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

项目总结 肉芽肿是肺结核(TB)的标志性病理特征，对两者都有贡献。 遏制结核分枝杆菌(Mtb)感染和发展为结核病。然而，我们并没有 了解免疫细胞的地理空间组织及其通信网络如何影响宿主 免疫功能使肉芽肿对结核分枝杆菌功能是允许的还是限制的。压力 结核分枝杆菌在感染过程中遇到的诱导细菌适应，从而促进结核杆菌存活和药物耐受性， 但我们对不同肉芽肿内细菌的生长和代谢变化知之甚少。 疾病或治疗期间的微环境，以及人类的地理空间组织和免疫状态 肉芽肿影响细菌的生理和杀伤力。为了了解细胞网络和肉芽肿如何在空间上 架构决定了主要肉芽肿类型的功能能力，我们建议开发一种结核病 肉芽肿信息系统(TB-GIS)，将为各个肉芽肿和层生成地理空间地图 关于免疫代谢和抗菌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复数成像 T-CyCIF(组织循环免疫荧光)，它允许深度地理空间免疫分析 组织(30个标记)。我们将利用我们成熟的非人类灵长类(NHP)产气结核分枝杆菌模型 感染概括了人类肺部病理损害的谱系，并整合了额外的切割- 用于探测不同结核肉芽肿宿主-病原体界面的边缘工具和计算模型(目标1)。 我们还将确定通过宿主或病原体导向治疗扰乱肉芽肿拓扑结构的影响 免疫功能和结核分枝杆菌代谢状态(目标2)。利用TB-GIS框架，我们将量化这种关系 特殊的肉芽肿特征与细胞网络、免疫功能和结核分枝杆菌生理之间的关系 未经治疗的动物。我们预计这些结核-地理信息系统研究将改变我们预测肉芽肿的能力 起作用并帮助设计新的治疗方法，以针对含有耐药细菌的肉芽肿，这些细菌很难 用目前的治疗方案清除。

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.