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系统级注释代谢组学数据集将 25000 个特征减少到不到 1000 个独特代谢物。

Systems-Level Annotation of a Metabolomics Data Set Reduces 25 000 Features to Fewer than 1000 Unique Metabolites.

机构信息

Department of Chemistry, Washington University , St. Louis, Missouri 63130, United States.

出版信息

Anal Chem. 2017 Oct 3;89(19):10397-10406. doi: 10.1021/acs.analchem.7b02380. Epub 2017 Sep 15.

DOI:10.1021/acs.analchem.7b02380
PMID:28914531
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6427824/
Abstract

When using liquid chromatography/mass spectrometry (LC/MS) to perform untargeted metabolomics, it is now routine to detect tens of thousands of features from biological samples. Poor understanding of the data, however, has complicated interpretation and masked the number of unique metabolites actually being measured in an experiment. Here we place an upper bound on the number of unique metabolites detected in Escherichia coli samples analyzed with one untargeted metabolomics method. We first group multiple features arising from the same analyte, which we call "degenerate features", using a context-driven annotation approach. Surprisingly, this analysis revealed thousands of previously unreported degeneracies that reduced the number of unique analytes to ∼2961. We then applied an orthogonal approach to remove nonbiological features from the data using the C-based credentialing technology. This further reduced the number of unique analytes to less than 1000. Our 90% reduction in data is 5-fold greater than previously published studies. On the basis of the results, we propose an alternative approach to untargeted metabolomics that relies on thoroughly annotated reference data sets. To this end, we introduce the creDBle database ( http://creDBle.wustl.edu ), which contains accurate mass, retention time, and MS/MS fragmentation data as well as annotations of all credentialed features.

摘要

当使用液相色谱/质谱(LC/MS)进行非靶向代谢组学分析时,现在通常可以从生物样本中检测到数万种特征。然而,由于对数据的理解不足,这使得解释变得复杂,并掩盖了实验中实际测量的独特代谢物的数量。在这里,我们对使用一种非靶向代谢组学方法分析的大肠杆菌样本中检测到的独特代谢物的数量设置了一个上限。我们首先使用基于上下文的注释方法对来自同一分析物的多个特征(我们称之为“退化特征”)进行分组。令人惊讶的是,这项分析揭示了数千个以前未报告的退化,将独特分析物的数量减少到约 2961 个。然后,我们使用基于 C 的认证技术,采用正交方法从数据中去除非生物学特征。这进一步将独特分析物的数量减少到不到 1000 个。我们的数据减少了 90%,比以前发表的研究多了 5 倍。基于这些结果,我们提出了一种替代的非靶向代谢组学方法,该方法依赖于经过充分注释的参考数据集。为此,我们引入了 creDBle 数据库(http://creDBle.wustl.edu),其中包含准确的质量、保留时间和 MS/MS 碎片数据以及所有认证特征的注释。

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