Typical LC-MS-based metabolomics workflow. Targeted metabolomics (bottom panel) aims to deliver quantitative concentrations of specific compounds using metabolite standards to optimize the extraction and LC-MS acquisition. Then, univariate statistical methods allow investigating the targeted **data** for statistical significance using adjustment of false discovery rate and descriptive analysis (e.g., adjusted p-value, single-and multiple-factor ANOVA), and comparative analysis between the control and infected samples at different time points using single or multiple groups (e.g., t-test, Kruskal-Wallis, ANOVA with post hoc tests). When using isotopically labelled standards, targeted metabolomics provide absolute quantification of the metabolite of interest. Alternatively, untargeted metabolomics (top panel) generates a comprehensive metabolic overview of several thousands of metabolic features from a global extraction, which are analyzed by untargeted LC-MS profiling, for instance. The processing of untargeted **data** relies on sophisticated bioinformatics workflow (including extraction, alignment, grouping and retention time correction) for all the detected LC-MS features. Multivariate statistical methods are subsequently applied to the **dataset** to visualize how metabolic profiles respond to the conditions (i.e., biotic stress) and unravel stress-responsive discriminant metabolic markers. These relevant markers are putatively annotated using metabolite databases, then confirmed by comparing MS/MS and retention time **data** with metabolite standards, thereby returning to a targeted approach for verification. Targeted and untargeted metabolomics are complementary in addressing how relevant metabolites and metabolism respond or comprehensively react to stress. Adapted from Williams et al. (2020).