Premium
Promises and pitfalls of untargeted metabolomics
Author(s) -
Gertsman Ilya,
Barshop Bruce A.
Publication year - 2018
Publication title -
journal of inherited metabolic disease
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.462
H-Index - 102
eISSN - 1573-2665
pISSN - 0141-8955
DOI - 10.1007/s10545-017-0130-7
Subject(s) - metabolomics , metabolite profiling , computational biology , metabolite , metabolome , biomarker discovery , profiling (computer programming) , biology , bioinformatics , proteomics , computer science , biochemistry , gene , operating system
Metabolomics is one of the newer omics fields, and has enabled researchers to complement genomic and protein level analysis of disease with both semi‐quantitative and quantitative metabolite levels, which are the chemical mediators that constitute a given phenotype. Over more than a decade, methodologies have advanced for both targeted (quantification of specific analytes) as well as untargeted metabolomics (biomarker discovery and global metabolite profiling). Untargeted metabolomics is especially useful when there is no a priori metabolic hypothesis. Liquid chromatography coupled to mass spectrometry (LC‐MS) has been the preferred choice for untargeted metabolomics, given the versatility in metabolite coverage and sensitivity of these instruments. Resolving and profiling many hundreds to thousands of metabolites with varying chemical properties in a biological sample presents unique challenges, or pitfalls. In this review, we address the various obstacles and corrective measures available in four major aspects associated with an untargeted metabolomics experiment: (1) experimental design, (2) pre‐analytical (sample collection and preparation), (3) analytical (chromatography and detection), and (4) post‐analytical (data processing).