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

介导肺纤维化进展和消退的细胞网络和超微结构状态的时空图谱

• 批准号: 10600647
• 负责人: Jason Liwei Guo
• 金额: $ 6.95万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-18 至 2026-07-17
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10600647
• 关键词: Affect; Architecture; Atlases; Bioinformatics; Biological; Biological Models; Biophysics; Breathing; COVID-19; Cell Communication; Cells; Chemical Injury; Chronic; Chronic Disease; Clinical; Computer Models; Data; Deposition; Diagnosis; Epigenetic Process; Evolution; Extracellular Matrix; Fibroblasts; Fibrosis; Genetic Transcription; Goals; Heterogeneity; Histologic; Human; Individual; Influenza; Kinetics; Linear Regressions; Link; Location; Lung; Machine Learning; Macrophage; Mediating; Mediator; Mesenchymal; Metadata; Modeling; Molecular Target; Mus; Neighborhoods; Organ; Outcome; Pathogenesis; Pathologic; Pathway interactions; Patient-Focused Outcomes; Patients; Pattern; Persons; Phenotype; Play; Population; Prognosis; Proteins; Pulmonary Fibrosis; RNA; Regression Analysis; Resolution; Respiratory Tract Infections; Role; Science; Slice; Specimen; System; Systemic Scleroderma; Techniques; Time; Tissue-Specific Gene Expression; Tissues; Validation; Variant; biocomputing; cellular targeting; clinical prognosis; clinically relevant; computational atlas; epigenomics; idiopathic pulmonary fibrosis; immunoregulation; in vivo; indium-bleomycin; interstitial; lens; lung repair; machine learning algorithm; machine learning model; mouse model; multiple omics; novel; repaired; single-cell RNA sequencing; spatiotemporal; survival prediction; therapeutic development; transcriptomics

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.

项目摘要 肺纤维化（PF）是一种全球性的临床负担，影响超过5，000，000人， 由于化学损伤、慢性疾病如系统性硬化症或呼吸道感染， 流感和COVID-19通常，PF没有临床上确定的原因，被诊断为特发性 PF，诊断后的中位生存时间仅为2-4年。鉴于通常不明确的 PF的发病机制，存在着强烈的临床需要，以阐明生物学机制，有助于 它的发生和发展。然而，驱动空间异质性和时间进展的因素， 纤维化结构还没有被很好地理解。此外，异常PF基质的纤维化后分辨率 仍然是一个难以捉摸的目标，迄今为止还没有进行单细胞表征。因此，这 该项目旨在建立一个PF进展的时空图谱，将多组学与空间联系起来 纤维化和纤维化后的定义的组织邻域和时间上定义的结构状态 分辨率 间充质细胞群在所有主要器官的纤维化中起关键作用，其中大量巨噬细胞 和成纤维细胞亚型，通常涉及纤维化ECM沉积的介质。根据以前的研究，我 假设转录定义的巨噬细胞和成纤维细胞亚型在空间和时间上都起作用， 纤维化结节和纤维化后消退。为了研究这一假设，该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PF的病理进展和小鼠纤维化后修复。具体目标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（转录组学，表观基因组学和超微结构）， 随着时间的推移生物变化的计算建模。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Visium空间 转录组学、BABEL中的插补空间表观基因组学和连续 组织切片。Specific Aim 3将开发一种机器学习算法，用于预测临床结果， 通过统一组织病理学结构、蛋白质和细胞空间网络， 临床元数据最终，该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，识别生物学上保守的机制途径， 预后和治疗发展的相关目标。