A Taguchi Design of Experiments Approach for Untargeted Metabolomics Sample Preparation Optimization

The field of metabolomics leverages advanced analytical techniques, such as nuclear magnetic resonance (NMR) and liquid chromatography-mass spectrometry (LC-MS), to identify and quantify metabolites that are integral to biology. The scope of untargeted metabolomics methods is highly dependent on the protocols employed prior to analysis. These include homogenization and extraction processes, which directly influence the metabolites detected and, consequently, the biological interpretations drawn. Given the substantial variability introduced by different homogenization and extraction parameters, the optimization of these protocols for non-routine or novel sample matrices is essential, particularly in core facilities where a diverse range of matrices are expected to be analyzed. In response to this need, we demonstrate the utility of a Taguchi design of experiments (DOE) method for the systematic optimization of matrix-specific sample preparation parameters using the model organism Caenorhabditis elegans. This methodology was applied to optimize four critical factors: (1) extraction solvent, (2) solvent volume, (3) extraction duration, and (4) LC reconstitution solvent, during a sequential non-polar and polar metabolite extraction for LC-MS and NMR spectroscopy. Despite its infrequent use in metabolomics, the Taguchi DOE method offers a structured and efficient pathway for optimizing multiple sample preparation variables, enhancing throughput, reproducibility, and cost-effectiveness. This approach is particularly valuable for the metabolomics community, as it provides a scalable, adaptable framework applicable across various sample types and research objectives. This work serves as a demonstration of the methodology, underscoring its potential to enhance method development and optimization across diverse metabolomics applications.