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Rapid and accurate metabolite annotation in mass spectrometry imaging (MSI) can improve the efficiency of spatially resolved metabolomics studies and accelerate the discovery of reliable

Cotton fibers develop from epidermal cells in the outer integument of ovules. The regulatory process underlying fiber cell development has been extensively studied, little is known about the spatio-temporal profiles of transcriptomes and metabolomes during its development. Here we characterized the dynamics of transcriptome and metabolome during the early developmental stages of cotton fiber cells using a combination of spatial transcriptomic, single-cell transcriptomic, and spatial metabolomic analyses. We identified the key genetic regulators and metabolites that initiate and determine the fate of fiber cells. Both knockdown and gain-of-function analyses of one new identified marker gene BEE3/Gh_A09G062900 revealed its important role in the initiation of cotton fiber. We also designed a website for the public visualization of spatial gene expression in cotton that provides important reference datasets of spatio-temporal gene expression and can be used to further decipher the genetic and metabolic regulation of cotton fiber development (http://CottonOvule.cricaas.com.cn/).

The improvement of on-tissue chemical derivatization for mass spectrometry imaging (MSI) of low-abundance and/or poorly ionizable functional molecules in biological tissue without delocalization is challenging. Here, we developed a novel hydrogel-assisted chemical derivatization (HCD) approach coupled with airflow-assisted desorption electrospray ionization (AFADESI)-MSI, allowing for enhanced visualization of inaccessible molecules in biological tissues. The derivatization reagent Girard's P (GP) reagent was creatively packaged into a hydrogel to form HCD blocks that have reactivity to carbonyl compounds as well as the feasibility of "cover/uncover" contact mode with tissue sections. The HCD blocks provided a favorable liquid microenvironment for the derivatization reaction and reduced matrix effects from derivatization reagents and tissue without obvious molecular migration, thus improving the derivatization efficiency. With this methodology, unusual carbonyl metabolites, including 166 fatty aldehydes (FALs) and 100 oxo fatty acids (FAs), were detected and visualized in rat brain, kidney, and liver tissue. This study provides a new approach to enhance chemical labeling for in situ tissue submetabolome profiling and improves our knowledge of the molecular histology and complex metabolism of biological tissues.

Large-scale and long-period metabolomics study is more susceptible to various sources of systematic errors, resulting in nonreproducibility and poor data quality. A reliable and robust batch correction method removes unwanted systematic variations and improves the statistical power of metabolomics data, which undeniably becomes an important issue for the quality control of metabolomics. This study proposed a novel data normalization and integration method, Norm ISWSVR. It is a two-step approach via combining the best-performance internal standard correction with support vector regression normalization, comprehensively removing the systematic and random errors and matrix effects. This method was investigated in three untargeted lipidomics or metabolomics datasets, and the performance was further evaluated systematically in comparison with that of 11 other normalization methods. As a result, Norm ISWSVR decreased the data's median cross-validated relative standard deviation (cvRSD), increased the correlation between QCs, improved the classification accuracy of biomarkers, and was well-compatible with quantitative data. More importantly, Norm ISWSVR also allows a low frequency of QCs, which could significantly decrease the burden of a large-scale experiment. Correspondingly, Norm ISWSVR favorably improves the data quality of large-scale metabolomics data.

Cardiovascular disease, as a multifactorial disease caused by genetics and environment, has emerged as a leading cause of mortality. The discovery of metabolic biomarkers for the clinical diagnosis, early warning and elucidation of the molecular pathogenesis of cardiovascular disease, using metabolomics, has attracted broad interest. Therefore, this work aimed to develop a sensitive and reliable targeted metabolomics method for the quantification of cardiovascular disease-related biomarkers in plasma.The method was developed and validated using ultrahigh-performance liquid chromatography augmented with tandem mass spectrometry (UHPLC/MS/MS). The LC conditions and MS parameters were optimized using selected reaction monitoring scanning mode to high-throughput and sensitive separation, and could detect 20 metabolic biomarkers in a single experiment. And the linearity, selectivity, accuracy, precision, stability and recovery of the developed method were assessed according to the Bioanalytical Method Validation guidelines of the United States Food and Drug Administration.These quantified metabolic biomarkers are involved in pathways such as aromatic amino acid catabolism (e.g. phenylalanine, tryptophan, tyrosine), trimethylamine N-oxide (TMAO) biosynthesis (e.g. TMAO, choline, carnitine, betaine) and histidine metabolism (e.g. histidine), among others. All analytes exhibited excellent linearities with coefficients of determination greater than 0.99. Accuracies deviated by less than 15% for medium- and high-concentration samples and less than 20% for low-concentration samples, with intra- and inter-day precisions of 1.12-14.12% and 0.30-13.74%, respectively. Recoveries and stabilities also met the analysis requirements of biological samples.The targeted metabolomics method was shown to have a powerful ability to accurately analyze metabolic biomarkers, thereby providing valuable information for large-scale biomarker validation and clarifying the potential material basis of cardiovascular disease for clinical diagnosis or early warning.

This study developed an open-source method, x-dimensional mass spectrometry imaging (MSI(x)), to reveal the spatially-resolved high-order metabolomics information associated with disease progression or drug action. This high order bioinformation includes metabolism pathway, species, biofunction, or biotransformation, which involves multiplex metabolites and cannot be presented by a single ion image. The MSI(x) enables the evaluation of the physiological status, the discovery of therapeutic or adverse effects, the investigation of regional heterogeneous response to drug treatment, possible molecular mechanism, and even drug potential target. MSI(x) was demonstrated to be a promising molecular imaging tool not only for efficacy and safety evaluation but also for the molecular mechanism investigation at the early stage of drug research and development.

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.

Neurotransmitters (NTs) are specific endogenous metabolites that act as “messengers” in synaptic transmission and are widely distributed in the central nervous system. Olanzapine (OLZ), a first-line antipsychotic drug, plays a key role in sedation and hypnosis, but, it presents clinical problems with a narrow therapeutic window, large individual differences and serious adverse effects, as well as an unclear mechanism in vivo. Herein, a simultaneous targeted NT quantification and nontargeted metabolomics method was developed and validated for pharmacometabolomics analysis of OLZ by using ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UPLC-HRMS). Considering the low physiological concentrations of NTs, a full MS scan and target selective ion monitoring (tSIM) scan were combined for nontargeted metabolomics and targeted NT quantification, respectively. By using this strategy, NTs at a very low physiological concentration can be accurately detected and quantified in biological samples by tSIM scans. Moreover, simultaneously nontargeted profiling was also achieved by the full MS scan. The newly established UPLC-HRMS method was further used for the pharmacometabolomics study of OLZ. Statistical analysis revealed that tryptophan, 5-hydroxytryptophan, 5-hydroxytryptamine, γ-aminobutyric acid etc. were significantly downregulated, while tyrosine was significantly upregulated, which suggested that OLZ could promote the downstream phase II reaction of 5-hydroxytryptamine, inhibit tyrosine hydroxylase activity, and increase the activity of γ-aminobutyric acid transaminase. In conclusion, this method could provide novel insights for revealing the pharmacodynamic effect and mechanism of antipsychotic drugs.

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, β-hexosaminidase (β-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.

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.

Ethnopharmacological relevance Traditional Chinese medicines (TCMs) have made great contributions to the prevention and treatment of human diseases in China, and especially in cases of COVID-19. However, due to quality problems, the lack of standards, and the diversity of dosage forms, adverse reactions to TCMs often occur. Moreover, the composition of TCMs makes them extremely challenging to extract and isolate, complicating studies of toxicity mechanisms. Aim of the review: The aim of this paper is therefore to summarize the advanced applications of mass spectrometry imaging (MSI) technology in the quality control, safety evaluations, and determination of toxicity mechanisms of TCMs. Materials and methods Relevant studies from the literature have been collected from scientific databases, such as “PubMed”, “Scifinder”, “Elsevier”, “Google Scholar” using the keywords “MSI”, “traditional Chinese medicines”, “quality control”, “metabolomics”, and “mechanism”. Results MSI is a new analytical imaging technology that can detect and image the metabolic changes of multiple components of TCMs in plants and animals in a high throughput manner. Compared to other chemical analysis methods, such as liquid chromatography-mass spectrometry (LC-MS), this method does not require the complex extraction and separation of TCMs, and is fast, has high sensitivity, is label-free, and can be performed in high-throughput. Combined with chemometrics methods, MSI can be quickly and easily used for quality screening of TCMs. In addition, this technology can be used to further focus on potential biomarkers and explore the therapeutic/toxic mechanisms of TCMs. Conclusions As a new type of analysis method, MSI has unique advantages to metabolic analysis, quality control, and mechanisms of action explorations of TCMs, and contributes to the establishment of quality standards to explore the safety and toxicology of TCMs.

Identifying the writing sequence of seals and signatures in documents is often performed and difficult to resolve in forensic determination. Morphological and physical-chemical analysis methods are often limited by the destructive nature of samples, a high signal response strength and specific materials. Mass spectrometry imaging (MSI) has been used as an alternative method because it can generate molecular images from many surfaces and produce rich chemical information. Herein, we developed a sequence identification method by coupling an air flow-assisted desorption electrospray ionization (AFADESI)-MSI system with a chemometric analysis, which can holistically and directly analyse document samples under ambient, moderate and selectable conditions and maintain the original appearance of the paper documents after sampling. By integrating principal component analysis (PCA) and the partial least squares discriminant analysis (PLS-DA), equivocal point analysis can be objectively performed, where knowing the components of the seal or signature is not necessary to identify the sequence. In total, 28 prepared samples with known sequences and two original blind test samples were analysed. One prepared sample was analysed in negative ionization mode, and other samples were inferred in positive ionization mode. All writing sequences were in accordance with the actual case. The writing sequence of the blind testing of the original samples was correctly identified. This study provided a convenient, objective and quasi-nondestructive method to investigate the sequence differences among equivocal document samples and is promising for providing an alternative method for the sequence identification of seals and signatures in questionable documents.

Mass spectrometry imaging (MSI) technology can simultaneously obtain the spatial distribution of thousands of chemical compounds and has unique advantages compared to other techniques that allow mapping the surface of bio-tissue. Here, we combined an air flow-assisted desorption electrospray ionization (AFADESI) MSI device with a high-resolution mass spectrometer to optimize the system parameters and achieve more accurate spatial distribution characteristics for compounds of interest while investigating bio-tissue sections. The platform set-up, required instrumentation, sample pretreatment, parameter optimization and bio-tissue characterization are described and discussed. Finally, the parameter conditions that can provide optimal ionic intensity and enhanced resolution were confirmed. The reasonable resolution and sensitivity improvements of AFADESI-MSI have been achieved through tandem a high-resolution mass spectrometer system, therefore, it would be a promising technique for the bio-tissue imaging analysis.

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.

Cell metabolomics is a valuable tool in oncology. To obtain a valid description of the intracellular metabolome, sample pretreatment is critical to the metabolomics workflow, as this step might influence the reliability of research outcomes. Here we report a systematic investigation to optimize each step of the sample preparation. We explored four quenching protocols, three disruption protocols and seven extraction protocols, and assessed them for effectiveness and robustness by profiling different kinds of representative metabolites by the LC-MS technique using the human glioma cell line U87MG as a model. The optimized pretreatment method for global metabolomics was as follows: after the cells were washed with PBS, cell metabolism quenching was completed within five minutes with 60% methanol (buffered with 0.85% (w/v) AMBIC) at −40 °C. Then, 80% methanol was added and three freeze–thaw cycles were completed to extract metabolites, which could eliminate environmental stress and allow broad coverage of the authentic intracellular metabolome. We also highlighted the advantages and disadvantages of some preanalytical processes for specific metabolites. In addition, the influences of different cell collection batches and liquid nitrogen storage durations were noted. The results suggested the risk of non-physiological or misleading results when using cell samples cultured in different batches or subjected to long-term storage.

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.

Significance Tumor 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., 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.

Targeted quantification and untargeted global profiling are the two mainstream approaches, a merging of which could provide enhanced analytical potential in metabolomics research. Here, a simultaneous targeted quantification and untargeted metabolomics (STQUM) strategy was developed for more efficient, accurate and comprehensive metabolomics research by using ultra-high-performance liquid chromatography coupled with high-resolution tandem mass spectrometry (UPLC-HRMS/MS). First, we selected 110 cancer-related metabolites as targets and established a dual LC sequential separation method for simultaneous analysis of strong and weak polar metabolites. In order to achieve efficient acquisition for synchronous qualitative and quantitative analysis, high-resolution, data-dependent parallel reaction monitoring (PRM) method and data-independent all ion fragmentation (AIF) method were established. Their performance in targeted confirmation and quantification, and untargeted analysis were systematically investigated and assessed. In total, 78 metabolites were confidently confirmed in positive ion mode in both PRM and AIF assays, in which 73 metabolites can be accurately quantified. In addition, simultaneously untargeted profiling of 4651 features of high reliability and validity were achieved. Both AIF and PRM methods revealed high confidence, sensitivity and accuracy. In the STQUM approach, another 15 metabolites could be accurately quantified in negative ion mode. The method offers a new perspective for merging the hypothesis-based targeted quantitative validation and untargeted biomarkers discovery in one run for improved analysis efficiency and integrity.

Metabolic networks and their dysfunction in the brain are closely associated with central nervous function and many psychogenic diseases. Thus, it is of utmost importance to develop a high-throughput imaging method for metabolic network mapping. Here, we developed a metabolic network mapping method to discover the metabolic contexts and alterations with spatially resolved information from the microregion of the brain by ambient-air flow-assisted desorption electrospray ionization mass spectrometry imaging and metabolomics analysis, which can be performed without any chemical derivatization, labels, or complex sample pretreatment. This method can map hundreds of different polar functional metabolites involved in multiple metabolic pathways, including not only neurotransmitters but also purines, organic acids, polyamines, cholines, and carbohydrates, in the rat brain. These high-coverage metabolite profile and microregional distribution information constitute complex networks that regulate advanced functions in the central nervous system. Moreover, this methodology was further used to discover not only the dysregulated metabolites but also the brain microregions involved in the pathology of a scopolamine-treated Alzheimer's model. Furthermore, this methodology was demonstrated to be a powerful visualizing tool that could offer novel insight into the metabolic events and provide spatial information about these events in central nervous system diseases.

The pharmacokinetic parameters of paroxetine vary between individuals, leading to differences in efficacy. The focus of our research was to predict responses to paroxetine using a pharmacometabonomic approach combining metabolic phenotypes and pharmacokinetic parameters. The pharmacokinetics of 12 healthy volunteers treated with paroxetine over two cycles was determined using high-performance liquid chromatography tandem mass spectrometry. Metabolic profiling and phenotyping were performed on the blood samples that remained after pharmacokinetic studies, using ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry. Thirty-nine metabolites (p < 0.05) increased or decreased after treatment with paroxetine. Vitamin B6 metabolism; valine, leucine, and isoleucine biosynthesis; phenylalanine metabolism; pantothenate and coenzyme A biosynthesis; tyrosine metabolism; and glyoxylate and dicarboxylate metabolism were likely to be relevant for the effects of paroxetine. The two-stage partial least squares (PLS) strategy was used to screen potential biomarkers and predict the pharmacokinetic parameters of paroxetine. An orthogonal PLS discriminant analysis strategy was then applied to separate the high- and low-value groups based on the screened biomarkers. Pearson correlation test and receiver operating characteristic curve analysis confirmed the key prediction biomarkers. Seven common biomarkers were able to predict both the area under the curve (AUC) and the maximum concentration (Cmax): cortisone, l-tyrosine, phenylpyruvate, l-valine, 2-oxoglutarate, l-lactate, and glycerate. Furthermore, homoprotocatechuate and l-glutamate were unique biomarkers for AUC, and citicoline and fumarate were unique biomarkers for Cmax. The selected biomarkers were able to predict the AUC and Cmax and discriminate good responders from poor responders to paroxetine treatment. This trial was registered with http://www.chinadrugtrials.org.cn/ (no. CTR20171590).

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.

Purpose/Objective(s) For stage I to II high-risk endometrioid adenocarcinoma patients, the optimal adjuvant radiotherapy modality remains controversial. The present study sought to optimize the treatment of pelvic external beam radiation (EBRT) with/or vaginal brachytherapy (VBT) for high-risk endometrioid adenocarcinoma patients in multiple radiation oncology centers across China. Materials/Methods This article retrospectively reviewed stage I to II resected endometrioid adenocarcinoma patients treated at 13 radiation centers from Jan. 1999 to Dec. 2015. Patients were eligible if they had high-risk features (stage IB Grade 3 disease or stage II Grade 1-3 disease) on the basis of ESMO-ESGO-ESTRO risk group consensus. Results A total of 218 patients were included in the analysis. Lymphadenectomy was achieved in 87.2% of all patients. Fifty-one patients received adjuvant EBRT (EBRT group), 25 patients received VBT (VBT group), and 142 patients were administered EBRT combined with VBT (EBRT + VBT group). The three groups were comparable in baseline characteristics, except that the proportion of stage IBand Grade 3 disease in the VBT group was significantly higher and their age was older (P Conclusion For patients with postoperative stage I to II high-risk endometrioid adenocarcinoma, compared with VBT alone, pelvic radiation significantly improved DFS, LRFS and DMFS with tolerable adverse effects. Survival outcomes were not significantly different between the EBRT and EBRT + VBT modalities. Author Disclosure W. Wang: None. L. Zou: None. T. Wang: None. Z. Liu: None. J. He: None. X. Sun: None. W. Zhong: None. F. Zhao: None. X. Li: None. S. Li: None. H. Zhu: None. Z. Ma: None. S. Sun: None. M. Jin: None. F. Zhang: None. X. Hou: None. L. Wei: None. K. Hu: None.

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.

Toll-like receptor 4 (TLR4) is an important cellular transmembrane receptor and pattern-recognition signaling molecule for pathogens in the immune system. High mobility group box 1 protein (HMGB1) plays an important role in myocardial ischemia (MI) and reperfusion through a TLR4-mediated inflammatory response. T lymphocytes are involved in MI injury; however, the specific mechanisms underlying this role remain unclear. In this study, C57BL/6 wild-type (WT) mice and TLR4 knockout mice were divided into three groups, including a normal control group, an MI group that was generated using high doses of isoproterenol (ISO), and an ISO+rHMGB1 group that was generated using a combination of ISO and recombinant HMGB1 (rHMGB1). Echocardiography, hematoxylin and eosin staining, terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL), and flow cytometry were used to examine each group. The results showed that rHMGB1 could further aggravate myocardial injury and increase the CD4

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.

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.

Quantitative mass spectrometry imaging (MSI) is a robust approach that provides both quantitative and spatial information for drug candidates' research. However, because of complicated signal suppression and interference, acquiring accurate quantitative information from MSI data remains a challenge, especially for whole-body tissue sample. Ambient MSI techniques using spray-based ionization appear to be ideal for pharmaceutical quantitative MSI analysis. However, it is more challenging, as it involves almost no sample preparation and is more susceptible to ion suppression/enhancement. Herein, based on our developed air flow-assisted desorption electrospray ionization (AFADESI)-MSI technology, an ambient quantitative MSI method was introduced by integrating inkjet-printing technology with normalization of the signal extinction coefficient (SEC) using the target compound itself. The method utilized a single calibration curve to quantify multiple tissue types. Basic blue 7 and an antitumor drug candidate (S-(+)-deoxytylophorinidine, CAT) were chosen to initially validate the feasibility and reliability of the quantitative MSI method. Rat tissue sections (heart, kidney, and brain) administered with CAT was then analyzed. The quantitative MSI analysis results were cross-validated by LC-MS/MS analysis data of the same tissues. The consistency suggests that the approach is able to fast obtain the quantitative MSI data without introducing interference into the in-situ environment of the tissue sample, and is potential to provide a high-throughput, economical and reliable approach for drug discovery and development.