BIOPHYSICAL CUES SHAPING MACROPHAGE AND T-CELL FUNCTIONS

About This Grant

Project summary. Inflammation underlies majority of human diseases including diabetes, atherosclerosis, and cancer. These diseases are responsible for majority of deaths and represent substantial global health burden. Macrophages and T-cells, subsets of immune cells, have emerged as key mediators of inflammation. The role of biochemical cues in shaping the transcriptional response of these cells have been investigated. However, accumulating evidence has shown that physical factors also tune their phenotype and effector functions. Recent two-dimensional studies have shown that mechanical confinement directs the nuclear translocation of transcription factors in macrophages. Another study found enhanced T-cell killing of cancer cells stiffened through cholesterol depletion. These studies have contributed to the field of mechano-immunology that seeks to understand how physical factors direct immune cell fate. Recent mechano-immunology findings have laid the groundwork for my proposal aimed at determining how biophysical cues shape macrophage and T-cell cell behavior. We have developed a three-dimensional culture that allows us to interrogate how biophysical cues regulate immune cell trafficking and macrophage-T- cell interaction in the tumor microenvironment. We have already identified that naïve macrophages are more efficient at trafficking to tumors than polarized macrophages. Furthermore, macrophages adopt different shapes depending on their activation state and their local microenvironment. Our preliminary results show that T-cells have longer-lived interactions with rounded macrophages, compared to elongated ones. This implicates macrophage shape, a biophysical property, in regulating its interaction with T-cells. We will extend these findings by elucidating the role of matrix viscoelasticity on immune cells behavior and performing a rigorous immunophenotyping of these cells. In addition, the proposal will implement machine learning algorithms to high resolution spatiotemporal information obtained from live confocal imaging. This will unlock the potential to identify heterogenous phenotypic states and quantify their evolution over time. Further, the proposal will integrate confocal live imaging with the single-cell RNA sequencing data. Such detailed, single cell analysis will identify genetic programs that are responsible for heterogenous morphometric states. The proposed research will be significant because it is expected to yield mechanistic insights that have broad translational impact for a myriad of diseases where inflammation is the underlying cause. These include Alzheimer’s, atherosclerosis, arthritis, diabetes, and cancer, which represent a growing global burden. The pathology of these diseases is orchestrated by macrophages and T-cells. Insight into the mechanobiology of macrophages, T-cells, and associated intracellular, transcriptional, and epigenetic modifications will deliver novel therapeutic options. Analysis of morphological heterogeneity using machine learning algorithms will provide a useful clinical and research tool to monitor disease progression.