Harrison A. Clarke, Xin Ma, Cameron J. Shedlock, Terrymar Medina, Tara R. Hawkinson, Lei Wu, Roberto A. Ribas, Shannon Keohane, Sakthivel Ravi, Jennifer L. Bizon, Sara N. Burke, Jose Francisco Abisambra, Matthew E. Merritt, Boone M. Prentice, Craig W. Vander Kooi, Matthew S. Gentry, Li Chen, Ramon C. Sun
{"title":"脑代谢组、脂质组和血糖的空间映射","authors":"Harrison A. Clarke, Xin Ma, Cameron J. Shedlock, Terrymar Medina, Tara R. Hawkinson, Lei Wu, Roberto A. Ribas, Shannon Keohane, Sakthivel Ravi, Jennifer L. Bizon, Sara N. Burke, Jose Francisco Abisambra, Matthew E. Merritt, Boone M. Prentice, Craig W. Vander Kooi, Matthew S. Gentry, Li Chen, Ramon C. Sun","doi":"10.1038/s41467-025-59487-7","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":19066,"journal":{"name":"Nature Communications","volume":"2 1","pages":""},"PeriodicalIF":15.7000,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spatial mapping of the brain metabolome lipidome and glycome\",\"authors\":\"Harrison A. Clarke, Xin Ma, Cameron J. Shedlock, Terrymar Medina, Tara R. Hawkinson, Lei Wu, Roberto A. Ribas, Shannon Keohane, Sakthivel Ravi, Jennifer L. Bizon, Sara N. Burke, Jose Francisco Abisambra, Matthew E. Merritt, Boone M. Prentice, Craig W. Vander Kooi, Matthew S. Gentry, Li Chen, Ramon C. Sun\",\"doi\":\"10.1038/s41467-025-59487-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":19066,\"journal\":{\"name\":\"Nature Communications\",\"volume\":\"2 1\",\"pages\":\"\"},\"PeriodicalIF\":15.7000,\"publicationDate\":\"2025-05-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Communications\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41467-025-59487-7\",\"RegionNum\":1,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Communications","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41467-025-59487-7","RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Spatial mapping of the brain metabolome lipidome and glycome
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.
期刊介绍:
Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.