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）代表了一个全球临床负担，影响超过500万人，并且可能发生 化学损伤的结果，诸如全身性硬化症或呼吸道感染之类的慢性疾病的结果，例如 流感和Covid-19。通常，PF没有临床确定的原因，被诊断为特发性 PF的中位生存时间仅在诊断后仅2 - 4年。给定经常不清楚的 PF的发病机理，存在着强大的临床需要，以阐明有助于的生物学机制 它的发作和进展。然而，推动空间异质性和时间进展的因素 纤维化建筑尚不清楚。此外，异常PF矩阵的纤维化分辨率 仍然是一个难以捉摸的目标，迄今为止，尚未执行单细胞特征。因此，这个 项目旨在建立PF进展的时空地图集 定义的组织邻域以及纤维化和纤维化后的时间定义的建筑状态 解决。 间充质细胞群在所有主要器官的纤维化中起关键作用，许多巨噬细胞 和成纤维细胞亚型通常与纤维化ECM沉积的介体有关。根据先前的研究，我 假设转录定义的巨噬细胞和成纤维细胞亚型作为空间和颞 纤维化和纤维化后分辨率的节点。为了调查这一假设，该项目将建立一个小说 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地图图集 介导小鼠PF和纤维化后修复的病理进展。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分辨率中的作用 肺建筑，使用高通量多词（转录组，表观基因组和超微结构）和 随着时间的推移，生物学变异的计算建模。特定的目标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，通过整合空间 转录组学，预算中的空间基因组学以及连续的超微结构定量 组织学切片。特定的目标3将开发一种机器学习算法，以预测临床结果 人类肺纤维化，通过统一组织病理结构，蛋白质和细胞空间网络以及 临床元数据。最终，该项目将建立一个多运动，跨物种和计算严格的 PF进展和修复的地图集，可以识别生物学保守的机械途径和临床 预后和治疗发展的相关目标。