural Crest (NC) cells during neural tube formation, and we will focus on elucidating how the mechano-chemical feedback guides the differentiation of local NC cells.
B02-2
Multicellular mechanics in angiogenesis using quantitative cell motility models
Angiogenesis is an important process in tissue formation and nutrient supply, where cell populations grow while maintaining a one-dimensional shape of tissue. Vascular endothelial growth factor (VEGF) induces angiogenesis, but tip cells do not migrate in the VEGF environment, even when subsequent stalk cells are laser-ablated to isolate the tip cells. It is not yet clear whether tip cells are pulling the stalk cells or if the stalk cells are pushing the tip cells. This project aims to understand the underlying principles of tissue formation during angiogenesis using image analysis and a mathematical model that incorporates mechanistic factors. Since qualitative models have limitations in consistently describing tissue movement according to experimental data, this research project plans to construct a quantitative cell model to reproduce angiogenesis and elucidate the principles from a mechanistic perspective.
B02-3
Self-organization of organoids woven by gene expression and mechanical boundary condition
This research aims to develop a method for a combined analysis of the mechanical properties (cell surface tension) and gene expression of individual cells in tissues and organoids. We will explore the temporal variation of gene expression and mechanical properties of cells during organogemesis and tissue remodeling to elucidate the molecular mechanisms that coordinates mechanical self-organization during tissue formation.
B02-4
Development and application of Bayesian inference of mechanical parameters
Measuring mechanical parameters in tissues, such as the elastic modulus of cell-cell junctions, is essential to decipher the mechanical control of morphogenesis. However, their in vivo measurement is technically challenging. Recently, we reported an image-based statistical approach to estimate the mechanical parameters of epithelial cells (Ogita et al. PLoS Comput. Biol., 2022). Candidate mechanical models are constructed based on force-cell shape correlations obtained from image data. Substitution of the model functions into force-balance equations at the cell vertex leads to an equation with respect to the parameters of the model, by which one can estimate the parameter values using a least-squares method. Over the next two years, we will reformulate our mechanical parameter inference in the framework of Bayesian statistics to incorporate more information such as a group property. We will then apply an extended method to image data that will be acquired in this consortium and we clarify the functional significance of mechanical parameters from both theoretical and experimental perspectives.
