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Engineering Mechanical and Biochemical Gradients to Control Cell Behaviors

In vivo, mechanical and biomolecular gradients play vital roles in various developmental and physiological processes. However, the complexity of the in vivo system hinders the study of the effect of one specific gradient factor. To decouple the impact of individual mechanical and biochemical gradients, it is essential to design engineered in vitro systems to precisely control the perspective gradient. I will discuss three innovative devices that drive cell behaviors in two general directions: mechanical gradient signals direct cell migration and biochemical morphogen gradient to pattern stem cell differentiation in 2D monolayer cell sheets and 3D brain organoids. In my first project, we for the first time depicted how single-cell migration is guided by mechanical strain gradients using combined experimental and computational approaches, termed “tensotaxis.” We further reveal that polarized focal adhesion distribution and cell protrusion may contribute to the tensotaxis. Mechanistically, we developed a motor-clutch model, which suggested that strain-introduced traction force determined integrin fibronectin pairs' catch-release dynamics. Together, our results identify a new class of mechanosensing behavior of cells. Given the prevalent existence of strain gradient during tissue growth and repairs, our work will open up a new area to study the effects of strain gradient in various biological contexts. In my second and third projects, we engineered two different localized passive diffusion devices which can differentiate stem cell monolayer or brain organoid into dorsal-ventral or/and rostral-caudal patternings. Recent advances in human pluripotent stem cells (hPSCs) derived in vitro models open a new avenue for studying early-stage human development. In vivo, the human brain is developed with distinct regional identities partially determined by signaling molecule gradients. Protocols were developed to derive brain organoids with specific regional identities. However, it is still impossible to fully recapitulate the cytoarchitecture of the developing brain with high reproducibility. Our device leverages localized passive diffusion to generate a stable chemical gradient in an open environment. We anticipate improving the reliability of the patterning through device optimization for large-scale applications such as drug screening and disease modeling.
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