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.

Elucidation of the mechanism of action for drug candidates is fundamental to drug development, and it is strongly facilitated by metabolomics. Herein, we developed an imaging metabolomics method based on air-flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) under ambient conditions. This method was subsequently applied to simultaneously profile a novel anti-insomnia drug candidate, N(6)-(4-hydroxybenzyl)-adenosine (NHBA), and various endogenous metabolites in rat whole-body tissue sections after the administration of NHBA. The principal component analysis (PCA) represented by an intuitive color-coding scheme based on hyperspectral imaging revealed in situ molecular profiling alterations in response to stimulation of NHBA, which are in a very low intensity and hidden in massive interferential peaks. We found that the abundance of six endogenous metabolites changed after drug administration. The spatiotemporal distribution indicated that five altered molecules—including neurotransmitter γ-aminobutyric acid, neurotransmitter precursors choline and glycerophosphocholine, energy metabolism-related molecules adenosine (an endogenous sleep factor), and creatine—are closely associated with insomnia or other neurological disorders. These findings not only provide insights into a deep understanding on the mechanism of action of NHBA, but also demonstrate that the AFADESI-MSI-based imaging metabolomics is a powerful technique to investigate the molecular mechanism of drug action, especially for drug candidates with multitarget or undefined target in the preclinical study stage.

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.

It is highly challenging to quantitatively map multiple analytes in biotissues without specific chemical labeling. Quantitative mass spectrometry imaging (QMSI) has this potential but still poses technical issues for its variant ionization efficiency across a complicated, heterogeneous biomatrices. Herein, a self-developed air-flow-assisted desorption electrospray ionization (AFADESI) is introduced to present a proof of concept method, virtual calibration (VC) QMSI. This method screens and utilizes analyte response-related endogenous metabolite ions from each mass spectrum as native internal standards (IS). Through machine-learning-based regression and clustering, tissue-specific ionization variation can be automatically recognized, predicted, and normalized region by region or pixel by pixel. Therefore, the quantity of analytes can be accurately mapped across highly structural biosamples including whole body, kidney, brain, tumor, etc. VC-QMSI has the advantages of simple sample preparation without laborious isotopic IS synthesis, extrapolation for those unknown tissues or regions without previous investigation, and automatic spatial recognition without histological guidance. This strategy is suitable for mass spectrometry imaging using a variety of in situ ionization techniques. It is believed that VC-QMSI has wide applicability for drug candidate's discovery, molecular mechanism elucidation, biomarker validation, and clinical diagnosis.

Ion suppression from the tissue matrix has a severe effect on the mass spectrometry imaging (MSI) of drugs. This problem hinders further applications of MSI in preclinical drug research and development. In this study, an in situ hydrogel conditioning method was developed to enhance the sensitivity of air-flow-assisted desorption electrospray ionization (AFADESI)-MSI. Instead of the traditional washing or digestion treatment in solvent, this method used a solid phase hydrogel to "wash" tissue sections. It was demonstrated that this in situ hydrogel conditioning method improved the drug signal by as much as 2- to 25-fold in MSI, especially for hydrophobic compounds. Furthermore, the obvious dislocation of analytes was not observed. The evaluation of spatial resolution indicated that the amount of dislocation in tissue sections with the hydrogel process was less than the resolution of AFADESI-MSI. The underlying reasons for the MSI signal enhancement were initially investigated. The decreased signal intensities of choline, betaine, and carnitine and the increased intensities of the [M + H]/[M + Na] and [M + H]/[M + K] ratios for drugs in the mass spectra of pretreated tissues provided evidence that this method can reduce the levels of highly competitive quaternary ammonium and inorganic salts in the tissues. The preformation of a thin liquid film for droplet pickup would also raise the ionization efficiency of drugs. These results demonstrated that this in situ hydrogel conditioning method provides a rapid and feasible approach to improving the sensitivity of ambient MSI for drug mapping in tissues.

Figure 2. A) MS images of representative metabolites in tissue homogenate model under different spray solvent systems. B) Relative ion intensity of endogenous ions (m/z 70-1000) with no extracting air flow (NER) or with high-rate extracting air flow (HER).

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