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Spatial Metabolomics for Human Kidneys

Spatial Metabolomics for Human Kidneys
人类肾脏空间代谢组学
批准号:
10222128
负责人:
Kumar Sharma
金额:
$18.65万
依托单位国家:
美国
项目类别:
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-09-15 至 2022-06-30

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中文摘要
翻译
项目摘要/摘要 急性肾脏损伤(AKI)和慢性肾脏疾病(CKD)是总发病率和 美国患者的死亡率。拟议研究的总体目标是建立无针对性和 靶向空间代谢组学分析正常和病变肾脏组织以评估细胞 代谢状态与健康功能、急性损伤、慢性疾病和康复有关。我们将聘用 超高分辨率成像质谱学(如MALDI-FTICR-IMS)与新颖的 用于代谢物注释的生物信息学(例如,METASPACE)和用于识别 病变肾脏与正常肾脏的代谢变化。我们的组织审问地点将是 由翻译肾病领导和影像质量组成的多学科协同计划 UCSD的光谱,太平洋西北国家大学用于多组学分析的优秀设施 实验室(PNNL),以及用于质谱学成像和三维重建的生物信息学 欧洲分子生物学实验室(EMBL)。每个阶段(即UG3和UH3)的三个具体目标是 建议。特别是,我们将为人类建立一个非靶向和靶向的空间代谢组学平台 并开发一个开放的生物信息学平台,用于数据查询、三维重建、 分子解读和公共分享。在UH3阶段,非定向平台和定向平台将 扩大规模以提高代谢物覆盖率并开发特定肾脏的关键代谢途径 AKI、CKD和疾病亚组患者肾脏中的间隔区。此外,生物信息学 该平台还将扩大规模,建立基于3D(下一代技术)的肾脏代谢物地图 正常和病变肾脏的结构。将实施一个网络服务SM-Kidney作为在线平台 将所有数据集和结果公开共享。我们会作出贡献,并与中环合作 HUB(CH)整合我们的服务,形成分子肾脏图谱。通过强大的协作 在来自UCSD、PNNL、EMBL等大学、研究所和行业合作伙伴的关键人员中, 我们的多学科团队将在UG3和UH3阶段达到关键里程碑。里程碑包括 1)建立了一个全面的、非靶向的正常人代谢物注释数据库 肾脏隔室,2)对选定的代谢物类别和 途径,3)满足组织采购、初始处理的严格质量控制标准的方法和标准操作规程 以及具有光学成像的IMS,以及4)向在线服务SM-Kidney提供图形用户界面 评估与肾脏疾病和发病机制相关的空间代谢特征。所有协议,样本, 正常、AKI和CKD样本的数据和代谢物图谱将在KPMP中共享。
英文摘要
Project Summary / Abstract Acute kidney injury (AKI) and chronic kidney disease (CKD) are major contributors to overall morbidity and mortality in patients in the US. The overall objective of the proposed study is to establish untargeted and targeted spatial metabolomics analysis of tissues from normal and diseased kidneys to assess cellular metabolic states associated with healthy function, acute injury, chronic condition, and recovery. We will employ ultra-high resolving power imaging mass spectrometry (e.g., MALDI-FTICR-IMS) complemented with novel bioinformatics (e.g., METASPACE) for metabolite annotation and big data interrogation strategies to identify alterations of metabolism in diseased kidneys compared with normal ones. Our Tissue Interrogation Site will be a multi-disciplinary coordinated program composed of leadership in translational nephrology and imaging mass spectrometry at UCSD, outstanding facilities for multi-omics analysis at Pacific Northwest National Laboratories (PNNL), and bioinformatics for mass spectrometry imaging and 3-D reconstruction housed at European Molecular Biology Laboratories (EMBL). Three specific aims in each phase (i.e., UG3 and UH3) are proposed. In particular, we will establish an untargeted and targeted spatial metabolomics platforms for human kidney interrogation and develop an open bioinformatics platform for data interrogation, 3-D reconstruction, molecular interpretation and public sharing. In the UH3 phase, the untargeted and targeted platforms will be scaled up to improve metabolite coverage and develop key metabolic pathways for specific renal compartments in kidneys from individuals with AKI, CKD, and disease subgroups. Moreover, the bioinformatics platform will also be scaled up to establish 3D (a next-generation technology) metabolite-based maps of kidney structure in normal and diseased kidneys. A web-service SM-Kidney will be implemented as the online platform to have all the datasets and the results publicly sharable. We will contribute and collaborate with the Central Hub (CH) to integrate our service for the formation of a molecular kidney atlas. With strong collaborations among the key personnel from UCSD, PNNL, EMBL, and other universities, institutes, and industry partners, our multidisciplinary team will reach key milestones during both the UG3 and UH3 phases. Milestones include 1) the development of a comprehensive untargeted database of metabolite annotations in the normal human renal compartments, 2) targeted spatial metabolomics analysis for selected classes of metabolites and pathways, 3) methods and SOPs that meet rigorous QC standards for tissue procurement, initial processing and IMS with optical imaging and 4) providing the online service SM-Kidney with a graphical user interface to evalaute spatial metabolic profiles associated with kidney disease and pathogenesis. All protocols, samples, data, and metabolite atlas of normal, AKI, and CKD samples will be shared across the KPMP.
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