Spatiotemporal Atlas of Cellular Networks and Ultrastructural States Mediating the Progression and Resolution of Pulmonary Fibrosis

项目总结 肺纤维化(PF)是一种全球性的临床负担，影响着500多万人，可以发生为 化学损伤、慢性疾病(如系统性硬化症)或呼吸道感染(如 流感和新冠肺炎。通常，肺间质纤维化没有临床可确定的原因，被诊断为特发性。 PF，诊断后中位生存期仅为2-4年。考虑到通常不清楚 PF的发病机制，临床上迫切需要阐明其发病的生物学机制。 它的发生和发展。然而，驱动空间异质性和时间进程的因素 纤维性结构还没有被很好地理解。此外，异常PF基质的肝纤维化后分辨 这仍然是一个难以实现的目标，到目前为止还没有对其进行单细胞鉴定。因此，这一点 该项目的目标是建立一个PF进展的时空图谱，将多组学与空间联系起来 明确的组织邻域和暂时确定的纤维化和纤维化后的建筑状态 决议。 间充质细胞群在所有主要器官的纤维化中起关键作用，其中有许多巨噬细胞。 成纤维细胞亚型常被认为是纤维性ECM沉积的介质。根据先前的研究，我 假设转录上定义的巨噬细胞和成纤维细胞亚型同时在空间和时间上起作用 纤维化结节和纤维化后消退。为了研究这一假设，这个项目将建立一个新的 转录/表观遗传细胞亚型、相互作用网络和超微结构状态的计算图谱 介导小鼠肺纤维化的病理进展和肝纤维化后的修复。特定目标1将调查 转录定义的细胞亚群在纤维化的时间进展和消退中的作用 肺结构，使用高通量多组学(转录组、表观基因组和超微结构)和 生物随时间变化的计算模型。特定目标2将定义空间组织 肺纤维化中细胞和基质介导的相互作用的邻域，通过整合维西姆空间 转录学，归因于巴别塔的空间表观基因组学，以及连续的 组织切片。具体目标3将开发一种机器学习算法，用于预测临床结果 人类肺纤维化，通过统一的组织病理结构、蛋白质和细胞空间网络，以及 临床元数据。最终，这个项目将建立一个多组学、跨物种和计算严格的 PF进展和修复图谱，识别生物保守的机械途径和临床 预后和治疗发展的相关指标。

PROJECT SUMMARY Pulmonary fibrosis (PF) represents a global clinical burden that affects over 5,000,000 people and can occur as a result of chemical injury, chronic conditions such as systemic sclerosis, or respiratory infections such as influenza and COVID-19. Commonly, PF has no clinically determinable cause and is diagnosed as idiopathic PF, which presents a median survival time of only 2-4 years after diagnosis. Given the often unclear pathogenesis of PF, there exists a strong clinical need to elucidate the biological mechanisms that contribute to its onset and progression. Nevertheless, the factors that drive spatial heterogeneity and temporal progression in fibrotic architecture are not well understood. Furthermore, the post-fibrotic resolution of aberrant PF matrix remains an elusive goal, for which no single-cell characterizations have been performed to date. Thus, this project aims to establish a spatiotemporal atlas of PF progression that links multi-omics with spatially defined tissue neighborhoods and temporally defined architectural states of fibrosis and post-fibrotic resolution. Mesenchymal cell populations play a critical role in the fibrosis of all major organs, with a number of macrophage and fibroblast subtypes often implicated as mediators of fibrotic ECM deposition. Based on prior studies, I hypothesize that transcriptionally defined macrophage and fibroblast subtypes act as both spatial and temporal nodes of fibrosis and post-fibrotic resolution. To investigate this hypothesis, this project will establish a novel computational atlas of transcriptional/epigenetic cell subtypes, interaction networks, and ultrastructural states that mediate the pathological progression of PF and post-fibrotic repair in mice. Specific Aim 1 will investigate the roles of transcriptionally defined cell subpopulations in the temporal progression and resolution of fibrotic pulmonary architecture, using high-throughput multi-omics (transcriptomic, epigenomic, and ultrastructural) and computational modeling of biological variations over time. Specific Aim 2 will define the spatial tissue neighborhoods of cell- and matrix- mediated interactions in pulmonary fibrosis, by integrating Visium spatial transcriptomics, imputed spatial epigenomics in BABEL, and ultrastructural quantification on consecutive histological slices. Specific Aim 3 will develop a machine learning algorithm for prognosis of clinical outcomes in human pulmonary fibrosis, by unifying histopathological architecture, protein and cell spatial networks, and clinical metadata. Ultimately, this project will establish a multi-omic, cross-species, and computationally rigorous atlas of PF progression and repair that identifies biologically conserved mechanistic pathways and clinically relevant targets for prognosis and therapeutic development.