Comprehensive reference atlas construction, geolocation and data integration for HuBMAP HIVE [5 of 5]

摘要 人类生物分子图谱计划(HuBMAP)正在重新定义我们对人体的理解 以前所未有的分辨率恢复多尺度的组织结构--解剖、组织和分子-- 通过各种实验测量的计算积分。目前的HuBMAP蜂巢已经领导了 人类共同坐标框架(CCF)的发展；在分子和空间上构建 已解析的人体组织参考图；开发了用于解释新的 数据集；并在HuBMAP内部以及与更广泛的 社区。在Y5-8，我们将建立人类参考地图集(HRA)，将HuBMAP门户网站建立为 人体组织参考图和多模式单细胞数据的“首选”资源。我们提出了一种映射 中心将在Y1-4取得进展的基础上再接再厉，实现这些目标。我们的中心代表着一种协同 三个实验室之间的合作：Rahul Satija博士(NYGC/NYU)、John Marioni博士(EMBL/EBI)和Jean博士 风扇(JHU)。我们的测绘中心建议领导HuBMAP的一系列关键任务。其中包括 构建30个人体组织的单细胞参考地图，其中地图将通过协调构建 多种多样的多模式单电池技术。我们还建议引领地图的发展 这些工具将使社区中的用户能够上传他们自己的数据集，并将它们映射到HuBMAP引用。 重要的是，这些工具不会局限于单细胞RNA测序数据集，而是将扩展到处理 测量染色质可及性、染色质修饰、DNA甲基化、细胞内和 表面蛋白质水平，以及代谢特征。我们还建议引领统计学的发展 方法对多个数据集进行比较分析。这将为两个用户启用关键用例 HuBMAP内部和外部，包括对健康和患病进行比较分析的能力 组织。我们期待我们的MC与HubMAP社区合作并带领他们更深入地了解 人类组织和组织的功能组织，并探索分子状态、环境和 解剖位置，影响细胞的特性和行为。

Abstract The Human BioMolecular Atlas Program (HuBMAP) is redefining our understanding of the human body by recovering multi-scale tissue organization--anatomical, histological, and molecular--at unprecedented resolution, through computational integration of diverse experimental measurements. The current HuBMAP HIVE has led the development of a human common coordinate framework (CCF); constructed molecularly and spatially resolved reference maps of human tissues; developed mapping frameworks for the interpretation of new datasets; and coordinated extensive collaborative activities both within HuBMAP and with the broader community. In Y5-8, we will construct a Human Reference Atlas (HRA), establishing the HuBMAP Portal as the “go-to” resource for human tissue reference maps and multimodal single-cell data. We propose a Mapping Center to build upon our progress in Y1-4, and achieve these goals. Our center represents a synergistic collaboration between three labs, Dr. Rahul Satija (NYGC/NYU), Dr. John Marioni (EMBL/EBI), and Dr. Jean Fan (JHU). Our Mapping Center proposes to lead a series of key tasks for HuBMAP. These include the construction of single-cell reference maps for 30 human tissues, where maps will be constructed by harmonizing a wide diversity of multimodal single-cell technologies. We also propose to lead the development of mapping tools that will enable users in the community to upload their own datasets and map them to HuBMAP references. Importantly, these tools will not be limited to single-cell RNA sequencing datasets, but will be extended to process datasets that measure chromatin accessibility, chromatin modifications, DNA methylation, intracellular and surface protein levels, and metabolic profiles as well. We also propose to lead the development of statistical methods to perform comparative analysis of multiple datasets. This will enable key use-cases for users both within and outside of HuBMAP, including the ability to perform comparative analysis of healthy vs. diseased tissue. We expect our MC to work together with and lead the HubMAP community to a deeper understanding of the functional organization of human tissues and, and to explore how molecular state, environment, and anatomical location, affect cellular identity and behavior.