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FT-ICR-MS-based metabolomics: A deep dive into plant metabolism
Mass Spectrometry Reviews ( IF 6.9 ) Pub Date : 2021-09-20 , DOI: 10.1002/mas.21731
Marisa Maia 1, 2 , Andreia Figueiredo 2 , Carlos Cordeiro 1 , Marta Sousa Silva 1
Affiliation  

Metabolomics involves the identification and quantification of metabolites to unravel the chemical footprints behind cellular regulatory processes and to decipher metabolic networks, opening new insights to understand the correlation between genes and metabolites. In plants, it is estimated the existence of hundreds of thousands of metabolites and the majority is still unknown. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a powerful analytical technique to tackle such challenges. The resolving power and sensitivity of this ultrahigh mass accuracy mass analyzer is such that a complex mixture, such as plant extracts, can be analyzed and thousands of metabolite signals can be detected simultaneously and distinguished based on the naturally abundant elemental isotopes. In this review, FT-ICR-MS-based plant metabolomics studies are described, emphasizing FT-ICR-MS increasing applications in plant science through targeted and untargeted approaches, allowing for a better understanding of plant development, responses to biotic and abiotic stresses, and the discovery of new natural nutraceutical compounds. Improved metabolite extraction protocols compatible with FT-ICR-MS, metabolite analysis methods and metabolite identification platforms are also explored as well as new in silico approaches. Most recent advances in MS imaging are also discussed.

中文翻译:

基于 FT-ICR-MS 的代谢组学:深入研究植物代谢

代谢组学涉及代谢物的识别和定量,以揭示细胞调节过程背后的化学足迹并破译代谢网络,为理解基因和代谢物之间的相关性提供新的见解。在植物中,估计存在数十万种代谢物,但大多数仍然未知。傅里叶变换离子回旋共振质谱 (FT-ICR-MS) 是应对此类挑战的强大分析技术。这种超高质量精度质量分析仪的分辨率和灵敏度使得可以分析复杂的混合物,例如植物提取物,并且可以根据天然丰富的元素同位素同时检测和区分数千种代谢物信号。在这篇综述中,描述了基于 FT-ICR-MS 的植物代谢组学研究,强调 FT-ICR-MS 通过有针对性和无针对性的方法增加在植物科学中的应用,从而更好地了解植物发育、对生物和非生物胁迫的反应、以及新的天然营养保健化合物的发现。还探索了与 FT-ICR-MS 兼容的改进代谢物提取方案、代谢物分析方法和代谢物鉴定平台以及新的计算机方法。还讨论了 MS 成像的最新进展。
更新日期:2021-09-20
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