Metabolism, downstream effectors of genomics, transcriptomics, and proteomics, can determine the potential of phenotype of an organism including plants. Profiling the global scenario of metabolism requires optimization of different solvent extraction methods. Here, we report an approach comparing three different metabolite extraction strategies, including ammonium acetate/methanol (AAM), water/methanol (WM), and sodium phosphate/methanol (PM) in soybean plant using ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS). Interestingly, both AAM and WM methods were found to cover a wider range of metabolites and provide better detection of molecular features than the PM method. Various clustering analyses based on multivariate statistical tools revealed that both AAM and WM methods showed tight and overlapping extraction strategy compared with the PM method. Using MatLab-based Mahalanobis distance (D M) calculation, statistically significant score plot separation was observed between AAM and PM, as well as WM and PM. However, no significant separation was observed between AAM and WM, which is expected from the overlap of principal component scores for these two methods. Using differential metabolite expression analysis, we identified that a large number of metabolites were extracted at a significantly higher level using AAM vs. PM. These comparative extraction methods suggest that AAM can effectively be applied for an LC/MS-based plant metabolomics profile study. Graphical abstract Step-by-step outline of three different metabolite extraction methods and data analysis.
Keywords: Extraction comparison; Liquid chromatography/mass spectrometry; Metabolite profiling; Metabolomics; Multivariate statistics.