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

基本信息

批准号：
10527519
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
项目类别：
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/10527519
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 Genomics 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 Proteins Proteoglycan Proteome Proteomics Protocols documentation RNA Receptor Gene Resolution Resources Role Sampling 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 scaffold single-cell RNA sequencing technology development tissue mapping transcriptomics

项目摘要

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.

项目概要细胞外基质 (ECM) 是由数百种蛋白质组成的复杂网络，构成了细胞外基质然而，ECM 的功能远远超出其结构作用。通过与细胞表面受体结合直接提供生化信号，或通过调节间接提供生化信号生长因子信号传导，调节许多控制细胞功能的重要途径，从增殖迁移和分化的生存，所有这些都与 ECM 的改变有关。许多疾病，包括先天性疾病（例如马凡综合征、阿尔波特综合征、埃勒斯-当洛斯综合征肌肉骨骼疾病（例如骨关节炎、肌病）、心血管疾病、纤维化和然而，尽管 ECM 很重要，但我们尚未对其进行充分的研究。破译器官、组织以及组织内专门的 ECM 蛋白质组成（或“基质体”）我们也不完全了解哪些细胞类型产生哪些 ECM 蛋白，也不知道如何产生。 ECM 的组成会随着时间和疾病发生而变化。这些知识差距主要是由于。缺乏足够的方法来研究 ECM 的分泌和翻译后修饰。随着时间的推移，ECM 中的积累对于 ECM 的正常功能至关重要，并且无法通过 RNA 水平进行充分研究因此，蛋白质水平的证据是理解 ECM 功能和动力学的关键。然而，ECM 蛋白通常非常大，经过大量翻译后修饰，并且总体而言，高度不可溶的，在全球蛋白质组数据集中代表性不足，我们建议通过以下方式填补这些知识空白。将我们在 ECM 生物学、ECM 蛋白质组学和计算生物学方面的专业知识贡献给该技术 - 人类生物分子图谱计划 (HuBMAP) 的开发和绘图工作，并最终建立所有器官基质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数据来识别细胞群体表达所有器官的 ECM 和 ECM 受体基因转录本，2) 整合现有的成像数据和质量 HuBMAP 生成的光谱数据可构建模型来预测蛋白质共表达并创建 ECM 的空间分辨组织图，3) 贡献我们在 ECM 蛋白质组学方面 10 多年的专业知识，以确保 HuBMAP 成员未来数据收集的有效性，以捕获 ECM 相关信息。为了使整个科学界受益，我们将通过以下方式部署所有数据集和技术： HuBMAP 门户和我们之前开发的 ECM 蛋白质知识数据库 MatrisomeDB。这项绘图工作将成为了解 ECM 在健康和疾病中的作用的第一步并致力于开发未来以 ECM 为重点的诊断和治疗策略。