Paeoniflorin, the main component of Xiaoyao Wan, presents low oral bioavailability and unclear antidepressant mechanism. To elucidate the potential reasons for the low bioavailability of paeoniflorin and explore its antidepressant mechanism from the perspective of the gut microbiota, here, a chronic unpredictable depression model and forced swimming test were firstly performed to examine the antidepressant effects of paeoniflorin. Then the pharmacokinetic study of paeoniflorin in rats was performed based on the gut microbiota; meanwhile, the gut microbiota incubated with paeoniflorin in vitro was used to identify the possible metabolites of paeoniflorin. Molecular virtual docking experiments together with the specific inhibitor tests were applied to investigate the mechanism of paeoniflorin metabolism by the gut microbiota. Finally, the intestinal microbiota composition was analyzed by 16S rRNA gene sequencing technology. The pharmacodynamics tests showed that paeoniflorin had significant antidepressant activity, but its oral bioavailability was 2.32%. Interestingly, we found paeoniflorin was converted into benzoic acid by the gut microbiota, and was mainly excreted through the urine with the gut metabolite benzoic acid as the prominent excreted form. Moreover, paeoniflorin could also regulate the composition of the gut microbiota by increasing the abundance of probiotics. Therefore, the metabolism effect of gut microbiota may be one of the main reasons for the low oral bioavailability of paeoniflorin. Additionally, paeoniflorin can be metabolized into benzoic acid via gut microbiota enzymes, which might exert antidepressant effects through the blood–brain barrier into the brain.

Mass spectrometry imaging (MSI)‐based spatially resolved metabolomics addresses the limitations inherent in traditional liquid chromatography‐tandem mass spectrometry (LC–MS)‐based metabolomics, particularly the loss of spatial context within heterogeneous tissues. MSI not only enhances our understanding of disease aetiology but also aids in the identification of biomarkers and the assessment of drug toxicity and therapeutic efficacy by converting invisible metabolites and biological networks into visually rendered image data. In this comprehensive review, we illuminate the key advancements in MSI‐driven spatially resolved metabolomics over the past few years. We first outline recent innovations in preprocessing methodologies and MSI instrumentation that improve the sensitivity and comprehensiveness of metabolite detection. We then delve into the progress made in functional visualization techniques, which enhance the precision of metabolite identification and annotation. Ultimately, we discuss the significant potential applications of spatially resolved metabolomics technology in translational medicine and drug development, offering new perspectives for future research and clinical translation.Highlights: MSI‐driven spatial metabolomics preserves metabolite spatial information, enhancing disease analysis and biomarker discovery. Advances in MSI technology improve detection sensitivity and accuracy, expanding bioanalytical applications. Enhanced visualization techniques refine metabolite identification and spatial distribution analysis. Integration of MSI with AI promises to advance precision medicine and accelerate drug development.