Issue 96, 2015

Current state of the art of mass spectrometry-based metabolomics studies – a review focusing on wide coverage, high throughput and easy identification

Abstract

Metabolomics aims at the comprehensive assessment of a wide range of endogenous metabolites and attempts to identify and quantify the attractive metabolites in a given biological sample. These metabolites have diverse physicochemical properties and are presented at different concentration ranges, which makes global analysis a difficult challenge. As a commonly used analytical platform, mass spectrometry (MS) coupled with various separation techniques, such as gas chromatography (GC) and liquid chromatography (LC), has recently undergone rapid development, providing promising solutions to these problems. The ambient ionization techniques, including desorption electrospray ionization (DESI), direct analysis in real time (DART) and extractive electrospray ionization (EESI), enable rapid detection of metabolites, making it ideal for high-throughput analysis in large-scale metabolomics studies. The current applications of these approaches are described with selected illustrative examples in the present review. Furthermore, regardless of “targeted” or “non-targeted” metabolomics study, the identification of the attractive biomarkers is required to further interpret the related metabolic pathways. Therefore, in the present review, recent novel MS-based techniques that allow more robust and easier metabolite identification are summarized, and their strengths and limitations are also discussed.

Graphical abstract: Current state of the art of mass spectrometry-based metabolomics studies – a review focusing on wide coverage, high throughput and easy identification

Article information

Article type
Review Article
Submitted
16 Jul 2015
Accepted
03 Sep 2015
First published
03 Sep 2015

RSC Adv., 2015,5, 78728-78737

Author version available

Current state of the art of mass spectrometry-based metabolomics studies – a review focusing on wide coverage, high throughput and easy identification

Y. Wang, S. Liu, Y. Hu, P. Li and J. Wan, RSC Adv., 2015, 5, 78728 DOI: 10.1039/C5RA14058G

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