Understanding the spatial distribution of bioactive small molecules is indispensable for elucidating their biological or pharmaceutical roles. Mass spectrometry imaging (MSI) enables determination of the distribution of ionizable molecules present in tissue sections of whole-body or single heterogeneous organ samples by direct ionization and detection. This emerging technique is now widely used for in situ label-free molecular imaging of endogenous or exogenous small molecules. MSI allows the simultaneous visualization of many types of molecules including a parent molecule and its metabolites. Thus, MSI has received much attention as a potential tool for pathological analysis, understanding pharmaceutical mechanisms, and biomarker discovery. On the other hand, several issues regarding the technical limitations of MSI are as of yet still unresolved. In this review, we describe the capabilities of the latest matrix-assisted laser desorption/ionization (MALDI)-MSI technology for visualizing in situ metabolism of endogenous metabolites or dietary phytochemicals (food factors), and also discuss the technical problems and new challenges, including MALDI matrix selection and metabolite identification, that need to be addressed for effective and widespread application of MSI in the diverse fields of biological, biomedical, and nutraceutical (food functionality) research.

Spatiotemporal information about biomolecules is indispensable for precise pathological analysis, but it remains largely unclear. Here we show a novel analytical platform combing mass spectrometry imaging (MSI) with its complementary technique, liquid chromatography–mass spectrometry (LC–MS), to elucidate more comprehensive metabolic behaviors, with spatiotemporal information, in tissues. Analysis of a rat transient middle cerebral artery occlusion (MCAO) brain tissue after ischemia–reperfusion was performed to characterize the detailed metabolomic response to pathological alterations. To compare the spatially resolved metabolic state between ischemic and contralateral hemispheres of the MCAO brain, coronally sliced tissues were subjected to MSI. We also measured the metabolites extracted from three different cerebral regions, including whole cortex (CTX), hippocampus (HI) and corpus striatum (CPu), by LC–MS. In the ischemic hemisphere, significant metabolic changes at the CTX and CPu were observed after reperfusion, while not at the HI. A region-specific metabolic behavior was observed in amino acid and nucleotide metabolism, as well as in the TCA cycle. Correlation between MSI and LC–MS data was relatively high in the CTX and CPu. Combination of both MS platforms visualized the diverse spatiotemporal metabolic dynamics during pathological progress. Thus, our proposed strategy will contribute to the understanding of the complex pathogenesis of ischemia–reperfusion.

The body is equipped with a “food factor-sensing system” that senses food factors, such as polyphenols, sulfur-containing compounds, and vitamins, taken into the body, and plays an essential role in manifesting their physiological effects. For example, (−)-epigallocatechin-3-O-gallate (EGCG), the representative catechin in green tea (Camellia sinensi L.), exerts various effects, including anti-cancer, anti-inflammatory, and anti-allergic effects, when sensed by the cell surficial protein 67-kDa laminin receptor (67LR). Here, we focus on three representative effects of EGCG and provide their specific signaling mechanisms, the 67LR-mediated EGCG-sensing systems. Various components present in foods, such as eriodictyol, hesperetin, sulfide, vitamin A, and fatty acids, have been found to act on the food factor-sensing system and affect the functionality of other foods/food factors, such as green tea extract, EGCG, or its O-methylated derivative at different experimental levels, i.e., in vitro, animal models, and/or clinical trials. These phenomena are observed by increasing or decreasing the activity or expression of EGCG-sensing-related molecules. Such functional interaction between food factors is called “functional food pairing”. In this review, we introduce examples of functional food pairings using EGCG.

Aspirin-induced enteropathy is now increasingly being recognized although the pathogenesis of small intestinal damage induced by aspirin is not well understood and related risk factors have not been established. To investigate pharmacogenomic profile of low dose aspirin (LDA)-induced small bowel bleeding. Genome-wide analysis of single nucleotide polymorphisms (SNPs) was performed using the Affymetrix DMET™ Plus Premier Pack. Genotypes of candidate genes associated with small bowel bleeding were determined using TaqMan SNP Genotyping Assay kits and direct sequencing. In the validation study in overall 37 patients with small bowel bleeding and 400 controls, 4 of 27 identified SNPs: CYP4F11 (rs1060463) GG (p=0.003), CYP2D6 (rs28360521) GG (p=0.02), CYP24A1 (rs4809957) T allele (p=0.04), and GSTP1 (rs1695) G allele (p=0.04) were significantly more frequent in the small bowel bleeding group compared to the controls. After adjustment for significant factors, CYP2D6 (rs28360521) GG (OR 4.11, 95% CI. 1.62 -10.4) was associated with small bowel bleeding. CYP4F11 and CYP2D6 SNPs may identify patients at increased risk for aspirin-induced small bowel bleeding.

Although understanding their chemical composition is vital for accurately predicting the bioactivity of multicomponent drugs, nutraceuticals, and foods, no analytical approach exists to easily predict the bioactivity of multicomponent systems from complex behaviors of multiple coexisting factors. We herein represent a metabolic profiling (MP) strategy for evaluating bioactivity in systems containing various small molecules. Composition profiles of diverse bioactive herbal samples from 21 green tea extract (GTE) panels were obtained by a high-throughput, non-targeted analytical procedure. This employed the matrix-assisted laser desorption ionization–mass spectrometry (MALDI–MS) technique, using 1,5-diaminonaphthalene (1,5-DAN) as the optical matrix for detecting GTE-derived components. Multivariate statistical analyses revealed differences among the GTEs in their antioxidant activity, oxygen radical absorbance capacity (ORAC). A reliable bioactivity-prediction model was constructed to predict the ORAC of diverse GTEs from their compositional balance. This chemometric procedure allowed the evaluation of GTE bioactivity by multicomponent rather than single-component information. The bioactivity could be easily evaluated by calculating the summed abundance of a few selected components that contributed most to constructing the prediction model. 1,5-DAN-MALDI–MS-MP, using diverse bioactive sample panels, represents a promising strategy for screening bioactivity-predictive multicomponent factors and selecting effective bioactivity-predictive chemical combinations for crude multicomponent systems.