SignificanceTumor cells reprogram their metabolism to support cell growth, proliferation, and differentiation, thus driving cancer progression. Profiling of the metabolic signatures in heterogeneous tumors facilitates the understanding of tumor metabolism and introduces potential metabolic vulnerabilities that might be targeted therapeutically. We proposed a spatially resolved metabolomics method for high-throughput discovery of tumor-associated metabolite and enzyme alterations using ambient mass spectrometry imaging. Metabolic pathway-related metabolites and metabolic enzymes that are associated with tumor metabolism were efficiently discovered and visualized in heterogeneous esophageal cancer tissues. Spatially resolved metabolic alterations hold the key to defining the dependencies of metabolism that are most limiting for cancer growth and exploring metabolic targeted strategies for better cancer treatment.

Histological examination with a deep link between functional metabolites and tissue structure and biofunctions will provide important in situ biochemical information, and then essentially reveal what has happened in tissue at the molecular level. However, due to the complexity and heterogeneity of tissue samples and the large number of metabolites, it is still a challenge to globally map the diverse metabolites, especially for those low‐abundance functional ones. Here, a sensitive air flow‐assisted desorption electrospray ionization mass spectrometry imaging method for the mapping of a broad range of metabolites is presented. It exhibits properties characteristic of wide coverage, high sensitivity, wide dynamic range, rapid analysis procedure, and high specificity for tissue metabolites imaging. More than 1500 metabolites, including cholines, polyamines, amino acids, carnitines, nucleosides, nucleotides, nitrogen bases, organic acids, carbohydrates, cholesterol sulfate, cholic acid, lipids, etc., can be visualized in an untargeted analysis. The distribution of metabolites shows good spatial match with tissue histological structure and biofunctions in heterogeneous rat kidney, rat brain, and human esophageal cancer tissue. This method possesses the ability to globally showcase the molecular processes in tissue, and provide an insightful way for structural and functional molecular recognition in histological examination, even for intraoperative decision‐making.

Carboxyl-containing metabolites (CCMs) play indispensable roles in cell energy metabolism and cell–cell signaling. Profiling tissue CCMs with spatial signatures is significant for the understanding of molecular histology and may provide new clues to uncover the complex metabolic reprogramming of organisms in response to external or internal stimuli. Here, we develop a sensitive on-tissue CCMs derivatization method, coupled with matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), to visualize the spatial distributions of CCMs in biological tissues. A novel reagent, N,N,N-trimethyl-2-(piperazin-1-yl)­ethan-1-aminium iodide (TMPA), was synthesized and used for the on-tissue derivatization of CCMs. Meanwhile, the on-tissue derivatization efficiency was significantly improved by introducing acetonitrile gas in the incubation system. With this methodology, a total of 28 CCMs, including 5 tricarboxylic acid cycle intermediates, 20 fatty acids, and 3 bile acids, were successfully detected and imaged in rat kidney tissues. More importantly, the introduction of a quaternary ammonium group into the chemical structure of CCMs enables simultaneous MALDI-MS imaging of tricarboxylic acid cycle intermediates, fatty acids, bile acids, and their metabolic pathway-related metabolites such as carnitines, cholines, glycerophosphocholine, phospholipids, and so on in the positive ion mode. This on-tissue derivatization MALDI-MSI approach was proven to be a powerful tool for probing the distributions and spatial metabolic networks of CMMs in biological tissues.

Mass spectrometry imaging (MSI) has become a powerful tool to probe molecule events in biological tissue. However, it is a widely held viewpoint that one of the biggest challenges is an easy-to-use data processing software for discovering the underlying biological information from complicated and huge MSI dataset. Here, a user-friendly and full-featured MSI software including three subsystems, Solution, Visualization and Intelligence, named MassImager, is developed focusing on interactive visualization, in-situ biomarker discovery and artificial intelligent pathological diagnosis. Simplified data preprocessing and high-throughput MSI data exchange, serialization jointly guarantee the quick reconstruction of ion image and rapid analysis of dozens of gigabytes datasets. It also offers diverse self-defined operations for visual processing, including multiple ion visualization, multiple channel superposition, image normalization, visual resolution enhancement and image filter. Regions-of-interest analysis can be performed precisely through the interactive visualization between the ion images and mass spectra, also the overlaid optical image guide, to directly find out the region-specific biomarkers. Moreover, automatic pattern recognition can be achieved immediately upon the supervised or unsupervised multivariate statistical modeling. Clear discrimination between cancer tissue and adjacent tissue within a MSI dataset can be seen in the generated pattern image, which shows great potential in visually in-situ biomarker discovery and artificial intelligent pathological diagnosis of cancer. All the features are integrated together in MassImager to provide a deep MSI processing solution at the in-situ metabolomics level for biomarker discovery and future clinical pathological diagnosis.