Multimodal mass spectrometry imaging of mouse and human liver

项目总结 我们请求行政补充，以建立第一个空间分辨率的人类3D地图 肝脏首次将细胞成分和代谢产物以及细胞外基质整合在一起 时间到了。HuBMAP变革性技术开发部门之间的合作努力 由田博士(匹兹堡大学，匹兹堡)和斯托克韦尔博士(哥伦比亚大学，哥伦比亚大学)领导的团队 由Naba博士(伊利诺伊大学芝加哥分校，UIC)领导的演示计划小组正在 与HuBMAP的全面器官标测愿景一致，提供了前所未有的视角 进入肝脏结构，达到前所未有的深度。 已经进行了广泛的努力来描述细胞外基质(ECM)的组成-或者 不同器官的母体，但对其空间分布的研究还很缺乏。 包围细胞并对功能多细胞结构有贡献的细胞外基质成分，其 正常与疾病的差异，以及与细胞的分子信号/串扰。挑战在于 (1)缺乏一组有效的抗ECM抗体，(2)进行多路传输的困难 单细胞分辨率的多水平生物分子图谱，以及(3)集成的困难 在单个样本中使用多个“体态”模式生成的数据集。田博士领导的团队 开发了质谱学成像(H2O)n&gt；25K-GCIB-SIMS双SIMS工作流程，集成 非靶向代谢组学、脂类组学和靶向蛋白质组学(最多40个靶点) 亚细胞空间分辨率(1微米)的组织切片。与斯托克韦尔博士领导的团队一起， 已经建立了一个强大的多模式成像工作流程来生成空间分辨率地图集 显示肝组织的主要组织结构、细胞类型和细胞类型的代谢状态。 利用Naba博士获得的人肝基质组草稿和 MatrisomeDB，她的团队创建的ECM蛋白质组学数据集的数据库，我们建议创建 人类肝脏基本母体成分的空间分辨地图，以及我们的电流 在单细胞水平上的多路传输肝图。 该项目将提供一个新的机会来描绘细胞和 细胞外生物分子，功能组织单位的定义，包括关于 微环境的利基。这将允许在未来定义相互作用的性质 以及在细胞和其周围的细胞外基质之间建立的信号，并开始理解 与疾病相关的失调(例如，纤维化、肝硬变、肝细胞癌)。

Project summary We request an administrative supplement to build the first spatially-resolved 3D map of the human liver integrating both cellular components and metabolites and the extracellular matrix for the first time. This collaborative effort between the HuBMAP Transformative Technology Development team led by Dr. Tian (University of Pittsburgh, Pitt) and Dr. Stockwell (Columbia University, CU) and the Demonstration Program team led by Dr. Naba (University of Illinois Chicago, UIC) is in line with HuBMAP’s vision for comprehensive organ mapping, providing an unprecedented view into liver architecture with a depth never achieved before. An extensive effort has been made to profile the extracellular matrix (ECM) composition – or matrisomes – of various organs; however, there is still lack of study on the spatial distribution of ECM components surrounding cells and contributing to functional multicellular structures, their variation in normal vs. disease, and molecular signaling/crosstalk with cells. The challenges lie in (1) the lack of panels of validated anti-ECM antibodies, (2) the difficulty in performing multiplexed mapping of multi-level biomolecules at single-cell resolution, and (3) the difficulty to integrate datasets generated using multiple “-omic” modalities in a single sample. The team led by Dr. Tian developed a mass spectrometry imaging (H2O)n>25k-GCIB-SIMS dual-SIMS workflow, integrating untargeted metabolomics, lipidomics, and targeted proteomics (up to 40 targets) on the same tissue section at subcellular spatial resolution (1 µm). Together with the team led by Dr. Stockwell, a robust multimodal imaging workflow has been established to generate a spatially-resolved atlas of liver tissue, visualizing major tissue structures, cell types, and metabolic states of cell types. Leveraging the draft of the human liver matrisome obtained by Dr. Naba and the content of MatrisomeDB, the database of ECM proteomics datasets her team created, we propose to create a spatially-resolved map of essential human liver matrisome components, along with our current multiplexed liver map at the single-cell level. This project will present a new opportunity to delineate the spatial organization of cellular and extracellular biomolecules, the definition of functional tissue units, including information on microenvironmental niches. This will allow, in the future, to define the nature of the interactions and signals established between cells and their surrounding ECM, and to begin to understand disease-associated dysregulations (e.g., fibrosis, cirrhosis, hepatocellular carcinoma).