The goal of this project is to address PQ7: What in vivo imaging methods can be developed to determine and record the identity, quantity, and location of each of the different cell types that contribute to the heterogeneity of a tumor and it microenvironment? Heterogeneity exists in tumor cell response to treatment, and in the pro-tumor vs. anti-tumor behavior of the cells in the microenvironment (e.g. fibroblasts, immune cells). However, there are a lack of in vivo imaging methods that can quantify this heterogeneity, thus limiting our understanding of cellular-level tumor behavior, and limiting our ability to develop improved cancer treatments. Cellular metabolism provides dynamic insight into individual cell behavior. We and others have shown that tumor cell metabolism reflects anti-cancer drug response, and tumor cells alter their metabolic activities in order to resist drug treatment. The metabolism of tumor-associated fibroblasts also support malignancy, by supplying nutrients to drive tumor growth. Additionally, the metabolism of immune cells is reflective of their pro- and anti-tumor behavior, with distinct changes in metabolic activities between M1-like and M2-like macrophages, between CD4+ and CD8+ T cells, and within the CD4+ T cell subset. Due to the key role of metabolism in maintaining the tumor and its microenvironment, drugs that disrupt the metabolism of tumor cells and cells in the microenvironment have been FDA approved for breast cancer treatment in combination with standard therapies. The goal of this proposal is to develop and validate optical metabolic imaging (OMI) to quantify dynamic metabolic heterogeneity within the tumor cell, fibroblast, macrophage, and T cell populations in tumors in vivo. Multiphoton microscopy will resolve individual cells within the Polyoma middle T (PyMT) mouse model of breast cancer throughout a treatment time-course. Autofluorescence from the metabolic co-enzymes NADH and FAD will quantify cellular metabolism, and report on metabolic heterogeneity within cell populations. Specifically, OMI will measure the optical redox ratio of each cell (fluorescence intensity of NADH divided by that of FAD), which reflects redox balance in a cell. OMI also quantifies the fluorescence lifetimes of NADH and FAD, which reflect the enzyme binding activity of these molecules. Our published work and preliminary data demonstrate that OMI is sensitive to heterogeneous drug response in tumors in vivo, and OMI can distinguish sub-types of tumor cells and immune cells. The proposed work will validate in vivo OMI of tumor heterogeneity with ex vivo flow cytometry and in vivo imaging of fluorescent cell surface markers. The proposed aims will test the hypothesis that in vivo OMI can record the identity, quantity, and location of different cell types that contribute to the metabolic heterogeneity of a tumor and its microenvironment. These tools will enable longitudinal quantification of the in vivo metabolic heterogeneity of tumor cells, fibroblasts, macrophages, and T- cells. The insights gained from these measurements can be used to develop improved cancer treatments that combat tumors on a single-cell level in order to achieve remission-free survival.