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

项目总结 细胞外基质(ECM)是由数百种蛋白质组成的复杂网络，这些蛋白质构成了 将我们的细胞凝聚在一起。然而，ECM的职能远远超出其结构作用。ECM蛋白 提供生化信号，直接通过与细胞表面受体结合，或通过调节 生长因子信号，调节许多控制细胞功能的基本途径，从增殖 存活到迁移和分化，都是组织和器官功能的关键。ECM的更改是关联的 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-丹洛斯综合征) 肌肉骨骼疾病(如骨关节炎、肌病)、心血管疾病、纤维化和 癌症。然而，尽管ECM很重要，但它在很大程度上仍未得到充分开发。例如，我们还没有 破译器官、组织的ECM蛋白质组成(或“母体”)，以及组织内特化的ECM蛋白质组成 利基市场。我们也不完全了解哪些细胞类型产生哪些细胞外基质蛋白，也不知道它们是如何产生的。 细胞外基质的组成随时间和疾病期间的变化而变化。这些知识上的差距主要是由于 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，在细胞外基质中积累对于正常的细胞外基质功能至关重要，并且不能通过RNA水平来完全研究 仅限观察。因此，蛋白质水平的证据是理解细胞外基质功能和动态的关键。 然而，ECM蛋白通常非常大，翻译后大量修饰，总体来说，高度 不可溶的，在全球蛋白质组数据集中表达不足。我们建议通过以下方式来填补这些知识空白 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- 开发和绘制人类生物分子图谱计划(HuBMAP)，并最终建立 所有器官的母体的空间分辨率地图。为实现这一目标，我们将努力实现以下目标： 1)重新分析HuBMAP产生的海量单细胞rna-seq数据以识别细胞群体 表达所有器官的细胞外基质和细胞外基质受体基因转录本，2)整合现有的成像数据和质量 由HuBMAP生成的光谱数据构建预测蛋白质共表达的模型并创建 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，即由HuBMAP成员收集与ECM相关的信息。 为了我们造福整个科学界的努力，我们将通过 HuBMAP门户网站和我们之前开发的ECM蛋白质知识库MatrisomeDB。 这一测绘工作将是了解ECM在健康和疾病中的作用的第一步 以及未来以ECM为重点的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.