Deep spatial immune profiling of granulomas and M. tuberculosis adaptation to disease and treatment

项目摘要 肉芽肿是肺结核（TB）的标志性病理特征， 遏制结核分枝杆菌（Mtb）感染和发展为TB疾病。但我们不 了解免疫细胞及其通信网络的地理空间组织如何影响宿主 免疫功能使得肉芽肿对Mtb在功能上是允许的而不是限制的。应力 在感染过程中遇到的Mtb诱导细菌适应， 但我们对不同肉芽肿内的细菌生长和代谢变化知之甚少 疾病或治疗过程中的微环境，以及微环境的地理空间组织和免疫状态 肉芽肿影响细菌生理学和杀灭。为了了解细胞网络和肉芽肿的空间分布 结构决定主要肉芽肿类型的功能能力，我们建议开发一种TB 肉芽肿信息系统（TB-GIS），将为单个肉芽肿和图层生成地理空间地图 关于免疫代谢和抗微生物功能以及结核分枝杆菌生理学和 适应为了表征肉芽肿的拓扑结构，我们开发并优化了一种新的高分辨率成像技术， 模式，t-CyCIF（组织循环免疫荧光），它允许深度的地理空间免疫分析， 组织（30+标记物）。我们将利用我们完善的产气结核分枝杆菌非人灵长类动物（NHP）模型， 感染，它概括了人类肺部病理病变的范围，并整合了额外的切割- 边缘工具和计算建模，以探测不同TB肉芽肿中的宿主-病原体界面（目标1）。 我们还将确定如何干扰肉芽肿拓扑与主机或病原体导向疗法的影响， 免疫功能和结核分枝杆菌代谢状态（目的2）。使用TB-GIS框架，我们将量化的关系 特定肉芽肿特征与细胞网络、免疫功能和Mtb生理学之间的关系， 未经处理的动物。我们预期这些TB-GIS研究将改变我们预测肉芽肿的能力 功能，并帮助设计新的疗法，以靶向肉芽肿窝藏耐药细菌，难以 目前的治疗方案。

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.