Shiqi Chen , Yifan Li , Huixia Zhang , Jingguang Li , Liu Yang , Qiqi Wang , Shuai Zhang , Pengjie Luo , Hongping Wang , Haiyang Jiang
{"title":"多层视觉代谢组学分析框架,加强对枸杞功能成分的探索","authors":"Shiqi Chen , Yifan Li , Huixia Zhang , Jingguang Li , Liu Yang , Qiqi Wang , Shuai Zhang , Pengjie Luo , Hongping Wang , Haiyang Jiang","doi":"10.1016/j.foodchem.2025.143583","DOIUrl":null,"url":null,"abstract":"<div><div>Wolfberry, regarded as a nutritious fruit, has garnered significant attention in the food industry due to potential health benefits. However, the tissue-specific distribution and dynamic accumulation patterns of nutritional metabolites such as flavonoids are still unclear. In this study, a novel spatial metabolomics framework was developed, incorporating instrumental optimization, metabolite identification, molecular network analysis, metabolic pathway mapping, and machine learning-based imaging. Using DESI-MSI, this approach enabled rapid, non-destructive, in situ analysis of wolfberry metabolites with enhanced sensitivity and spatial resolution. Detailed insights into chemical and spatial changes during ripening were obtained, with a focus on flavonoids. The visualization of the flavonoid biosynthetic pathway highlighted the impact of C-3 hydroxylation on flavonoid redistribution. Furthermore, a classification model achieved a prediction accuracy exceeding 99 %, consistent with metabolic network analyses. This framework provides a powerful tool for plant metabolomics, facilitating the exploration of functional components and metabolic pathways.</div></div>","PeriodicalId":318,"journal":{"name":"Food Chemistry","volume":"477 ","pages":"Article 143583"},"PeriodicalIF":9.8000,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multilayered visual metabolomics analysis framework for enhanced exploration of functional components in wolfberry\",\"authors\":\"Shiqi Chen , Yifan Li , Huixia Zhang , Jingguang Li , Liu Yang , Qiqi Wang , Shuai Zhang , Pengjie Luo , Hongping Wang , Haiyang Jiang\",\"doi\":\"10.1016/j.foodchem.2025.143583\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Wolfberry, regarded as a nutritious fruit, has garnered significant attention in the food industry due to potential health benefits. However, the tissue-specific distribution and dynamic accumulation patterns of nutritional metabolites such as flavonoids are still unclear. In this study, a novel spatial metabolomics framework was developed, incorporating instrumental optimization, metabolite identification, molecular network analysis, metabolic pathway mapping, and machine learning-based imaging. Using DESI-MSI, this approach enabled rapid, non-destructive, in situ analysis of wolfberry metabolites with enhanced sensitivity and spatial resolution. Detailed insights into chemical and spatial changes during ripening were obtained, with a focus on flavonoids. The visualization of the flavonoid biosynthetic pathway highlighted the impact of C-3 hydroxylation on flavonoid redistribution. Furthermore, a classification model achieved a prediction accuracy exceeding 99 %, consistent with metabolic network analyses. This framework provides a powerful tool for plant metabolomics, facilitating the exploration of functional components and metabolic pathways.</div></div>\",\"PeriodicalId\":318,\"journal\":{\"name\":\"Food Chemistry\",\"volume\":\"477 \",\"pages\":\"Article 143583\"},\"PeriodicalIF\":9.8000,\"publicationDate\":\"2025-02-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Food Chemistry\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0308814625008349\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Food Chemistry","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0308814625008349","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}
Multilayered visual metabolomics analysis framework for enhanced exploration of functional components in wolfberry
Wolfberry, regarded as a nutritious fruit, has garnered significant attention in the food industry due to potential health benefits. However, the tissue-specific distribution and dynamic accumulation patterns of nutritional metabolites such as flavonoids are still unclear. In this study, a novel spatial metabolomics framework was developed, incorporating instrumental optimization, metabolite identification, molecular network analysis, metabolic pathway mapping, and machine learning-based imaging. Using DESI-MSI, this approach enabled rapid, non-destructive, in situ analysis of wolfberry metabolites with enhanced sensitivity and spatial resolution. Detailed insights into chemical and spatial changes during ripening were obtained, with a focus on flavonoids. The visualization of the flavonoid biosynthetic pathway highlighted the impact of C-3 hydroxylation on flavonoid redistribution. Furthermore, a classification model achieved a prediction accuracy exceeding 99 %, consistent with metabolic network analyses. This framework provides a powerful tool for plant metabolomics, facilitating the exploration of functional components and metabolic pathways.
期刊介绍:
Food Chemistry publishes original research papers dealing with the advancement of the chemistry and biochemistry of foods or the analytical methods/ approach used. All papers should focus on the novelty of the research carried out.