Balancing metabolome coverage and reproducibility for untargeted NMR-based metabolic profiling in tissue samples through mixture design methods.

Abstract

Untargeted metabolomics attempts to acquire a comprehensive and reproducible set of small-molecule metabolites in biological systems. However, metabolite extraction method significantly affects the quality of metabolomics data. In the present study, we calculated the number of peaks (NP) and coefficient of variation (CV) to reflect metabolome coverage and reproducibility in untargeted NMR-based metabolic profiling of tissue samples in rats under different methanol/chloroform/water (MCW) extraction conditions. Different MCW extractions expectedly generated diverse characteristics of metabolome. Moreover, the classic MCW method revealed tissue-specific differences in the NP and CV values. To obtain high-quality metabolomics data, therefore, we used mixture design methods to optimize the MCW extraction strategy by maximizing the NP value and minimizing the CV value in each tissue sample. Results show that the optimal formulations of MCW extraction were 2:2:8 (ml/mg tissue) for brain sample, 2:4:6 (ml/mg tissue) for heart sample, 1.3:2:8.7 (ml/mg tissue) for liver sample, 4:2:6 (ml/mg tissue) for kidney sample, 2:3:7 (ml/mg tissue) for muscle sample, and 2:4:6 (ml/mg tissue) for pancreas sample. Therefore, these findings demonstrate that different tissue samples need a specific optimal extraction condition for balancing metabolome coverage and reproducibility in the untargeted metabolomics study. Mixture design method is an effective tool to optimize metabolite extraction strategy for tissue samples. Graphical abstract ᅟ.