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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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

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 13 CD 3 -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 13 CD 3 -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.

The pseudo-targeted metabolomics approach was developed recently which combined the advantages of untargeted and targeted analysis. However, the current pseudo-targeted analysis method has limitations due to the technical characteristics. In this study, a novel metabolic pathway-based pseudo-targeted approach was proposed for urine metabolomics analysis using an ultra-high-performance liquid chromatography (UPLC)-MS/MS system operated in the multiple reaction monitoring (MRM) mode. MRM ion pairs were acquired from urine samples through untargeted analysis using UPLC-HRMS, as well as by searching for metabolites in related pathways in relevant databases and from previous relevant research, including amino acids, fatty acids, nucleosides, carnitines, glycolysis metabolites, and steroids. This improved pseudo-targeted method exhibited good repeatability and precision, and no complicated peak alignment was required. As a proof of concept, the developed novel method was applied to the discovery of urine biomarkers for patients with esophageal squamous cell carcinoma (ESCC). The results showed that ESCC patients had altered acylcarnitines, amino acids, nucleosides, and steroid derivative levels et al. compared to those of healthy controls. The novelty of this study lies in the fact that it provides an approach for acquiring MRM ion pairs not only from untargeted MS analysis but also from targeted searching for metabolites in related metabolic pathways. By improving the detection limit of low-abundance metabolites, it enlarges the range for the discovery of potential biomarkers. Our work provides a foundation for achieving pseudo-targeted metabolomics analysis on the widely used LC-MS/MS MRM platform., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Characterization of tumor metabolism with spatial information contributes to our understanding of complex cancer metabolic reprogramming, facilitating the discovery of potential metabolic vulnerabilities that might be targeted for tumor therapy. However, given the metabolic variability and flexibility of tumors, it is still challenging to characterize global metabolic alterations in heterogeneous cancer. Here, we propose a spatially resolved metabolomics approach to discover tumor-associated metabolites and metabolic enzymes directly in their native state. A variety of metabolites localized in different metabolic pathways were mapped by airflow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) in tissues from 256 esophageal cancer patients. In combination with in situ metabolomics analysis, this method provided clues into tumor-associated metabolic pathways, including proline biosynthesis, glutamine metabolism, uridine metabolism, histidine metabolism, fatty acid biosynthesis, and polyamine biosynthesis. Six abnormally expressed metabolic enzymes that are closely associated with the altered metabolic pathways were further discovered in esophageal squamous cell carcinoma (ESCC). Notably, pyrroline-5-carboxylate reductase 2 (PYCR2) and uridine phosphorylase 1 (UPase1) were found to be altered in ESCC. The spatially resolved metabolomics reveal what occurs in cancer at the molecular level, from metabolites to enzymes, and thus provide insights into the understanding of cancer metabolic reprogramming., Competing Interests: The authors declare no conflict of interest., (Copyright © 2019 the Author(s). Published by PNAS.)

We performed a metabolomics study using liquid chromatography-mass spectrometry (LC-MS) combined with multivariate data analysis (MVDA) to discriminate global urine profiles in urine samples from esophageal squamous cell carcinoma (ESCC) patients and healthy controls (NC). Our work evaluated the feasibility of employing urine metabolomics for the diagnosis and staging of ESCC. The satisfactory classification between the healthy controls and ESCC patients was obtained using the MVDA model, and obvious classification of early-stage and advanced-stage patients was also observed. The results suggest that the combination of LC-MS analysis and MVDA may have potential applications for ESCC diagnosis and staging. We then conducted LC-MS/MS experiments to identify the potential biomarkers with large contributions to the discrimination. A total of 83 potential diagnostic biomarkers for ESCC were screened out, and 19 potential biomarkers were identified; the variations between the differences in staging using these potential biomarkers were further analyzed. These biomarkers may not be unique to ESCCs, but instead result from any malignant disease. To further elucidate the pathophysiology of ESCC, we studied related metabolic pathways and found that ESCC is associated with perturbations of fatty acid β-oxidation and the metabolism of amino acids, purines, and pyrimidines.

Rationale: Acquiring global information on plasma-endogenous metabolites challenges metabolomics. This study has been designed to investigate the suitability of integrated ionization rapid-resolution liquid chromatography/tandem mass spectrometry (RRLC/MS/MS) for different kinds of metabolites in complex plasma, and provides an approach for plasma metabolomics in acquiring more comprehensive data of metabolites., Methods: Integrated ionization of electrospray ionization (ESI), atmospheric pressure chemical ionization (APCI), and atmospheric pressure photoionization (APPI) combined with RRLC/MS/MS has been carried out to perform analysis on the global plasma metabolome of healthy volunteers. The contributions to the total numbers of ion features by RRLC/MS with ESI, APCI, and APPI in positive and negative ion modes were calculated. Representative unique and identical ions were identified. The intensities of identical ions were compared., Results: Each of ESI, APCI, and APPI coupled with RRLC/MS has its own advantage over the other two techniques for certain types of metabolites in plasma. LC/ESI-MS is very sensitive for detecting glycerophosphocholines, glycerophosphoethanolamines, acyl carnitines, bile acids, sulfate, etc. LC/APCI-MS is suitable for analyzing cyclic alcohols, fatty acids, and linoleic acids. LC/APPI-MS proves to be appropriate in detecting steroids, sphingolipids, some amino acids, nucleosides, and purines in plasma., Conclusions: It is suggested that the integrated ionization LC/MS approach should be applied for global plasma metabolomics. Moreover, the results obtained demonstrate that it is preferable to choose certain techniques from LC/ESI-MS, LC/APCI-MS, and LC/APPI-MS for metabolite target analysis., (Copyright © 2013 John Wiley & Sons, Ltd.)

It is essential to choose one preprocessing method for liquid chromatography-mass spectrometry (LC-MS)-based metabolomics studies of urine samples in order to overcome their variability. However, the commonly used normalization methods do not substantially reduce the high variabilities arising from differences in urine concentration, especially for signal saturation (abundant metabolites exceed the dynamic range of the instrumentation) or missing values. Herein, a simple preacquisition strategy based on differential injection volumes calibrated by creatinine (to reduce the concentration differences between the samples), combined with normalization to "total useful MS signals" or "all MS signals", is proposed to overcome urine variabilities. This strategy was first systematically compared with other popular normalization methods by application to serially diluted urine samples. Then, the method has been verified using rat urine samples of pre- and postinoculation of Walker 256 carcinoma cells. The results showed that the calibration of injection volumes based on creatinine values could effectively eliminate intragroup differences caused by variations in the concentrations of urinary metabolites, thus giving better parallelism and clustering effects. In addition, peak area normalization could further eliminate intraclass differences. Therefore, the strategy of combining peak area normalization with calibration of injection volumes of urine samples based on their creatinine values is effective for solving problems associated with urinary metabolomics.

A metabolomics strategy based on rapid resolution liquid chromatography/tandem mass spectrometry (RRLC-MS/MS) and multivariate statistics has been implemented to identify potential biomarkers in uterine cervix cancer. Due to the importance of the data pre-processing method, three popular software packages have been compared. Then they have been used to acquire respective data matrices from the same LC-MS/MS data. Multivariate statistics was subsequently used to identify significantly changed biomarkers for uterine cervix cancer from the resulting data matrices. The reliabilities of the identified discriminated metabolites have been further validated on the basis of manually extracted data and ROC curves. Nine potential biomarkers have been identified as having a close relationship with uterine cervix cancer. Considering these in combination as a biomarker group, the AUC amounted to 0.997, with a sensitivity of 92.9% and a specificity of 95.6%. The prediction accuracy was 96.6%. Among these potential biomarkers, the amounts of four purine derivatives were greatly decreased, which might be related to a P2 receptor that might lead to a decrease in cell number through apoptosis. Moreover, only two of them were identified simultaneously by all of the pre-processing tools. The results have demonstrated that the data pre-processing method could seriously bias the metabolomics results. Therefore, application of two or more data pre-processing methods would reveal a more comprehensive set of potential biomarkers in non-targeted metabolomics, before a further validation with LC-MS/MS based targeted metabolomics in MRM mode could be conducted.

Metabonomics is an important platform for investigating the metabolites of integrated living systems and their dynamic responses to changes caused by both endogenous and exogenous factors. A metabonomics strategy based on liquid chromatography-mass spectrometry/mass spectrometry in both positive and negative ion modes was applied to investigate the short-term and long-term stability of metabolites in plasma. Principal components analysis and ten types of identified metabolites were used to summarize the time-dependent change rules in metabolites systematically at different temperatures. The long-term stability of metabolites in plasma specimens stored at -80 °C for five years was also studied. Analysis of these subjects identified 36 metabolites with statistically significant changes in expression (p < 0.05) and found a kind of metabolite with a hundred-fold change. The stability of metabolites in blood at 4 °C for 24 h was also investigated. These studies show that a thorough understanding of the effects of metabolite stability are necessary for improving the reliability of potential biomarkers.

A metabonomic approach based on complementary hydrophilic interaction chromatography and reversed-phase liquid chromatography combined with tandem mass spectrometry and time-course analysis of metabolites was implemented to find more reliable potential biomarkers in urine of Walker 256 tumor-bearing rats. A major challenge in metabonomics is distinguishing reliable biomarkers that are closely associated with the genesis and progression of diseases from those that are unrelated but altered significantly. In this study, these biomarkers were selected according to the change trends of discriminating metabolites during the genesis and progression of cancer. Seven consecutive batches of urine samples from preinoculation to 16 days after were collected and analyzed. Multivariate analysis revealed 87 discriminating metabolites. Time-course analysis of discriminating metabolites was used to select more reliable biomarkers with regular and reasonable change trends. Finally, 47 were found and 15 were identified including 12 carnitine derivatives, 2 amino acid derivatives, 1 nucleoside. On the basis of time-course behaviors of these potential biomarkers, we hypothesize such disruption might result from elevated cell proliferation, reduced β-oxidation of fatty acids, and poor renal tubular reabsorption. These studies demonstrate that this method can help to find more reliable potential biomarkers and provide valuable biochemical insights into metabolic alterations in tumor-bearing biosystems.

An integrated ionization approach of electrospray ionization (ESI), atmospheric pressure chemical ionization (APCI), and atmospheric pressure photoionization (APPI) combining with rapid resolution liquid chromatography mass spectrometry (RRLC-MS) has been developed for performing global metabonomic analysis on complex biological samples. This approach was designed to overcome the low ionization efficiencies of endogenous metabolites due to diverse physicochemical properties as well as ion suppression, and obtain comprehensive metabolite profiles in LC-MS analysis. Ionization capability and applicability were manifested by improved ionization efficiency and enlarged metabolite coverage in analysis on typical urinary metabolite standards and urine samples from healthy volunteers. The method was validated by the limit of detection and precision. When applied to the global metabonomic studies of lung cancer, more comprehensive biomarker candidates were obtained to reflect metabolic traits between healthy volunteers and lung cancer patients, including 74 potential biomarkers in positive ion mode and 59 in negative ion mode. Taking identical potential biomarkers of any two or three ionization methods into account, analysis using ESI-MS in positive (+) and negative (-) ion mode contributed to 70 and 64% of the total potential biomarkers, respectively. The biomarker discovery capability of (+/-) APCI-MS accounted for 45 and 42% of the overall; meanwhile (+/-) APPI-MS amounted for 39 and 54%. These results indicated that potential biomarkers with vital biological information could be missed if only a single ionization method was used. Furthermore, 11 potential biomarkers were identified including amino acids, nucleosides, and a metabolite of indole. They revealed elevated amino acid and nucleoside metabolism as well as protein degradation in lung cancer patients. This proposed approach provided a more comprehensive picture of the metabolic changes and further verified identical biomarkers that were obtained simultaneously using different ionization methods.

A metabonomics strategy based on rapid resolution liquid chromatography/tandem mass spectrometry (RRLC-MS/MS), multivariate statistics and metabolic correlation networks has been implemented to find biologically significant metabolite biomarkers in breast cancer. RRLC-MS/MS analysis by electrospray ionization (ESI) in both positive and negative ion modes was employed to investigate human urine samples. The resulting data matrices were analyzed using multivariate analysis. Application of orthogonal projections to latent structures discriminate analysis (OPLS-DA) allowed us to extract several discriminated metabolites reflecting metabolic characteristics between healthy volunteers and breast cancer patients. Correlation network analysis between these metabolites has been further applied to select more reliable biomarkers. Finally, high resolution MS and MS/MS analyses were performed for the identification of the metabolites of interest. We identified 12 metabolites as potential biomarkers including amino acids, organic acids, and nucleosides. They revealed elevated tryptophan and nucleoside metabolism as well as protein degradation in breast cancer patients. These studies demonstrate the advantages of integrating metabolic correlation networks with metabonomics for finding significant potential biomarkers: this strategy not only helps identify potential biomarkers, it also further confirms these biomarkers and can even provide biochemical insights into changes in breast cancer.

Hua Zhuo Ning Fu Decoction (HZD) is an empirical prescription from traditional Chinese medicine that shows excellent clinical results for psoriasis patients. Uncertainty lingered over HZD's potential anti-psoriasis mechanisms. The study's objective is to investigate the pharmacological processes and therapeutic effects of HZD on psoriasis. In the initial phase of the study, an investigation was conducted to assess the effects of HZD on psoriasis-afflicted mice using an imiquimod (IMQ)-induced murine model. The experimental mice were randomly allocated to different groups, including the IMQ-induced model group, the control group, the HZD therapy groups with varying dosage levels (low, medium, and high), and Dexamethasone (DEX, the positive control medicine) group. Bioinformatics analysis and molecular docking were subsequently employed to identify the primary components and molecular targets associated with the therapeutic action of HZD in the context of psoriasis. Additionally, to find the impacts on metabolite regulation, plasma metabolomics based on ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) was used. It's interesting to note that the combined mechanisms from metabolomics were examined in tandem with the targets. In vivo tests were the last step in validating the potential mechanism. Throughout the trial, the following data were recorded: body weight, psoriasis area and severity index (PASI). The molecular targets connected to HZD's anti-psoriasis activities were revealed using histological examination, western blot (WB), and ELISA investigation. In mice induced with IMQ, HZD shown good anti-psoriasis effects in terms of PASI score and epidermal acanthosis. 95 HZD targets and 77 bioactive chemicals connected to psoriasis were found by bioinformatics research; of these, 7 key targets (EPHX2, PLA2G2A, TBXAS1, MAOA, ALDH1A3, ADH1A, and ADH1B) were linked to the mechanisms of HZD, the combination degree of which was finally expressed by the score of docking. In addition, HZD regulated nine metabolites. In line with this, HZD modified three metabolic pathways. Additionally, a combined examination of 7 key targets and 9 metabolites suggested that the metabolism of arachidonic acid might be the key metabolic route, which was identified by ELISA analysis. The in vivo investigation shown that HZD could control cytokines associated to inflammation (IL-10, TGF-β, IL-17A, and IL-23), as well as important antioxidant system markers (ROS, GSH, and MDA). Moreover, HZD controlled iron levels and the expression of ferroptosis-related proteins (ACSL4 and GPX4), suggesting that ferroptosis played a crucial role in this process. Our findings demonstrated the whole mechanism and anti-psoriasis effectiveness of HZD, which will promote its clinical application and aid in the investigation of new bioactive components of HZD against psoriasis. [ABSTRACT FROM AUTHOR]