Promise of spatially resolved omics for tumor research.

Tumors are spatially heterogeneous tissues that comprise numerous cell types with intricate structures. By interacting with the microenvironment, tumor cells undergo dynamic changes in gene expression and metabolism, resulting in spatiotemporal variations in their capacity for proliferation and metastasis. In recent years, the rapid development of histological techniques has enabled efficient and high-throughput biomolecule analysis. By preserving location information while obtaining a large number of gene and molecular data, spatially resolved metabolomics (SRM) and spatially resolved transcriptomics (SRT) approaches can offer new ideas and reliable tools for the in-depth study of tumors. This review provides a comprehensive introduction and summary of the fundamental principles and research methods used for SRM and SRT techniques, as well as a review of their applications in cancer-related fields.

Identification of Diagnostic Metabolic Signatures in Thyroid Tumors Using Mass Spectrometry Imaging.

"Gray zone" thyroid follicular tumors are difficult to diagnose, especially when distinguishing between benign follicular thyroid adenoma (FTA) and malignant carcinoma (FTC). Thus, proper classification of thyroid follicular diseases may improve clinical prognosis. In this study, the diagnostic performance of metabolite enzymes was evaluated using imaging mass spectrometry to distinguish FTA from FTC and determine the association between metabolite enzyme expression with thyroid follicular borderline tumor diagnosis. Air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFAIDESI-MSI) was used to build a classification model for thyroid follicular tumor characteristics among 24 samples. We analyzed metabolic enzyme marker expression in an independent validation set of 133 cases and further evaluated the potential biological behavior of 19 thyroid borderline lesions. Phospholipids and fatty acids (FAs) were more abundant in FTA than FTC (p < 0.001). The metabolic enzyme panel, which included FA synthase and Ca2+-independent PLA2, was further validated in follicular thyroid tumors. The marker combination showed optimal performance in the validation group (area under the ROC, sensitivity, and specificity: 73.6%, 82.1%, and 60.6%, respectively). The findings indicate that AFAIDESI-MSI, in combination with low metabolic enzyme expression, could play a role in the diagnosis of thyroid follicular borderline tumors for strict follow-up.

Discovering metabolic vulnerability using spatially resolved metabolomics for antitumor small molecule-drug conjugates development as a precise cancer therapy strategy.

Against tumor-dependent metabolic vulnerability is an attractive strategy for tumor-targeted therapy. However, metabolic inhibitors are limited by the drug resistance of cancerous cells due to their metabolic plasticity and heterogeneity. Herein, choline metabolism was discovered by spatially resolved metabolomics analysis as metabolic vulnerability which is highly active in different cancer types, and a choline-modified strategy for small molecule-drug conjugates (SMDCs) design was developed to fool tumor cells into indiscriminately taking in choline-modified chemotherapy drugs for targeted cancer therapy, instead of directly inhibiting choline metabolism. As a proof-of-concept, choline-modified SMDCs were designed, screened, and investigated for their druggability in vitro and in vivo. This strategy improved tumor targeting, preserved tumor inhibition and reduced toxicity of paclitaxel, through targeted drug delivery to tumor by highly expressed choline transporters, and site-specific release by carboxylesterase. This study expands the strategy of targeting metabolic vulnerability and provides new ideas of developing SMDCs for precise cancer therapy.

Spatially resolved multi-omics highlights cell-specific metabolic remodeling and interactions in gastric cancer.

Mapping tumor metabolic remodeling and their spatial crosstalk with surrounding non-tumor cells can fundamentally improve our understanding of tumor biology, facilitates the designing of advanced therapeutic strategies. Here, we present an integration of mass spectrometry imaging-based spatial metabolomics and lipidomics with microarray-based spatial transcriptomics to hierarchically visualize the intratumor metabolic heterogeneity and cell metabolic interactions in same gastric cancer sample. Tumor-associated metabolic reprogramming is imaged at metabolic-transcriptional levels, and maker metabolites, lipids, genes are connected in metabolic pathways and colocalized in the heterogeneous cancer tissues. Integrated data from spatial multi-omics approaches coherently identify cell types and distributions within the complex tumor microenvironment, and an immune cell-dominated "tumor-normal interface" region where tumor cells contact adjacent tissues are characterized with distinct transcriptional signatures and significant immunometabolic alterations. Our approach for mapping tissue molecular architecture provides highly integrated picture of intratumor heterogeneity, and transform the understanding of cancer metabolism at systemic level.

Ginsenoside 3ß-O-Glc-DM (C3DM) suppressed glioma tumor growth by downregulating the EGFR/PI3K/AKT/mTOR signaling pathway and modulating the tumor microenvironment.

Ginsenosides are the main bioactive constituents of Panax ginseng, which have been broadly studied in cancer treatment. Our previous studies have demonstrated that 3ß-O-Glc-DM (C3DM), a biosynthetic ginsenoside, exhibited antitumor effects in several cancer cell lines with anti-colon cancer activity superior to ginsenoside 20(R)-Rg3 in vivo. However, the efficacy of C3DM on glioma has not been proved yet. In this study, the antitumor activities and underlying mechanisms of C3DM on glioma were investigated in vitro and in vivo. Cell viability, apoptosis, migration, FCM, IHC, RT-qPCR, quantitative proteomics, and western blotting were conducted to evaluate the effect of C3DM on glioma cells. ADP-Glo™ kinase assay was used to validate the interaction between C3DM and EGFR. Co-cultured assays, lactic acid kit, and spatially resolved metabolomics were performed to study the function of C3DM in regulating glioma microenvironment. Both subcutaneously transplanted syngeneic models and orthotopic models of glioma were used to determine the effect of C3DM on tumor growth in vivo. We found that C3DM dose-dependently induced apoptosis, and inhibited the proliferation, migration and angiogenesis of glioma cells. C3DM significantly inhibited tumor growth in both subcutaneous and orthotopic mouse glioma models. Moreover, C3DM attenuated the acidified glioma microenvironment and enhanced T-cell function. Additionally, C3DM inhibited the kinase activity of EGFR and influenced the EGFR/PI3K/AKT/mTOR signaling pathway in glioma. Overall, C3DM might be a promising candidate for glioma prevention and treatment.

Integrated spatially resolved metabolomics and network toxicology to investigate the hepatotoxicity mechanisms of component D of Polygonum multiflorum Thunb.

ETHNOPHARMACOLOGICAL RELEVANCE: The liver toxicity of Reynoutria multiflora (Thunb.) Moldenke. (Polygonaceae) (Polygonum multiflorum Thunb, PM) has always attracted much attention, but the related toxicity materials and mechanisms have not been elucidated due to multi-component and multi-target characteristics. In previous hepatotoxicity screening, different components of PM were first evaluated and the hepatotoxicity of component D [95% ethanol (EtOH) elution] in a 70% EtOH extract of PM (PM-D) showed the highest hepatotoxicity. Furthermore, the main components of PM-D were identified and their hepatotoxicity was evaluated based on a zebrafish embryo model. However, the hepatotoxicity mechanism of PM-D is unknown. AIM OF THE STUDY: This work is to explore the hepatotoxicity mechanisms of PM-D by integrating network toxicology and spatially resolved metabolomics strategy. MATERIALS AND METHODS: A hepatotoxicity interaction network of PM-D was constructed based on toxicity target prediction for eight key toxic ingredients and a hepatotoxicity target collection. Then the key signaling pathways were enriched, and molecular docking verification was implemented to evaluate the ability of toxic ingredients to bind to the core targets. The pathological changes of liver tissues and serum biochemical assays of mice were used to evaluate the liver injury effect of mice with oral administration of PM-D. Furthermore, spatially resolved metabolomics was used to visualize significant differences in metabolic profiles in mice after drug administration, to screen hepatotoxicity-related biomarkers and analyze metabolic pathways. RESULTS: The contents of four key toxic compounds in PM-D were detected. Network toxicology identified 30 potential targets of liver toxicity of PM-D. GO and KEGG enrichment analyses indicated that the hepatotoxicity of PM-D involved multiple biological activities, including cellular response to endogenous stimulus, organonitrogen compound metabolic process, regulation of the apoptotic process, regulation of kinase, regulation of reactive oxygen species metabolic process and signaling pathways including PI3K-Akt, AMPK, MAPK, mTOR, Ras and HIF-1. The molecular docking confirmed the high binding activity of 8 key toxic ingredients with 10 core targets, including mTOR, PIK3CA, AKT1, and EGFR. The high distribution of metabolites of PM-D in the liver of administrated mice was recognized by mass spectrometry imaging. Spatially resolved metabolomics results revealed significant changes in metabolic profiles after PM-D administration, and metabolites such as taurine, taurocholic acid, adenosine, and acyl-carnitines were associated with PM-D-induced liver injury. Enrichment analyses of metabolic pathways revealed tht linolenic acid and linoleic acid metabolism, carnitine synthesis, oxidation of branched-chain fatty acids, and six other metabolic pathways were significantly changed. Comprehensive analysis revealed that the hepatotoxicity caused by PM-D was closely related to cholestasis, mitochondrial damage, oxidative stress and energy metabolism, and lipid metabolism disorders. CONCLUSIONS: In this study, the hepatotoxicity mechanisms of PM-D were comprehensively identified through an integrated spatially resolved metabolomics and network toxicology strategy, providing a theoretical foundation for the toxicity mechanisms of PM and its safe clinical application.

Molecular Pathological Diagnosis of Thyroid Tumors Using Spatially Resolved Metabolomics.

The pathological diagnosis of benign and malignant follicular thyroid tumors remains a major challenge using the current histopathological technique. To improve diagnosis accuracy, spatially resolved metabolomics analysis based on air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) technique was used to establish a molecular diagnostic strategy for discriminating four pathological types of thyroid tumor. Without any specific labels, numerous metabolite features with their spatial distribution information can be acquired by AFADESI-MSI. The underlying metabolic heterogeneity can be visualized in line with the cellular heterogeneity in native tumor tissue. Through micro-regional feature extraction and in situ metabolomics analysis, three sets of metabolic biomarkers for the visual discrimination of benign follicular adenoma and differentiated thyroid carcinomas were discovered. Additionally, the automated prediction of tumor foci was supported by a diagnostic model based on the metabolic profile of 65 thyroid nodules. The model prediction accuracy was 83.3% when a test set of 12 independent samples was used. This diagnostic strategy presents a new way of performing in situ pathological examinations using small molecular biomarkers and provides a model diagnosis for clinically indeterminate thyroid tumor cases.

Spatially resolved metabolomics combined with multicellular tumor spheroids to discover cancer tissue relevant metabolic signatures.

Spatially resolved metabolomics offers unprecedented opportunities for elucidating metabolic mechanisms during cancer progression. It facilitated the discovery of aberrant cellular metabolism with clinical application potential. Here, we developed a novel strategy to discover cancer tissue relevant metabolic signatures by high spatially resolved metabolomics combined with a multicellular tumor spheroid (MCTS) in vitro model. Esophageal cancer MCTS were generated using KYSE-30 human esophageal cancer cells to fully mimic the 3D microenvironment under physiological conditions. Then, the spatial features and temporal variation of metabolites and metabolic pathways in MCTS were accurately mapped by using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) with a spatial resolution at ∼12 µm. Metabolites, such as glutamate, tyrosine, inosine and various types of lipids displayed heterogeneous distributions in different microregions inside the MCTS, revealing the metabolic heterogenicity of cancer cells under different proliferative states. Subsequently, through joint analysis of metabolomic data of clinical cancer tissue samples, cancer tissue relevant metabolic signatures in esophageal cancer MCTS were identified, including glutamine metabolism, fatty acid metabolism, de novo synthesis phosphatidylcholine (PC) and phosphatidylethanolamine (PE), etc. In addition, the abnormal expression of the involved metabolic enzymes, i.e., GLS, FASN, CHKA and cPLA2, was further confirmed and showed similar tendencies in esophageal cancer MCTS and cancer tissues. The MALDI-MSI combined with MCTS approach offers molecular insights into cancer metabolism with real-word relevance, which would potentially benefit the biomarker discovery and metabolic mechanism studies.

Rewiring of purine metabolism in response to acidosis stress in glioma stem cells.

Glioma stem cells (GSCs) contribute to therapy resistance and poor outcomes for glioma patients. A significant feature of GSCs is their ability to grow in an acidic microenvironment. However, the mechanism underlying the rewiring of their metabolism in low pH remains elusive. Here, using metabolomics and metabolic flux approaches, we cultured GSCs at pH 6.8 and pH 7.4 and found that cells cultured in low pH exhibited increased de novo purine nucleotide biosynthesis activity. The overexpression of glucose-6-phosphate dehydrogenase, encoded by G6PD or H6PD, supports the metabolic dependency of GSCs on nucleotides when cultured under acidic conditions, by enhancing the pentose phosphate pathway (PPP). The high level of reduced glutathione (GSH) under acidic conditions also causes demand for the PPP to provide NADPH. Taken together, upregulation of G6PD/H6PD in the PPP plays an important role in acidic-driven purine metabolic reprogramming and confers a predilection toward glioma progression. Our findings indicate that targeting G6PD/H6PD, which are closely related to glioma patient survival, may serve as a promising therapeutic target for improved glioblastoma therapeutics. An integrated metabolomics and metabolic flux analysis, as well as considering microenvironment and cancer stem cells, provide a precise insight into understanding cancer metabolic reprogramming.

Rapid Imaging of Unsaturated Lipids at an Isomeric Level Achieved by Controllable Oxidation.

Lipid imaging plays an important role in the research of some diseases, such as cancers. Unsaturated lipids are often present as isomers that can have different functions; however, traditional tandem mass spectrometry imaging (MSI) cannot differentiate between different isomers, which presents difficulties for the pathological study of lipids. Herein, we propose a method for the MSI of the C═C double-bond isomers of unsaturated lipids based on oxidative reactions coupled with air flow-assisted desorption electrospray ionization, which can conveniently achieve rapid MSI of unsaturated lipids at an isomeric level. Using this method, tissue sections can be scanned directly with MSI after only 10 min of accelerated oxidation. This method was used for the imaging of mouse lung cancer tissues, revealing a distributional difference in the unsaturated lipid isomers of normal and pathological regions. Through the MSI of unsaturated lipids at an isomeric level in tissues infected with cancer cells, the regions where the isomers were enriched were exhibited, indicating that these regions were the most concentrated regions of cancer cells. This method provides a convenient platform for studying the functional effects of the isomers of unsaturated lipids in pathological tissues.

5-HMF induces anaphylactoid reactions in vivo and in vitro.

AIM: Excessive exposure to 5-hydroxymethylfurfural (5-HMF), which is a common impurity in various sugar-containing products, induces serious side effects. Our previous study revealed that 5-HMF exerted immune sensitizing potential when injected into rodents. In this study, we explored 5-HMF mediated anaphylactoid reactions and its underlying molecular mechanisms. METHODS: We investigated anaphylactoid reactions in Brown Norway (BN) rats and Institute of Cancer Research (ICR) mice to identify 5-HMF mediated in vivo anaphylactoid reactions. RBL-2H3 and P815 cell degranulation models were also established, and degranulation, enzyme-linked immunosorbent, filamentous actin (F-actin) microfilament staining, and western blot assays were performed in these cells. RESULTS: We showed that 5-HMF induced anaphylactoid reactions by increasing blood vessel permeability in mice, and significantly elevating histamine (His) and glutathione peroxidase-1 (Gpx-1) levels in rat serum. Moreover, after incubation with 5-HMF, ß-hexosaminidase (ß-Hex), His, IL-4 and IL-6 levels were all significantly increased, thereby inducing cellular degranulation in RBL-2H3 and P815 cells. Finally, 5-HMF also upregulated Lyn, Syk, p38 and JNK protein phosphorylation levels. CONCLUSIONS: Our findings suggest that 5-HMF induces anaphylactoid reactions both in vivo and in vitro, therefore 5-HMF limits in sugar-containing products should receive more regulatory attention.

Development of a high-coverage metabolome relative quantitative method for large-scale sample analysis.

The development of quantitative metabolomics approaches for future standardized and translational applications has become increasingly important. Data-independent targeted quantitative metabolomics (DITQM) is a newly proposed method providing ion pair information on 1324 metabolites. However, the quantification of more than 1000 metabolites in large sample sizes has still not been implemented. In this study, on the basis of the DITQM concept, scheduled multiple reaction monitoring (MRM) methods for both high-abundant and low-abundant metabolites were established to broaden the quantification coverage, and an open-source program "Quanter_1.0" was coded to facilitate efficient data handling. Our results demonstrated that 1015 metabolites in human plasma met the quantitative requirements and could be relatively determined in an effective manner. The method was then applied to a large-scale sample study of lung cancer consisting of three distinct analytical batches. It was obvious that data quality that originated from quantitative metabolomics was improved, with substantially lower intra- and inter-batch data variation, resulting in a more effective multivariate statistical model. Finally, 26 potential biomarkers of lung cancer were discovered. Collectively, our approach provides a promising tool for quantitative metabolomics research involving large-scale sample sizes and clinical application.

Strategy for Global Profiling and Identification of 2- and 3-Hydroxy Fatty Acids in Plasma by UPLC-MS/MS.

2-Hydroxy fatty acids (2-OHFAs) and 3-hydroxy fatty acids (3-OHFAs) with the same carbon backbone are isomers, both of which are closely related to diseases involving fatty acid oxidation disorder. However, the comprehensive profiling of 2- and 3-OHFAs remains an ongoing challenge due to their high structure similarity, few structure-informative product ions, and limited availability of standards. Here, we developed a new strategy to profile and identify 2- and 3-OHFAs according to structure-dependent retention time prediction models using ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Both accurate MS and MS/MS spectra were collected for peak annotation by comparison with an in-house database of theoretically possible 2- and 3-OHFAs. The structures were further confirmed by the validated structure-dependent retention time prediction models, taking advantage of the correlation between the retention time, carbon chain length and number of double bonds, as well as the hydroxyl position-induced isomeric retention time shift rule. With the use of this strategy, 18 2-OHFAs and 32 3-OHFAs were identified in the pooled plasma, of which 7 2-OHFAs and 20 3-OHFAs were identified for the first time in this work, furthering our understanding of OHFA metabolism. Subsequent quantitation method was developed by scheduled multiple reaction monitoring (MRM) and then applied to investigate the alteration of 2- and 3-OHFAs in esophageal squamous cell carcinoma (ESCC) patients. Finally, a potential biomarker panel consisting of six OHFAs with good diagnostic performance was achieved. Our study provides a new strategy for isomer identification and analysis, showing great potential for targeted metabolomics in clinical biomarker discovery.

Evaluation of the tumor-targeting efficiency and intratumor heterogeneity of anticancer drugs using quantitative mass spectrometry imaging.

The development of improved or targeted drugs that discriminate between normal and tumor tissues is the key therapeutic issue in cancer research. However, the development of an analytical method with a high accuracy and sensitivity to achieve quantitative assessment of the tumor targeting of anticancer drugs and even intratumor heterogeneous distribution of these drugs at the early stages of drug research and development is a major challenge. Mass spectrometry imaging is a label-free molecular imaging technique that provides spatial-temporal information on the distribution of drugs and metabolites in organisms, and its application in the field of pharmaceutical development is rapidly increasing. Methods: The study presented here accurately quantified the distribution of paclitaxel (PTX) and its prodrug (PTX-R) in whole-body animal sections based on the virtual calibration quantitative mass spectrometry imaging (VC-QMSI) method, which is label-free and does not require internal standards, and then applied this technique to evaluate the tumor targeting efficiency in three treatment groups-the PTX-injection treatment group, PTX-liposome treatment group and PTX-R treatment group-in nude mice bearing subcutaneous A549 xenograft tumors. Results: These results indicated that PTX was widely distributed in multiple organs throughout the dosed body in the PTX-injection group and the PTX-liposome group. Notably, in the PTX-R group, both the prodrug and metabolized PTX were mainly distributed in the tumor tissue, and this group showed a significant difference compared with the PTX-liposome group, the relative targeting efficiency of PTX-R group was increased approximately 50-fold, leading to substantially decreased systemic toxicities. In addition, PTX-R showed a significant and specific accumulation in the poorly differentiated intratumor area and necrotic area. Conclusion: This method was demonstrated to be a reliable, feasible and easy-to-implement strategy to quantitatively map the absorption, distribution, metabolism and excretion (ADME) of a drug in the whole-body and tissue microregions and could therefore evaluate the tumor-targeting efficiency of anticancer drugs to predict drug efficacy and safety and provide key insights into drug disposition and mechanisms of action and resistance. Thus, this strategy could significantly facilitate the design and optimization of drugs at the early stage of drug research and development.

Butyrate Improves the Metabolic Disorder and Gut Microbiome Dysbiosis in Mice Induced by a High-Fat Diet.

Background: Metabolic syndrome (MS) is one of the major causes of coronary artery diseases (CAD). Gut microbiome diversity and its natural fermentation products are not only correlated with MS and CAD, but their correlations also appear to be stronger than the associations with traditional risk factors. Therefore, the aim of this study was to provide a new potential pathway for the natural fermentation product butyrate to improve MS and to examine whether it is associated with serum metabolic profiles and gut flora composition. Methods: C57BL/6J mice fed a high-fat diet (HFD) were treated with 400 mg/kg of sodium butyrate for 16 weeks. Blood and fecal samples were collected, and the metabolite concentrations and 16s rRNA were measured with liquid chromatography-MS and Illumina platform, respectively. The plasma differential metabolites and gut microbiome composition were analyzed with XCMS online and QIIME 2, respectively. Results: Gut microbiome-derived butyrate reduced glucose intolerance and insulin resistance, resisting HFD-induced increase in the relative abundance of f_Lachnospiraceae, f_Rikenellaceae, and f_Paraprevotellaceae. Meanwhile, sodium butyrate increased the levels of α-linolenate, all-trans-retinal, resolvin E1, and leukotriene in the plasma, and the differential pathways showed enrichment in mainly resolvin E biosynthesis, histidine degradation, lipoxin biosynthesis, and leukotriene biosynthesis. Moreover, sodium butyrate increased the levels of phosphorylated-adenosine 5'-monophosphate-activated protein kinase (p-AMPK) and facilitated glucose transporter member 4 (GLUT4) in the adipose tissue. Conclusion: Butyrate can induce AMPK activation and GLUT4 expression in the adipose tissue, improving cardiovascular disease (CVD)-related metabolic disorder, resisting HFD-induced gut microbiome dysbiosis, and promoting resolvin E1 and lipoxin biosynthesis. Oral supplement of the natural fermentation product butyrate can be a potential strategy for preventing CVD.

Accurate Classification of Non-small Cell Lung Cancer (NSCLC) Pathology and Mapping of EGFR Mutation Spatial Distribution by Ambient Mass Spectrometry Imaging.

Objectives: Tumor pathology examination especially epidermal growth factor receptor (EGFR) mutations molecular testing has been integral part of lung cancer clinical practices. However, the EGFR mutations spatial distribution characteristics remains poorly investigated, which is critical to tumor heterogeneity analysis and precision diagnosis. Here, we conducted an exploratory study for label-free lung cancer pathology diagnosis and mapping of EGFR mutation spatial distribution using ambient mass spectrometry imaging (MSI). Materials and Methods: MSI analysis were performed in 55 post-operative non-small cell lung cancer (NSCLC) tumor and paired normal tissues to distinguish tumor from normal and classify pathology. We then compared diagnostic sensitivity of MSI and ADx-amplification refractory mutation system (ARMS) for the detection of EGFR mutation in pathological confirmed lung adenocarcinoma (AC) and explored EGFR mutations associated biomarkers to depict EGFR spatial distribution base on ambient MSI. Results: Of 55 pathological confirmed NSCLC, MSI achieved a diagnostic sensitivity of 85.2% (23/27) and 82.1% (23/28) for AC and squamous cell carcinoma (SCC), respectively. Among 27 AC, there were 17 EGFR-wild-type and 10 EGFR-mutated-positive samples detected by ARMS, and MSI achieved a diagnostic sensitivity of 82.3% (14/17) and 80% (8/10) for these two groups. Several phospholipids were specially enriched in AC compared with SCC tissues, with the higher ions intensity of phospholipids in EGFR-mutated-positive compared with EGFR-wild-type AC tissues. We also found EGFR mutations distribution was heterogeneous in different regions of same tumor by multi-regions ARMS detection, and only the regions with higher ions intensity of phospholipids were EGFR-mutated-positive. Conclusion: MSI method could accurately distinguish tumor pathology and subtypes, and phospholipids were reliable EGFR mutations associated biomarkers, phospholipids imaging could intuitively visualize EGFR mutations spatial distribution, may facilitate our understanding of tumor heterogeneity.

A graphical data processing pipeline for mass spectrometry imaging-based spatially resolved metabolomics on tumor heterogeneity.

Spatially resolved metabolomics is an excellent tool for elucidating in situ molecular events, but its use remains challenging due to the complexity of the endogenous metabolites in bio-tissue and tissue heterogeneity. In this study, a data processing pipeline for spatially resolved metabolomics analysis of tumor microregion heterogeneity was developed and built into a graphical interface with MSI software. Biological tissue sections were analysed by ambient air-flow assisted desorption electrospray ionization mass spectrometry imaging. Histology-driven and characterized ion images overlay combined with metabolic feature-based spatial segmentation were developed to accurately extract the metabolic profile from the tissue microregion of interest. In addition, appropriate data pretreatment methods were investigated to evaluate their ability to identify biological variations from the complicated spatially resolved metabolomics data. Diverse graphical metabolic feature extraction and various data pretreatment methods enable not only the achievement of the best multivariate statistical results in an intuitive and simple way but also the discovery of low-abundance but reliable biomarkers. The results from a papillary thyroid cancer tissue study demonstrated that this data processing pipeline is a powerful and easy-to-use tool for investigating the spatial molecular events in tumor microenvironments and to therefore thoroughly understand their metabolic heterogeneity.

Development of methionine methylation profiling and relative quantification in human breast cancer cells based on metabolic stable isotope labeling.

Methylation of components involved in one-carbon metabolism is extremely important in cancer; comprehensive studies on methylation are essential and may provide us with a better understanding of tumorigenesis, and lead to the discovery of potential biomarkers. Here, we present an improved methodology for methylated metabolite profiling and its relative quantification in breast cancer cell lines by isotope dilution mass spectrometry based on 13CD3-methionine metabolic labeling using ultra-high-performance liquid chromatography coupled with high-resolution tandem mass spectrometry (UPLC-HRMS/MS). First, all the methylated metabolites related to methionine were first screened and profiled by introducing 13CD3-methionine as the only medium into breast cancer cell growth cultures for both cellular polar metabolites and lipids. In total, we successfully found 20 labeled methylated metabolites and most of them were identified, some of which have not been reported before. We also developed a relative quantification method for all identified methylated metabolites based on isotope dilution mass spectrometry assays. Finally, the developed method was used for different breast cancer cells and mammary epithelial cells. Most methylated metabolites were disrupted in cancer cells. 1-Methyl-nicotinamide was decreased significantly, while trimethylglycine-glutamic acid-lysine and trimethyl-lysine were increased more than five times. This method offers a new insight into the methylation process, with several key pathways and important new metabolites being identified. Further investigation with biological assays should help to reveal the overall methylation metabolic network.

Whole-body spatially-resolved metabolomics method for profiling the metabolic differences of epimer drug candidates using ambient mass spectrometry imaging.

Investigation of the in vivo drug action and metabolic differences of epimer drugs is challenging. Whole-body MSI analysis can visually present the stereoscopic distribution of molecules related to the interaction of drugs and organisms, and can provide more comprehensive organ-specific profiling information. Herein, we developed a whole-body spatially-resolved imaging metabolomics method based on an air flow-assisted ionisation desorption electrospray ionisation (AFADESI)-MSI system coupled with a high-resolution mass spectrometer and highly discriminating imaging software. The epimeric sedative-hypnotic drug candidates YZG-331 and YZG-330 were selected as examples, and rats administered normal or high oral doses were used. By performing multivariate statistical data-mining on the combined MSI data, organ-specific differential ions were screened. By comparing the variations in the relative contents of the drugs, their metabolites, and endogenous neurotransmitters throughout whole-body tissue sections of the rats, rich information that could potentially explain the more significant sedative-hypnotic effects of YZG-330 compared to YZG-331 was obtained. Such as the increased ratio of gamma-aminobutyric acid in the brain and stomach of the rats (0.25, 0.47, 0.68, 0.30, and 0.89 for the control and YZG-331-H, YZG-330-H, YZG-331-L, and YZG-330-L, respectively) were interesting. This study provided a convenient and visual method to investigate in vivo molecular metabolic differences and provide insight towards a better understanding of the pharmacodynamic mechanisms of these sedative-hypnotic drug-candidates.