研究空间代谢的小鼠器官组织大型代谢物成像库。

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Journal of Proteome Research Pub Date : 2025-06-06 Epub Date: 2025-04-28 DOI:10.1021/acs.jproteome.4c00594
Karl W Smith, Antonia Fecke, Siva Swapna Kasarla, Prasad Phapale
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引用次数: 0

摘要

质谱成像(MSI)通过实现高空间分辨率的小分子代谢物(SMMs)的可视化,彻底改变了组织代谢的研究。然而,目前缺乏针对不同器官组织的综合SMM影像数据库,阻碍了我们对空间器官代谢的认识。为了解决这一资源缺口,我们提出了一个针对小鼠脑、肾和肝脏的大规模SMM成像库,捕获了跨越8种化学超类和40多种代谢途径的SMM。人工管理和显示这些成像数据集揭示了代谢物的空间模式,这些代谢物在报告的器官中较少记录。具体来说,我们在脑冠状切片中鉴定了65个SMMs,在矢状组织切片中鉴定了71个SMMs,包括神经递质的空间模式。此外,我们在肾脏中绘制了98个smm,在肝脏中绘制了66个smm,从而深入了解了它们的氨基酸和谷胱甘肽代谢。我们富有洞察力的SMM影像库为空间代谢研究界提供了重要的资源,填补了重要的资源缺口。该资源可免费下载,并可通过BioImage Archive和METASPACE存储库访问,为潜在的未来计算模型提供高质量的注释图像,并在空间水平上推进我们对组织代谢的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Large-Scale Metabolite Imaging Gallery of Mouse Organ Tissues to Study Spatial Metabolism.

Mass spectrometry imaging (MSI) has revolutionized the study of tissue metabolism by enabling the visualization of small molecule metabolites (SMMs) with high spatial resolution. However, comprehensive SMM imaging databases for different organ tissues are lacking, hindering our understanding of spatial organ metabolism. To address this resource gap, we present a large-scale SMM imaging gallery for mouse brain, kidney, and liver, capturing SMMs spanning eight chemical super classes and encompassing over 40 metabolic pathways. Manual curation and display of these imaging data sets unveil spatial patterns of metabolites that are less documented in the reported organs. Specifically, we identify 65 SMMs in brain coronal sections and 71 in sagittal tissue sections, including spatial patterns for neurotransmitters. Furthermore, we map 98 SMMs in kidneys and 66 SMMs in liver, providing insights into their amino acid and glutathione metabolism. Our insightful SMM imaging gallery serves as a critical resource for the spatial metabolism research community, filling a significant resource gap. This resource is freely available for download and can be accessed through the BioImage Archive and METASPACE repositories, providing high-quality annotated images for potential future computational models and advancing our understanding of tissue metabolism at the spatial level.

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来源期刊
Journal of Proteome Research
Journal of Proteome Research 生物-生化研究方法
CiteScore
9.00
自引率
4.50%
发文量
251
审稿时长
3 months
期刊介绍: Journal of Proteome Research publishes content encompassing all aspects of global protein analysis and function, including the dynamic aspects of genomics, spatio-temporal proteomics, metabonomics and metabolomics, clinical and agricultural proteomics, as well as advances in methodology including bioinformatics. The theme and emphasis is on a multidisciplinary approach to the life sciences through the synergy between the different types of "omics".
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