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成像技术与空间分辨质谱技术在肾脏病学中的应用

Imaging and spatially resolved mass spectrometry applications in nephrology.

作者信息

Gorman Brittney L, Shafer Catelynn C, Ragi Nagarjunachary, Sharma Kumar, Neumann Elizabeth K, Anderton Christopher R

机构信息

Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA.

Department of Chemistry, University of California, Davis, Davis, CA, 95695, USA.

出版信息

Nat Rev Nephrol. 2025 Jun;21(6):399-416. doi: 10.1038/s41581-025-00946-1. Epub 2025 Mar 27.

Abstract

The application of spatially resolved mass spectrometry (MS) and MS imaging approaches for studying biomolecular processes in the kidney is rapidly growing. These powerful methods, which enable label-free and multiplexed detection of many molecular classes across omics domains (including metabolites, drugs, proteins and protein post-translational modifications), are beginning to reveal new molecular insights related to kidney health and disease. The complexity of the kidney often necessitates multiple scales of analysis for interrogating biofluids, whole organs, functional tissue units, single cells and subcellular compartments. Various MS methods can generate omics data across these spatial domains and facilitate both basic science and pathological assessment of the kidney. Optimal processes related to sample preparation and handling for different MS applications are rapidly evolving. Emerging technology and methods, improvement of spatial resolution, broader molecular characterization, multimodal and multiomics approaches and the use of machine learning and artificial intelligence approaches promise to make these applications even more valuable in the field of nephology. Overall, spatially resolved MS and MS imaging methods have the potential to fill much of the omics gap in systems biology analysis of the kidney and provide functional outputs that cannot be obtained using genomics and transcriptomic methods.

摘要

空间分辨质谱(MS)和MS成像方法在研究肾脏生物分子过程中的应用正在迅速发展。这些强大的方法能够对跨组学领域的多种分子类别(包括代谢物、药物、蛋白质和蛋白质翻译后修饰)进行无标记和多重检测,开始揭示与肾脏健康和疾病相关的新分子见解。肾脏的复杂性通常需要对生物流体、整个器官、功能组织单位、单细胞和亚细胞区室进行多尺度分析。各种MS方法可以在这些空间域生成组学数据,并促进肾脏的基础科学研究和病理评估。针对不同MS应用的与样品制备和处理相关的最佳流程正在迅速发展。新兴技术和方法、空间分辨率的提高、更广泛的分子表征、多模态和多组学方法以及机器学习和人工智能方法的使用有望使这些应用在肾脏病学领域更具价值。总体而言,空间分辨MS和MS成像方法有潜力填补肾脏系统生物学分析中大部分的组学空白,并提供使用基因组学和转录组学方法无法获得的功能输出。

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