Predicting Tissue Dynamics based on Stochastic Variations in Cell Stiffness and Spatial Clustering within the Tissue Environment
Abstract
Major Goals: The main goal of this project was to understand the role of local and global heterogeneity in the mechanical properties of cells within a tissue on overall tissue dynamics. To achieve this goal, the work to be performed was split to focus on the following main aims - 1) Determine the mobility of cells within the tissue environment as a function of the mechanical properties of the cell and its neighbors Cells within dense tissue environments can migrate by exchanging spaces with neighbors and generating traction forces on the surrounding extra-cellular matrix. The cells can move individually or in clusters depending on the mechanical interactions between each other and their environment. As the tumor micro-environment is extremely heterogeneous in its architecture and mechanical properties, how this heterogeneity influences single and collective cell migration is extremely important to predict overall tissue dynamics. 2) Determine the rate of clustering and spatial segregation of cells with similar mechanical properties within the tissue environment As cell migrate through a tissue environment, they can exchange neighbors and segregate themselves from certain cell types while aggregate with certain others giving rise to distinct distributions of cell populations within the tissue environment. How cells with different mechanical phenotypes interact with each other and form patterns within the tissue environment is investigated as a part of this aim. 3) Determine the likelihood of tumor-like malignant cell populations, identified by their peculiar mechanical properties, growing and forming malignant tumors within these tissue environments. With a specific focus on tumor formation within healthy tissue environments, we aimed to predict how the mechanical heterogeneity of a tissue environment gives rise to cell clustering, proliferation and invasion of one specific phenotype.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 01, 2022
- Accession Number
- AD1196772
Entities
People
- Parag Katira
Organizations
- University of California, San Diego