Abstract One of the greatest challenges in cancer biology is how to overcome issues of tumor heterogeneity and a dynamic microenvironment to select effective therapeutic treatments, reduce therapy resistance, and produce better patient outcomes. While genetic sequencing as standard of care for solid tumors has expanded the therapeutic toolbox, the molecular mechanisms underlying tumor heterogeneity, metastasis, and drug resistance remain poorly understood. For example, failures of targeted therapies like PI3 kinase inhibitors highlight the need for better understanding of how genetic and phenotypic heterogeneity work in concert with microenvironment pressures to enable cancer cell survival, metastasis, and evolution. Although less well studied in terms of heterogeneity, increased intracellular pH (pHi) is a feature of most cancers and enables a host of cancer phenotypes, including increased cell proliferation, metastasis, evasion from apoptosis, migration, and drug resistance. While the constitutively higher pHi of cancer has been shown to enable these behaviors on a population level in various models, little is known about how single-cell spatiotemporal pHi dynamics or pH heterogeneity might influence or drive single-cell cancer phenotypes. Here, I will use an innovative optogenetic tool to spatiotemporally manipulate pHi in living cells and elucidate the role of pHi dynamics in initiating or supporting single-cell cancer behaviors. I hypothesize that increased pHi is a critical indicator of cancer cell function and directly relevant to promoting single-cell invasion and drug resistance. Furthermore, I predict that pHi heterogeneity correlates with other more cryptic markers of heterogeneity including metabolic changes, stem cell markers, and epithelial and mesenchymal markers. In this work, I propose the rigorous investigation of the roles of pHi in supporting cancer cell behaviors through two complementary approaches. First, I will use an optogenetic tool to increase pHi in single cells to determine whether increased pHi is sufficient to drive single-cell invasion and drug resistance in 2D and 3D cancer models. Second, I will obtain pHi heterogeneity maps of 3D cancer models to determine if monitoring pHi increases can be predictive of individual cells that are likely to migrate, invade, or acquire drug resistance. The ability to dynamically measure pHi in living cells makes pHi an attractive biomarker for aggressive tumor subpopulations. Completion of these studies will transform our understanding of how pH dynamics support and promote cancer progression while revealing new routes for monitoring pHi as a diagnostic or prognostic tool or for identifying appropriate therapeutic interventions.