ENG-BIOTECH: Collaborative Research: Data-driven Mechanomics to Uncover Age-related Mechanoregulation of Bone

Certain types of stem cells play a key role in the repair and regeneration of musculoskeletal tissues. Physical activities like walking and running enhance healing by influencing these cells. Mimicking these mechanical signals offers a promising avenue for creating biotechnologies that accelerate tissue regeneration, reduce medical costs, and improve quality of life. However, designing precise mechanical signals to guide healing and regeneration at the cellular level remains a challenge. This award supports research to quantify the mechanical forces at work inside living cells and determine whether changes in these forces impact how the cells produce and secrete proteins that shape their surrounding environment. Gaining this knowledge could lead to predictive tools that use mechanical forces to control cell behavior, enabling advances in tissue engineering, clinical treatments, and diagnostics for identifying and addressing diseases. The educational efforts will engage engineering students from diverse disciplines, improving their retention and success in research. The research team seeks to also inspire future scientists by partnering with Idaho elementary schools, strengthening the pipeline to careers in science and engineering. A major technical challenge in developing mechano-biotechnologies for guiding cellular differentiation is the lack of methods to non-invasively measure dynamic changes in intracellular mechanical stresses and gene expression in living cells. This research addresses this challenge by combining confocal imaging and computational finite element modeling to create a digital representation of the cell nucleus and surrounding actin cytoskeleton. The model will allow for the analysis of forces generated by actin cytoskeleton and the quantification intra-nuclear of stresses in a cell-specific manner. The research team will validate the model by experimentally tracking 3D nuclear deformations and advance mechanobiology knowledge by: (1) Developing predictive mechanobiotechnology paradigms to control cell anabolism via externally applied mechanical forces. (2) Producing a foundational, cell-specific dataset that can inform or train clinical imaging modalities, such as CT and in-situ probes, to improve diagnoses of conditions including osteoporosis, hypertension, cancer, and age-related extracellular matrix stiffening. Ultimately, advancing a data-driven mechanomics approach to identify cell specific forces in living cells will lead to a better understanding of cell mechanosignaling and provide a mechanical basis for identifying and addressing pathologies. This project is jointly funded by Biomechanics and Mechanobiology Program and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.