scholarly journals Systems-Level Annotation of Metabolomics Data Reduces 25,000 Features to Fewer than 1,000 Unique Metabolites

2017 ◽  
Author(s):  
Nathaniel G. Mahieu ◽  
Gary J. Patti

SUMMARYWhen 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 metabolomic 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 ~2,961. We then applied an orthogonal approach to remove non-biological features from the data by using the 13C-based credentialing technology. This further reduced the number of unique analytes to less than 1,000.

2020 ◽  
Author(s):  
Piyi Xing ◽  
Zhenqiao Song ◽  
Xingfeng Li

AbstractWheatgrass has emerged as a functional food source in recent years, but the detailed metabolomics basis for its health benefits remains poorly understood. In this study, liquid chromatography-mass spectrometry (LC-MS) analysis were used to study the metabolic profiling of seedlings from wheat, barley, rye and triticale, which revealed 1800 features in positive mode and 4303 features in negative mode. Principal component analysis (PCA) showed clear differences between species, and 164 differentially expressed metabolites (DEMs) were detected, including amino acids, organic acids, lipids, fatty acids, nucleic acids, flavonoids, amines, polyamines, vitamins, sugar derivatives and others. Unique metabolites in each species were identified. This study provides a glimpse into the metabolomics profiles of wheat and its wild relatives, which may form an important basis for nutrition, health and other parameters.Practical ApplicationThis manuscript present liquid chromatography-mass spectrometry (LC-MS) results of young sprouts of common wheat and its relatives. Our results may help to better understand the natural variation due to the genotype before metabolomics data are considered for application to wheatgrass and can provide a basis (assessment) for its potential pharmaceutical and nutritional value.


2012 ◽  
Vol 84 (22) ◽  
pp. 9848-9857 ◽  
Author(s):  
Andrew A. Vaughan ◽  
Warwick B. Dunn ◽  
J. William Allwood ◽  
David C. Wedge ◽  
Fiona H. Blackhall ◽  
...  

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