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

项目摘要 细胞外基质（ECM）是由数百种蛋白质组成的复杂网络， 将我们的细胞连接在一起然而，企业内容管理的职能远远超出其结构作用。ECM蛋白 通过与细胞表面受体结合直接提供生化信号，或通过调节 生长因子信号传导，调节许多控制细胞功能的重要途径， 从存活到迁移和分化，所有这些都是组织和器官功能的关键。ECM的改变与 许多疾病，包括先天性疾病（例如，马凡氏综合征、Alport综合征、Ehlers-Danlos二氏综合征 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，以及 癌然而，尽管ECM很重要，但它在很大程度上仍未得到充分探索。例如，我们还没有 译解器官、组织和组织内的特化细胞的ECM蛋白质组成（或“基质体”）。 壁龛我们也不完全了解哪种细胞类型产生哪种ECM蛋白，也不知道如何产生 ECM的组成随时间和在疾病期间发生变化。这些知识上的差距主要是由于 缺乏足够的方法来研究ECM。分泌和翻译后修饰， 随着时间的推移在ECM中积累对于适当的ECM功能至关重要，并且不能通过RNA水平进行充分研究。 只是观察。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，经过大量的后修饰，并且总体上高度依赖于细胞外基质。 不溶性，在全球蛋白质组数据集中代表不足。我们建议填补这些知识空白， 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， 人类生物分子图谱计划（HuBMAP）的开发和绘图工作，并最终建立 所有器官的基质体的空间分辨图。为了实现这一目标，我们将努力实现以下目标： 1)重新分析HuBMAP生成的大量单细胞RNA-seq数据，以识别细胞群 表达所有器官的ECM和ECM受体基因转录物，2）整合现有的成像数据和质量， 通过HuBMAP生成的光谱数据来构建模型，以预测蛋白质共表达并创建 ECM的空间分辨组织图，3）贡献了我们在ECM蛋白质组学方面10多年的专业知识，以确保 未来数据收集的有效性，以捕获ECM相关信息，由HUBMAP成员。 为了使整个科学界受益，我们将通过 HuBMAP门户和通过MatrisomeDB，ECM蛋白质知识数据库，我们以前开发的。 这项测绘工作将是理解ECM在健康和疾病中作用的第一步 以及未来ECM重点诊断和治疗策略的发展。

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.