Metabolites identification in non-targeted metabolomics approaches : a multi-analytical mass spectrometry platform view
Year: 2014
Type: conference-paper
Abstract: Metabolomics is an area of increased interest in many scientific fields such as chemical food safety and toxicology in order to investigate and identify (bio)markers of exposure, effect and/or susceptibility. Biological samples contain hundreds or even thousands of metabolites with different physicochemical properties limiting the analysis of the entire metabolome using an unique analytical technique. Therefore, the use of a multi-analytical platform in combination with appropriated sample preparation is required to enlarge the number of metabolites detected. However, one of the most important and challenging steps of a metabolomics study is the unequivocal identification of the detected metabolites [1]. In general, different public databases are available but the most straightforward approach to obtain the unambiguous confirmation of the identity of metabolites is to analyse a commercially available standard compound using the same analytical system. As a result, an in house-database containing information about chromatographic retention time, elemental composition and fragmentation pattern has been developed to facilitate metabolite identification [2]. In addition, an automatic Excel© macro was developed to help filtering and allowing a fast and direct annotation of compounds. Criteria as retention time, accurate mass and isotopic pattern have established as a primary identification. Nevertheless, these criteria are not always sufficient for unequivocal identification and the use of tandem mass spectrometry and/or other analytical techniques are necessary. As a result a multi-analytical platform based on LC-HRMS using the complementarities of reversed phase and HILIC, and GC-MS has been developed for a comprehensive metabolomics characterization. [1] Creek, D.J., et al., Metabolomics, 2014, 350-353.[2] Courant, F., et al., Analyst, 2012, 4958–4967.
Source: HAL
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7223427079