Deconstructing Adaptive Piezoelectric Responses in Pathological and Healthy Microenvironments

forthcoming

Tissues are piezoelectric materials, generating electrical signals in response to mechanical stress, and yet this important aspect has not yet been incorporated into the design of disease research models, particularly in conditions like idiopathic pulmonary fibrosis (IPF). Piezoelectricity affects cellular migration, growth, and differentiation. However, most of our understanding of cell-tissue interactions comes from studying cells under ‘static’ mechanical conditions. Cells interact with their microenvironment by applying forces and receiving bioelectric feedback through ion channels, which are overexpressed in fibrotic tissues (16-fold). Because macrophages and fibroblasts are involved in IPF-related fibrogenesis, we hypothesize that piezoelectricity plays a critical role in their differentiation states during disease progression. Current IPF research models do not replicate the electro-mechanical cellular environment, widening the gap between predictive in vitro models and clinical outcomes. Bridging this gap requires a paradigm shift toward models that incorporate electro-mechanical coupling. With DAPHNE, the applicant will develop a state-of-the-art piezoelectric platform providing both electrical and mechanical signals, enhancing the physiological relevance of current models. This innovation will accelerate drug development by enabling the testing of novel anti-fibrotic therapies targeting ion channel activity and creating the first statistically powerful library of cell-ECM electro-mechanical interactions. At the core of DAPHNE is the ReShape platform, which integrates three key functions: • Force: Applying dynamic forces to modulate piezoelectricity and cellular activity. • Visualize: Using real-time, label-free Fluorescence Lifetime Imaging Microscopy (FLIM) to track cellular ‘metabolic state’ changes at relevant biological timescale. • ReShape: Evaluating tissue-wide responses to cellular activity at high throughput.