MASSACHUSETTS INSTITUTE OF TECHNOLOGY | RAGON INSTITUTE
Research Program
Why do rapidly dividing T cells need lipids?
Fatty acids and other lipids are chronically dysregulated across many human disease states, including obesity, aging, and cancer. Lipids belong to a chemically diverse class of metabolites, yet little is known about the molecular fates of lipids in rapidly dividing cells, particularly with complex lipid mixtures that are encountered under normal physiology. The Ringel lab seeks to reveal the metabolic, structural, and signaling fates of lipids in T cells that regulate immune responses.
How does the aging immune system interact with tumors?
While aging is a universal cancer risk factor, little is known about how the aging immune system interacts with a growing tumor. The T cell pool is remarkably susceptible to age-related changes that reduce immune function. T cells are also responsible for tumor surveillance, where a healthy T cell compartment can eliminate malignant cells before they grow into tumors. Our goal is to identify molecular axes of T cell dysfunction during aging that contribute to tumorigenesis and can be targeted to improve cancer prevention and therapy.
Toolsets to measure metabolism approaching single cell resolution.
Tools to measure bioenergetics and cellular metabolism with greater sensitivity and resolution will provide new insight into metabolic networks within tissues and tumors. We develop well-validated tools to study metabolism in single cells and within subcellular structures and then apply these tools to understand metabolic heterogeneity in the tumor niche.
How do metabolic networks regulate tumorigenesis?
Metabolic reprogramming is hallmark feature of both tumor and immune cells, which coexist in the tumor niche and share finite resources. There is considerable overlap between the nutrient preferences in tumor cells and the metabolic dependencies of T cells. Yet surprisingly little is known about the impact of cancer metabolism on T cells within the same tumor niche beyond competition for major carbon sources. Using a variety of experimental approaches and model systems, we aim to identify and investigate networked relationships that govern tumor progression and response to therapy.