PROJECT SUMMARY Granulomas are hallmark pathological features of pulmonary tuberculosis (TB) and contribute to both containment of Mycobacterium tuberculosis (Mtb) infection and progression to TB disease. However, we do not understand how the geospatial organization of immune cells and their communication networks impact the host immune functions that render a granuloma functionally permissive versus restrictive to Mtb. Stresses encountered by Mtb during infection induce bacterial adaptations that promote Mtb survival and drug tolerance, but we know little about the bacterial growth and metabolic changes induced within different granuloma microenvironments during disease or treatment and how the geospatial organization and immune state of the granuloma impacts bacterial physiology and killing. To understand how cellular networks and granuloma spatial architecture determine the functional capacities of major granuloma types, we propose to develop a TB Granuloma Information System (TB-GIS) that will generate a geospatial map for individual granulomas and layer on additional data related to immunometabolic and antimicrobial functions, as well as Mtb physiology and adaptation. To characterize granuloma topology, we have exploited and optimized a novel high-plex imaging modality, t-CyCIF (tissue Cyclic Immunofluorescence), which allows for deep geospatial immune profiling of tissue (30+ markers). We will leverage our well-established nonhuman primate (NHP) model of aerogenic Mtb infection which recapitulates the spectrum of human lung pathological lesions and integrate additional cutting- edge tools and computational modeling to probe the host-pathogen interface in different TB granulomas (Aim 1). We will also determine how perturbing granuloma topology with host- or pathogen-directed therapies impacts immune function and Mtb metabolic state (Aim 2). Using the TB-GIS framework, we will quantify the relationship between specific granuloma features and cellular networks, immune function and Mtb physiology in treated and untreated animals. We anticipate that these TB-GIS studies will transform our ability to predict granuloma function and help design new therapies to target granulomas harboring drug-tolerant bacteria that are difficult to clear with current treatment regimens.