Clarke Harrison A, Ma Xin, Shedlock Cameron J, Medina Terrymar, Hawkinson Tara R, Wu Lei, Ribas Roberto A, Keohane Shannon, Ravi Sakthivel, Bizon Jennifer L, Burke Sara N, Abisambra Jose Francisco, Merritt Matthew E, Prentice Boone M, Vander Kooi Craig W, Gentry Matthew S, Chen Li, Sun Ramon C
Department of Biochemistry & Molecular Biology, College of Medicine, University of Florida, Gainesville, FL, USA.
Center for Advanced Spatial Biomolecule Research, University of Florida, Gainesville, FL, USA.
Nat Commun. 2025 May 12;16(1):4373. doi: 10.1038/s41467-025-59487-7.
Metabolites, lipids, and glycans are fundamental but interconnected classes of biomolecules that form the basis of the metabolic network. These molecules are dynamically channeled through multiple pathways that govern cellular physiology and pathology. Here, we present a framework for the simultaneous spatial analysis of the metabolome, lipidome, and glycome from a single tissue section using mass spectrometry imaging. This workflow integrates a computational platform, the Spatial Augmented Multiomics Interface (Sami), which enables multiomics integration, high-dimensional clustering, spatial anatomical mapping of matched molecular features, and metabolic pathway enrichment. To demonstrate the utility of this approach, we applied Sami to evaluate metabolic diversity across distinct brain regions and to compare wild-type and Ps19 Alzheimer's disease (AD) mouse models. Our findings reveal region-specific metabolic demands in the normal brain and highlight metabolic dysregulation in the Ps19 model, providing insights into the biochemical alterations associated with neurodegeneration.
代谢物、脂质和聚糖是构成代谢网络基础的基本但相互关联的生物分子类别。这些分子通过控制细胞生理和病理的多种途径动态传递。在这里，我们提出了一个使用质谱成像对单个组织切片中的代谢组、脂质组和糖组进行同步空间分析的框架。该工作流程集成了一个计算平台，即空间增强多组学接口（Sami），它能够实现多组学整合、高维聚类、匹配分子特征的空间解剖图谱绘制以及代谢途径富集。为了证明这种方法的实用性，我们应用Sami来评估不同脑区的代谢多样性，并比较野生型和Ps19阿尔茨海默病（AD）小鼠模型。我们的研究结果揭示了正常大脑中区域特异性的代谢需求，并突出了Ps19模型中的代谢失调，为与神经退行性变相关的生化改变提供了见解。
