Reconstruction of Glutathione Metabolism in the Neuronal Model of Rotenone-Induced Neurodegeneration Using Mass Isotopologue Analysis with Hydrophilic Interaction Liquid Chromatography-Zeno High-Resolution Multiple Reaction Monitoring.

Accurate reconstruction of metabolic pathways is an important prerequisite for interpreting metabolomics changes and understanding the diverse biological processes in disease models. A tracer-based metabolomics strategy utilizes stable isotope-labeled precursors to resolve complex pathways by tracing the labeled atom(s) to downstream metabolites through enzymatic reactions. Isotope enrichment analysis is informative and achieved by counting total labeled atoms and acquiring the mass isotopologue distribution (MID) of the intact metabolite. However, quantitative analysis of labeled metabolite substructures/moieties (MS2 fragments) can offer more valuable insights into the reaction connections through measuring metabolite transformation. In order to acquire the isotopic labeling information at the intact metabolite and moiety level simultaneously, we developed a method that couples hydrophilic interaction liquid chromatography (HILIC) with Zeno trap-enabled high-resolution multiple reaction monitoring (MRMHR). The method enabled accurate and reproducible MID quantification for intact metabolites as well as their fragmented moieties, with notably high sensitivity in the MS2 fragmentation mode based on the measurement of 13C- or 15N-labeled cellular samples. The method was applied to human-induced pluripotent stem cell-derived neurons to trace the fate of 13C/15N atoms from D-13C6-glucose/L-15N2-glutamine added to the media. With the MID analysis of both intact metabolites and fragmented moieties, we validated the pathway reconstruction of de novo glutathione synthesis in mid-brain neurons. We discovered increased glutathione oxidization from both basal and newly synthesized glutathione pools under neuronal oxidative stress. Furthermore, the significantly decreased de novo glutathione synthesis was investigated and associated with altered activities of several key enzymes, as evidenced by suppressed glutamate supply via glucose metabolism and a diminished flux of glutathione synthetic reaction in the neuronal model of rotenone-induced neurodegeneration.

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.

Spatially resolved metabolomics to discover tumor-associated metabolic alterations.

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.

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.

Virtual Calibration Quantitative Mass Spectrometry Imaging for Accurately Mapping Analytes across Heterogenous Biotissue.

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.

In Situ Hydrogel Conditioning of Tissue Samples To Enhance the Drug's Sensitivity in Ambient Mass Spectrometry Imaging.

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

Oxygen-Dependent Interactions between the Ruthenium Cage and the Photoreleased Inhibitor in NAMPT-Targeted Photoactivated Chemotherapy.

Photoactivated chemotherapy agents form a new branch of physically targeted anticancer agents with potentially lower systemic side effects for patients. On the other hand, limited information exists on the intracellular interactions between the photoreleased metal cage and the photoreleased anticancer inhibitor. In this work, we report a new biological study of the known photoactivated compound Ru-STF31 in the glioblastoma cancer cell line, U87MG. Ru-STF31 targets nicotinamide phosphoribosyltransferase (NAMPT), an enzyme overexpressed in U87MG. Ru-STF31 is activated by red light irradiation and releases two photoproducts: the ruthenium cage and the cytotoxic inhibitor STF31. This study shows that Ru-STF31 can significantly decrease intracellular NAD+ levels in both normoxic (21% O2) and hypoxic (1% O2) U87MG cells. Strikingly, NAD+ depletion by light activation of Ru-STF31 in hypoxic U87MG cells could not be rescued by the addition of extracellular NAD+. Our data suggest an oxygen-dependent active role of the ruthenium photocage released by light activation.

Metabolomic analysis of dietary-restriction-induced attenuation of sarcopenia in prematurely aging DNA repair-deficient mice.

BACKGROUND: Sarcopenia is characterized by loss of skeletal muscle mass and function, and is a major risk factor for disability and independence in the elderly. Effective medication is not available. Dietary restriction (DR) has been found to attenuate aging and aging-related diseases, including sarcopenia, but the mechanism of both DR and sarcopenia are incompletely understood. METHODS: In this study, mice body weight, fore and all limb grip strength, and motor learning and coordination performance were first analysed to evaluate the DR effects on muscle functioning. Liquid chromatography-mass spectrometry (LC-MS) was utilized for the metabolomics study of the DR effects on sarcopenia in progeroid DNA repair-deficient Ercc1∆/- and Xpg-/- mice, to identify potential biomarkers for attenuation of sarcopenia. RESULTS: Muscle mass was significantly (P < 0.05) decreased (13-20%) by DR; however, the muscle quality was improved with retained fore limbs and all limbs grip strength in Ercc1∆/- and Xpg-/- mice. The LC-MS results revealed that metabolites and pathways related to oxidative-stress, that is, GSSG/GSH (P < 0.01); inflammation, that is, 9-HODE, 11-HETE (P < 0.05), PGE2, PGD2, and TXB2 (P < 0.01); and muscle growth (PGF2α) (P < 0.01) and regeneration stimulation (PGE2) (P < 0.05) are significantly downregulated by DR. On the other hand, anti-inflammatory indicator and several related metabolites, that is, ß-hydroxybutyrate (P < 0.01), 14,15-DiHETE (P < 0.0001), 8,9-EET, 12,13-DiHODE, and PGF1 (P < 0.05); consumption of sources of energy (i.e., muscle and liver glycogen); and energy production pathways, that is, glycolysis (glucose, glucose-6-P, fructose-6-P) (P < 0.01), tricarboxylic acid cycle (succinyl-CoA, malate) (P < 0.001), and gluconeogenesis-related metabolite, alanine (P < 0.01), are significantly upregulated by DR. The notably (P < 0.01) down-modulated muscle growth (PGF2α) and regeneration (PGE2) stimulation metabolite and the increased consumption of glycogen in muscle and liver may be related to the significantly (P < 0.01) lower body weight and muscle mass by DR. The downregulated oxidative stress, pro-inflammatory mediators, and upregulated anti-inflammatory metabolites resulted in a lower energy expenditure, which contributed to enhanced muscle quality together with upregulated energy production pathways by DR. The improved muscle quality may explain why grip strength is maintained and motor coordination and learning performance are improved by DR in Ercc1∆/- and Xpg-/- mice. CONCLUSIONS: This study provides fundamental supporting information on biomarkers and pathways related to the attenuation of sarcopenia, which might facilitate its diagnosis, prevention, and clinical therapy.

Systematic Evaluation of HILIC Stationary Phases for Global Metabolomics of Human Plasma.

Polar hydrophilic metabolites have been identified as important actors in many biochemical pathways. Despite continuous improvement and refinement of hydrophilic interaction liquid chromatography (HILIC) platforms, its application in global polar metabolomics has been underutilized. In this study, we aimed to systematically evaluate polar stationary phases for untargeted metabolomics by using HILIC columns (neutral and zwitterionic) that have been exploited widely in targeted approaches. To do so, high-resolution mass spectrometry was applied to thoroughly investigate selectivity, repeatability and matrix effect at three pH conditions for 9 classes of polar compounds using 54 authentic standards and plasma matrix. The column performance for utilization in untargeted metabolomics was assessed using plasma samples with diverse phenotypes. Our results indicate that the ZIC-c HILIC column operated at neutral pH exhibited several advantages, including superior performance for different classes of compounds, better isomer separation, repeatability and high metabolic coverage. Regardless of the column type, the retention of inorganic ions in plasma leads to extensive adduct formation and co-elution with analytes, which results in ion-suppression as part of the overall plasma matrix effect. In ZIC-c HILIC, the sodium chloride ion effect was particularly observed for amino acids and amine classes. Successful performance of HILIC for separation of plasma samples with different phenotypes highlights this mode of separation as a valuable approach in global profiling of plasma sample and discovering the metabolic changes associated with health and disease.

A Sample Preparation Method for the Simultaneous Profiling of Signaling Lipids and Polar Metabolites in Small Quantities of Muscle Tissues from a Mouse Model for Sarcopenia.

The metabolic profiling of a wide range of chemical classes relevant to understanding sarcopenia under conditions in which sample availability is limited, e.g., from mouse models, small muscles, or muscle biopsies, is desired. Several existing metabolomics platforms that include diverse classes of signaling lipids, energy metabolites, and amino acids and amines would be informative for suspected biochemical pathways involved in sarcopenia. The sample limitation requires an optimized sample preparation method with minimal losses during isolation and handling and maximal accuracy and reproducibility. Here, two developed sample preparation methods, BuOH-MTBE-Water (BMW) and BuOH-MTBE-More-Water (BMMW), were evaluated and compared with previously reported methods, Bligh-Dyer (BD) and BuOH-MTBE-Citrate (BMC), for their suitability for these classes. The most optimal extraction was found to be the BMMW method, with the highest extraction recovery of 63% for the signaling lipids and 81% for polar metabolites, and an acceptable matrix effect (close to 1.0) for all metabolites of interest. The BMMW method was applied on muscle tissues as small as 5 mg (dry weight) from the well-characterized, prematurely aging, DNA repair-deficient Ercc1∆/- mouse mutant exhibiting multiple-morbidities, including sarcopenia. We successfully detected 109 lipids and 62 polar targeted metabolites. We further investigated whether fast muscle tissue isolation is necessary for mouse sarcopenia studies. A muscle isolation procedure involving 15 min at room temperature revealed a subset of metabolites to be unstable; hence, fast sample isolation is critical, especially for more oxidative muscles. Therefore, BMMW and fast muscle tissue isolation are recommended for future sarcopenia studies. This research provides a sensitive sample preparation method for the simultaneous extraction of non-polar and polar metabolites from limited amounts of muscle tissue, supplies a stable mouse muscle tissue collection method, and methodologically supports future metabolomic mechanistic studies of sarcopenia.

Erratum: A Sensitive and Wide Coverage Ambient Mass Spectrometry Imaging Method for Functional Metabolites Based Molecular Histology.

[This corrects the article DOI: 10.1002/advs.201800250.].

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.

A Sensitive and Wide Coverage Ambient Mass Spectrometry Imaging Method for Functional Metabolites Based Molecular Histology.

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

Rapid and sensitive liquid chromatography-tandem mass spectrometric method for the quantitative determination of potentially harmful substance 5,5'-oxydimethylenebis (2-furfural) in traditional Chinese medicine injections.

With the rapid development and wide application of traditional Chinese medicine injection (TCMI), a number of adverse events of some TCMIs have incessantly been reported and have drawn broad attention in recent years. Establishing effective and practical analytical methods for safety evaluation and quality control of TCMI can help to improve the safety of TCMIs in clinical applications. In this study, a sensitive and rapid high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method has been developed and validated for the quantitative determination of potentially harmful substance 5,5'-oxydimethylenebis (2-furfural, OMBF) in TCMI samples. Chromatographic separation was performed on a C18 reversed-phase column (150 mm × 2.1 mm, 5 µm) by gradient elution, using methanol-water containing 0.1% formic acid as mobile phase at the flow rate of 0.3 mL/min. MS/MS detection was performed on a triple quadrupole mass spectrometer with positive electrospray ionization in the multiple reaction-monitoring mode. The method was sensitive with a limit of quantification of 0.3 ng/mL and linear over the range of 0.3-30 ng/mL (r=0.9998). Intra- and inter-day precision for analyte was <9.52% RSD with recoveries in the range 88.0-109.67% at three concentration levels. The validated method was successfully applied to quantitatively determine the compound OMBF in TCMIs and glucose injections. Our study indicates that this method is simple, sensitive, practicable and reliable, and could be applied for safety evaluation and quality control of TCMIs and glucose injections.

MassImager: A software for interactive and in-depth analysis of mass spectrometry imaging data.

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