Metabolomics
Metabolomics
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DOI：
10.1002/9783527823468.ch9
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发表时间：
2021-06
影响因子：
8.4
通讯作者：
Tomek Diederen;A. Delabrière;Alaa Othman;M. Reid;Nicola Zamboni
中科院分区：
文献类型：
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作者：
Tomek Diederen;A. Delabrière;Alaa Othman;M. Reid;Nicola Zamboni
本章描述了基于质谱（MS）的代谢组学的方法、技术和算法基础。代谢组学分析分为两个互补的类别：靶向和非靶向。现代质谱仪以前所未有的灵敏度和速度联合并行检测大量代谢物。在生物样品的分析中，分离技术用于进一步减少干扰，区分MS无法分辨的代谢物，或通过保留时间信息支持代谢物的鉴定。代谢组学中的数据分析旨在从MS谱中识别和定量代谢物。单细胞代谢组学是代谢工程研究克隆效应、转录噪声的后果或亚群出现的进一步趋势主题。本章提供了代谢组学如何在代谢工程中使用的代表性例子。具有数万种突变体的大规模代谢组学研究即将到来，并有望支持筛选更好的生产者，并解开有利于产品形成的细胞内状态。
This chapter describes the methodological, technical, and algorithmic fundamentals of mass spectrometry (MS)‐based metabolomics. Metabolomics analyses fall into two complementary categories: targeted and untargeted. Modern mass spectrometers unite parallel detection of large sets of metabolites with unprecedented sensitivity and speed. In the analysis of biological samples, separation techniques are used to further reduce interferences, distinguish metabolites that cannot be resolved by MS, or support identification of metabolites with retention time information. Data analysis in metabolomics aims at identifying and quantifying metabolites from MS spectra. Single cell metabolomics is a further trending topic of relevance in metabolic engineering to investigate clonal effects, the consequence of transcriptional noise, or the emergence of subpopulations. The chapter provides representative examples on how metabolomics is used in metabolic engineering. Large‐scale metabolomics studies with tens of thousands of mutants are in reach, and hold promise to support screening for better producers and unravel intracellular state that is beneficial for product formation.