Thinking outside the cell: Leveraging HuBMAP data to build the human ECM atlas

细胞外思考：利用 HuBMAP 数据构建人类 ECM 图谱

• 批准号: 10649523
• 负责人: Yu Gao
• 金额: $ 46.5万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10649523
• 关键词: 3-Dimensional; Aging; Antigens; Atlases; Basement membrane; Binding; Binding Proteins; Biochemical; Biology; Biomedical Research; Cardiovascular Diseases; Cell Surface Receptors; Cell physiology; Cells; Collaborations; Collagen; Communities; Complex; Computational Biology; Computer Models; Data; Data Collection; Data Set; Databases; Degenerative polyarthritis; Development; Diagnostic; Disease; ECM receptor; Effectiveness; Ehlers-Danlos Syndrome; Enhance LC; Ensure; Etiology; Extracellular Matrix; Extracellular Matrix Proteins; Extracellular Space; Fibrosis; Future; Genes; Goals; Growth Factor; Health; Hereditary nephritis; Human; Human BioMolecular Atlas Program; Image; Knowledge; Level of Evidence; Link; Malignant Neoplasms; Maps; Marfan Syndrome; Mass Spectrum Analysis; Methods; Modality; Modeling; Morphogenesis; Musculoskeletal Diseases; Myopathy; Organ; Pathway interactions; Physiology; Population; Post-Translational Protein Processing; Preparation; Process; Production; Proliferating; Proteins; Proteoglycan; Proteome; Proteomics; Protocols documentation; RNA; Receptor Gene; Resolution; Resources; Role; Sampling; Set protein; Signal Pathway; Signal Transduction; Technology; Therapeutic; Thinking; Time; Tissues; Transcript; Visualization; cell type; cellular transduction; computerized data processing; data analysis pipeline; human tissue; intercellular connection; interstitial; member; migration; protein expression; proteogenomics; scaffold; single-cell RNA sequencing; technology development; tissue mapping; transcriptomics

Project summary The extracellular matrix (ECM) is a complex meshwork of hundreds of proteins that constitute the scaffold that holds our cells together. However, the functions of the ECM extend far beyond its structural roles. ECM proteins provide biochemical signals, either directly, by binding to cell surface receptors, or indirectly, by modulating growth factor signaling, that regulate many essential pathways controlling cellular functions, from proliferation and survival to migration and differentiation, all key to tissue and organ functions. Alteration of the ECM is linked to many diseases, including congenital diseases (e.g., Marfan syndrome, Alport syndrome, Ehlers–Danlos syndrome), musculo-skeletal diseases (e.g., osteoarthritis, myopathies), cardiovascular diseases, fibrosis, and cancer. Yet, despite its importance, the ECM remains largely underexplored. For example, we have yet to decipher the ECM protein composition (or “matrisome”) of organs, of tissues, and, within tissues, of specialized niches. We also do not fully understand which cell types produced which ECM proteins, nor do we know how the composition of the ECM changes over time and during diseases. These gaps in knowledge are mainly due to the lack of adequate methods to study the ECM. The secretion and post-translational modifications that accumulate in the ECM over time are critical for proper ECM functions and cannot be fully studied by RNA-level observations only. Thus, protein-level evidence is key to understand the function and dynamics of the ECM. However, ECM proteins, being typically very large, heavily post-translationally modified, and, overall, highly insoluble, are under-represented in global proteomic datasets. We propose to fill these gaps in knowledge by contributing our expertise in ECM biology, ECM proteomics, and computational biology to the technology- development and mapping efforts of the Human BioMolecular Atlas Program (HuBMAP), and ultimately build spatially-resolved maps of the matrisome of all organs. To achieve this goal, we will pursue the following aims: 1) re-analyze the vast amount of single-cell RNA-seq data generated by HuBMAP to identify the cell populations expressing ECM and ECM receptor gene transcripts for all organs, 2) integrate existing imaging data and mass spectrometry data generated by the HuBMAP to build a model to predict protein co-expression and create spatially-resolved tissue maps of the ECM, 3) contribute our 10+ years of expertise in ECM proteomics to ensure the effectiveness of future data collection, to capture ECM-relevant information, by members of the HuBMAP. For our efforts to benefit the entire scientific community, we will deploy all datasets and technologies via the HuBMAP portal and via MatrisomeDB, the ECM protein knowledge database we have previously developed. This mapping effort will constitute a first step toward understanding the roles of the ECM in health and diseases and toward the development of future ECM-focused diagnostic and therapeutic strategies.

项目摘要 细胞外基质（ECM）是数百种蛋白质的复杂网格，构成脚手架 将我们的细胞固定在一起。但是，ECM的功能远远超出了其结构作用。 ECM蛋白质 通过调节，直接与细胞表面受体结合或间接提供生化信号 生长因子信号传导，这些信号传导从增殖中调节许多控制细胞功能的基本途径 以及迁移和分化的生存，这都是组织和器官功能的关键。 ECM的改变是链接的 对于许多疾病，包括先天性疾病（例如，马凡综合症，埃勒斯综合症，埃勒斯 - 丹洛斯 综合征），肌肉骨骼疾病（例如骨关节炎，肌病），心血管疾病，纤维化和 癌症。然而，目的地的重要性，ECM在很大程度上仍未得到充实。例如，我们还没有 解读组织，组织和组织内器官的ECM蛋白组成（或“矩阵”） 利基。我们也不完全了解哪种细胞类型产生了哪种ECM蛋白，也不知道如何 ECM的组成随时间和疾病期间变化。知识中的这些差距主要是由于 缺乏研究ECM的足够方法。分泌和翻译后修改 随着时间的推移积累在ECM中对于适当的ECM函数至关重要，无法通过RNA级别进行全面研究 仅观察。这是蛋白质水平的证据是了解ECM的功能和动力学的关键。 但是，ECM蛋白质通常非常大，经过重大的翻译后修饰，总体而言， 不溶性在全球蛋白质组学数据集中的代表性不足。我们建议通过 为技术贡献我们在ECM生物学，ECM蛋白质组学和计算生物学方面的专业知识 - 人类生物分子图集计划（Hubmap）的开发和映射工作，并最终建立 所有器官的生成体的空间分辨图。为了实现这一目标，我们将追求以下目标： 1）重新分析Hubmap生成的大量单细胞RNA-seq数据，以识别细胞种群 表达所有器官的ECM和ECM受体基因转录本，2）整合现有成像数据和质量 Hubmap生成的光谱数据数据构建模型以预测蛋白质共表达并创建 ECM的空间分辨组织图，3）在ECM蛋白质组学方面贡献了10多年的专业知识，以确保 Hubmap成员捕获未来数据收集的有效性，以捕获与ECM相关的信息。 为了使整个科学界受益，我们将通过 Hubmap门户网站和通过MatrisomedB，我们先前开发的ECM蛋白知识数据库。 这种映射工作将构成了解ECM在健康和疾病中的作用的第一步 以及发展未来以ECM为重点的诊断和治疗策略。