Separation strategies for untargeted metabolomics

Abstract

Metabolomics has rapidly become a profiling technique of choice for biomarker elucidation and molecular diagnostics in addition to studies focused on understanding disease pathogenesis. Key to the success of metabolomics in these areas has been the techniques to separate and analyze the chemically diverse group of compounds comprising the metabolome by using global and untargeted approaches. Untargeted metabolomic efforts have the goal of examining as many metabolites as possible simultaneously and most frequently use an LC/MS-based approach. Here, the importance of LC in an untargeted metabolomic workflow is outlined and separation strategies for optimization are reviewed within the context of these criteria.

Metabolite Measurement: Pitfalls to Avoid and Practices to Follow

“…Instead, to obtain optimal coverage, one must employ multiple LC-MS approaches (49). Reversed-phase chromatography using a C18 column, while providing good separation of fatty acids and lipids, is not ideal for the analysis of polar metabolites due to poor retention on column.…”

Non-targeted screening approaches for contaminants and adulterants in food using liquid chromatography hyphenated to high resolution mass spectrometry

“…Separation strategies for untargeted metabolomics were reviewed previously, and include suggestions for potential column chemistries and ion pairing agents that can be implemented [42]. By using three different column chemistries (reverse phase, HILIC, and PFPP), Boudah et al determined that less than 12% of the detected entities were detected in all three separations [43], which emphasizes the advantage of analyzing samples under different conditions.…”

Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration

“…blood plasma and serum, urine, saliva, solid tissues and cultured cells). According to the kind of sample to analyze, several approaches are used, as described in details elsewhere [18][19][20][21]. Samples are usually homogenized or pulverized into smaller particles to increase their surface area for the exposure to the extraction buffer, which is chosen in accordance to their chemical characteristics.…”

“…Although several literature about metabolomic data production and analysis techniques exist [3][4][5][15][16][17][18][19][20][21][22][23], to the best of our knowledge, a comprehensive review illustrating the available tools for the analysis and integration of metabolomic data with other 'omics' data has not been reported yet. Here, we describe and compare four tools (MetaCore TM , MetaboAnalyst, InCroMAP and 3Omics [24][25][26][27]), which we selected among the several ones currently available for metabolomic data analysis and integration (Table 1 have not yet overcome the previous ones.…”