整合NMR和MS改善新陈代谢分析:从方法到应用
Patricia Homobono Brito de Moura1,2,3, Guillaume Leleu1, Grégory Da Costa1
1Bordeaux INP, INRAE, Bordeaux Sciences Agro, OENO, UMR 1366, ISVV, University of Bordeaux, 33140 Villenave d'Ornon, France.
Molecules (Basel, Switzerland)
|June 27, 2025
概括
数据融合结合了核磁共振 (NMR) 和质谱 (MS) 进行增强的代谢. 这种方法整合了互补的数据,提供了对生物系统的更全面的理解.
科学领域:
- 代谢学 代谢学 代谢学
- 分析化学 分析化学
- 生物化学 生物化学
背景情况:
- 代谢学利用质谱学 (MS) 和核磁共振 (NMR) 谱学进行代谢物分析.
- MS具有高灵敏度,但在结构数据有限的情况下具有破坏性;NMR是非破坏性的,使结构阐明和量化成为可能.
- 多发性硬化和核磁共振的互补优势需要综合方法来进行全面的代谢分析.
研究的目的:
- 在过去十年中,审查数据融合 (DF) 方法,将NMR和MS数据整合到代谢学中.
- 分析各种融合技术,统计方法,以及它们在代谢学研究中的应用.
- 突出DF在促进代谢学研究中的日益重要的意义.
主要方法:
- 在SciFinder,Scopus和Clarivate Web of Science数据库中进行全面的文献搜索.
- 在代谢学中分析使用数据融合策略对NMR和MS数据的研究.
- 综合技术,统计模型及其报告结果的系统审查.
主要成果:
- 数据融合 (DF) 策略越来越多地被采用,以在代谢学中利用互补的NMR和MS数据.
- 通过整合不同的数据类型,DF提高了代谢分析的全面性.
- 审查的方法证明了DF在各种生物矩阵中的实用性,包括临床,植物和食品样本.
结论:
- 数据融合对于最大限度地利用NMR和MS在代谢学中获得的信息至关重要.
- 整合NMR和MS数据提供了对代谢途径和生物化学过程的更全面的了解.
- 在代谢学中DF的应用正在扩大,为复杂的生物系统提供了强大的洞察力.
相关概念视频
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