Theoretical and Computational Analysis of Cell Migration in Complex Tissue Environments

Citation

Wang, Zitong (Jerry) (2025) Theoretical and Computational Analysis of Cell Migration in Complex Tissue Environments. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/mj08-b258. https://resolver.caltech.edu/CaltechTHESIS:06152024-132652470

Abstract

Cells sense and respond in spatially structured environments, including soils and tissue. My Ph.D. projects centered on developing new theoretical models and computational methods to understand how cells migrate in complex environments.

The first project is more theoretical in nature, leveraging information theory to study how the spatial organization of cell signaling pathways are adapted to the cell's natural environment. In tissue and soil, cells must localize to their targets by navigating distributions of extracellular ligands that are spatially discontinuous, consisting of local concentration peaks, due to binding a non-uniform network of ECM fibers. It is unclear how cells navigate patchy environments while not getting trapped in local concentration peaks. To answer this question, we framed navigation as a problem of maximizing mutual information in space and developed a computational algorithm for computing signaling pathway architectures that maximize mutual information in simulated natural environments. We found that for cells in tissues and soils, dynamic localization of membrane receptors dramatically boosts sensing precision and enables cells to navigate to chemical sources 30 times faster, but this receptor localization strategy is relatively inconsequential for cells in purely diffusive environments. Further, we found that anisotropic receptor dynamics previously observed in immune cells and growth cones are nearly optimal as predicted by our model.

The second project is more computational in nature, leveraging multiplexed tissue imaging to understand T-cell migration in tumor microenvironments. Immunotherapies can halt or slow down cancer progression by activating either endogenous or engineered T-cells to detect and kill cancer cells. T-cells must infiltrate the tumor core for immunotherapies to be effective. However, many solid tumors resist T-cell infiltration, challenging the efficacy of current therapies. In collaboration with clinician scientists at Cedars-Sinai Medical Center, we developed an integrated deep learning framework, Morpheus, that takes large-scale spatial omics profiles of patient tumors, and combines a formulation of T-cell infiltration prediction as a self-supervised machine learning problem with a counterfactual optimization strategy to generate minimal tumor perturbations predicted to boost T-cell infiltration. We applied Morpheus to 368 metastatic melanoma and colorectal cancer samples assayed using 40-plex imaging mass cytometry, discovering cohort-dependent, combinatorial perturbations, involving CXCL9, CXCL10, CCL22 and CCL18 for melanoma and CXCR4, PD-1, PD-L1 and CYR61 for colorectal cancer, predicted to support T-cell infiltration across large patient cohorts. Using only raw image data, Morpheus also identified distinct therapeutic strategies for different patient strata such as cancer stage or fatty liver presence. Our work presents a paradigm for counterfactual-based prediction and design of cancer therapeutics using spatial omics data.

Thesis Files