Liquid–liquid extraction combined with differential isotope dimethylaminophenacyl labeling for improved metabolomic profiling of organic acids

Quantification of gut microbiome metabolites using chemical isotope derivatization strategy combined with LC-MS/MS: Application in neonatal hypoxic-ischemic encephalopathy rat model

The gut microbiome plays pivotal roles in various physiological and pathological processes, with key metabolites including short chain fatty acids (SCFAs), bile acids (BAs), and tryptophan (TRP) derivatives gaining significant attention for their diverse physiological roles. However, quantifying these metabolites presents challenges due to structural similarity, low abundance, and inherent technical limitations in traditional detection methods. In this study, we developed a precise and sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) method utilizing a chemical isotope derivatization technique employing 4-(aminomethyl)-N,N-dimethylaniline-d0/d6 (4-AND-d0/d6) reagents to quantify 37 typical gut microbiome-derived metabolites. This method achieved an impressive 1500-fold enhancement in sensitivity for detecting metabolites, compared to methods using non-derivatized, intact molecules. Moreover, the quantitative accuracy of our chemical isotope derivatization strategy proved comparable to the stable isotope labeled internal standards (SIL-IS) method. Subsequently, we successfully applied this newly developed method to quantify target metabolites in plasma, brain, and fecal samples obtained from a neonatal hypoxic-ischemic encephalopathy (HIE) rat model. The aim was to identify crucial metabolites associated with the progression of HIE. Overall, our sensitive and reliable quantification method holds promise in elucidating the role of gut microbiome metabolites in the pathogenesis of various diseases.

Plasma metabolome analysis for predicting antiviral treatment efficacy in chronic hepatitis B: diagnostic biomarkers and therapeutic insights

The early and accurate identification of predictive biomarkers for antiviral treatment efficacy remains a significant clinical challenge, particularly in the management of chronic hepatitis B (CHB). This study aimed to assess whether the plasma metabolome could reliably predict the success of antiviral therapy in CHB patients. We conducted a retrospective analysis on 56 treatment-naive CHB patients at the First Affiliated Hospital of Zhejiang University from December 2013 to March 2016. Patients who underwent a 48-week treatment regimen of entecavir (ETV) and interferon-alpha (IFN-α) were randomly assigned to either a discovery cohort (n=29) or a validation cohort (n=27). Based on the outcome of the treatment, patients were classified as HBeAg seroconversion group (High responders, Hrp) or the non-remission group (Low responder, Lrp). Our methodology involved an untargeted analysis of the amine/phenol and carboxylic acid submetabolomes in the CHB patients under treatment, utilizing chemical isotope labeling (CIL) techniques with liquid chromatography-mass spectrometry (LC-MS). Several metabolites were identified as having significant diagnostic potential for distinguishing Hrp from Lrp, with areas under the receiver operating characteristic curve (AUC) exceeding those typical clinical indicators. Notably, four metabolites, namely 2-methyl-3-ketovaleric acid, 2-ketohexanoic acid, 6-oxo-1,4,5,6-tetrahydronicotinic acid, and α-ketoisovaleric acid, demonstrated exceptionally high sensitivity and specificity in both cohorts, nearing 100%. In contrast, the clinical indicators, including HBcAb, log(HBsAg), and HBeAb, demonstrated lower and inconsistent sensitivity and specificity between the discovery and validation cohorts. Using HBcAb as a marker, the sensitivity was 87.5% with 76.9% specificity in the discovery cohort; however, the sensitivity dropped to 46.7% with 91.7% specificity in the validation cohort. Using log(HBsAg), the sensitivity was 84.6% with 69.2% specificity in the discovery cohort, compared to 85.7% sensitivity and 83.3% specificity in the validation cohort. For HBeAb, the separation of Hrp and Lrp had a sensitivity of 87.5% with 69.2% specificity in the discovery cohort, while the validation cohort showed 86.7% sensitivity and 91.7% specificity.

Click chemistry-based enrichment strategy for tracing cellular fatty acid metabolism by LC-MS/MS

Fatty acids (FAs), which were initially recognized as energy sources and essential building blocks of biomembranes, serve as the precursors of important signaling molecules. Tracing FA metabolism is essential to understanding the biochemical activity and role of FAs in physiological and pathological events. Inspired by the advances in click chemistry for protein enrichment, we herein established a click chemistry-based enrichment (CCBE) strategy for tracing the cellular metabolism of eicosapentaenoic acid (EPA, 20:5 n-3) in neural cells. Terminal alkyne-labeled EPA (EPAA) used as a surrogate was incubated with N2a, mouse neuroblastoma cells, and alkyne-labeled metabolites (ALMs) were selectively captured by an azide-modified resin via a Cu(I)-catalyzed azide-alkyne cycloaddition reaction for enrichment. After removing unlabeled metabolites, ALMs containing a triazole moiety were cleaved from solid-phase resins and subjected to liquid chromatography mass spectrometry (LC-MS) analysis. The proposed CCBE strategy is highly selective for capturing and enriching alkyne-labeled metabolites from the complicated matrices. In addition, this method can overcome current detection limits by enhancing MS sensitivity of targets, improving the chromatographic separation of sn-position glycerophospholipid regioisomers, facilitating structural characterization of ALMs by a specific MS/MS fragmentation signature, and providing versatile fluorescence detection of ALMs for cellular distribution. This CCBE strategy might be expanded to trace the metabolism of other FAs, small molecules, or drugs.

CIL-ExPMRM: An Ultrasensitive Chemical Isotope Labeling Assisted Pseudo-MRM Platform to Accelerate Exposomic Suspect Screening

Exposome is the future of next-generation environmental health to establish the association between environmental exposure and diseases. However, due to low concentrations of exposure chemicals, exposome has been hampered by lacking an effective analytical platform to characterize its composition. In this study, by combining the benefit of chemical isotope labeling and pseudo-multiple reaction monitoring (CIL-pseudo-MRM), we have developed one highly sensitive and high-throughput platform (CIL-ExPMRM) by isotope labeling urinary exposure biomarkers. Dansyl chloride (DnsCl), N-methylphenylethylamine (MPEA), and their isotope-labeled forms were used to derivatize polar hydroxyl and carboxyl compounds, respectively. We have programmed a series of scripts to optimize MRM transition parameters, curate the MRM database (>70,000 compounds), predict accurate retention time (RT), and automize dynamic MRMs. This was followed by an automated MRM peak assignment, peak alignment, and statistical analysis. A computational pipeline was eventually incorporated into a user-friendly website interface, named CIL-ExPMRM (http://www.exposomemrm.com/). The performance of this platform has been validated with a relatively low false positive rate (10.7%) across instrumental platforms. CIL-ExPMRM has systematically overcome key bottlenecks of exposome studies to some extent and outperforms previous methods due to its independence of MS/MS availability, accurate RT prediction, and collision energy optimization, as well as the ultrasensitivity and automated robust intensity-based quantification. Overall, CIL-ExPMRM has great potential to advance the exposomic studies based on urinary biomarkers.

In-depth profiling of carboxyl compounds in Chinese Baijiu based on chemical derivatization and ultrahigh-performance liquid chromatography coupled to high-resolution mass spectrometry

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A systematic strategy for detection and annotation of carboxyl compound was developed.

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197 carboxyl compounds were detected in Chinese Baijiu for the first time.

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Annotation was based on MS1, t_R, in-silico MS/MS, and characteristic fragments.

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Three of carboxyl compounds were newly identified in Chinese Baijiu.

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Distribution of carboxyl compounds in Baijiu with different flavors was revealed.

Carboxyl compounds have a significant influence on the flavor of Chinese Baijiu. However, because of the structural diversity and low concentration, the deep profiling of carboxyl compounds in Chinese Baijiu is still challenging. In this work, a systematic method for comprehensive analysis of carboxyl compounds in Chinese Baijiu was established. After derivatized under optimized conditions, 197 p-dimethylaminophenacyl bromide-derived carboxylic compounds were annotated by multidimensional information including accurate mass, predicted t_R, in-silico MS/MS, and diagnostic ions for the first time. In addition, 48 of the 197 carboxyl compounds were positively identified, and three of them were newly identified in Chinese Baijiu. Moreover, we found the number and the concentration of carboxyl compounds in sauce-flavor Baijiu were more abundant than in strong-flavor Baijiu. This work provides a novel method for the analysis of carboxyl compounds in Baijiu and other complex samples.

Simultaneous 3-Nitrophenylhydrazine Derivatization Strategy of Carbonyl, Carboxyl and Phosphoryl Submetabolome for LC-MS/MS-Based Targeted Metabolomics with Improved Sensitivity and Coverage

Metabolomics is a powerful and essential technology for profiling metabolic phenotypes and exploring metabolic reprogramming, which enables the identification of biomarkers and provides mechanistic insights into physiology and disease. However, its applications are still limited by the technical challenges particularly in its detection sensitivity for the analysis of biological samples with limited amount, necessitating the development of highly sensitive approaches. Here, we developed a highly sensitive liquid chromatography tandem mass spectrometry method based on a 3-nitrophenylhydrazine (3-NPH) derivatization strategy that simultaneously targets carbonyl, carboxyl, and phosphoryl groups for targeted metabolomic analysis (HSD^ccp-TM) in biological samples. By testing 130 endogenous metabolites including organic acids, amino acids, carbohydrates, nucleotides, carnitines, and vitamins, we showed that the derivatization strategy resulted in significantly improved detection sensitivity and chromatographic separation capability. Metabolic profiling of merely 60 oocytes and 5000 hematopoietic stem cells primarily isolated from mice demonstrated that this method enabled routine metabolomic analysis in trace amounts of biospecimens. Moreover, the derivatization strategy bypassed the tediousness of inferring the MS fragmentation patterns and simplified the complexity of monitoring ion pairs of metabolites, which greatly facilitated the metabolic flux analysis (MFA) for glycolysis, the tricarboxylic acid (TCA) cycle, and pentose phosphate pathway (PPP) in cell cultures. In summary, the novel 3-NPH derivatization-based method with high sensitivity, good chromatographic separation, and broad coverage showed great potential in promoting metabolomics and MFA, especially in trace amounts of biospecimens.

Options of the Main Derivatization Approaches for Analytical ESI and MALDI Mass Spectrometry

The inclusion of preliminary chemical labeling (derivatization) in the analysis process by such powerful and widespread methods as electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) is a popular and widely used methodological approach. This is due to the need to remove some fundamental limitations inherent in these powerful analytic methods. Although a number of special reviews has been published discussing the utilization of derivatization approaches, the purpose of the present critical review is to comprehensively summarize, characterize and evaluate most of the previously developed and practically applied, as well as recently proposed representative derivatization reagents for ESI-MS and MALDI-MS platforms in their mostly sensitive positive ion mode and frequently hyphenated with separation techniques. The review is focused on the use of preliminary chemical labeling to facilitate the detection, identification, structure elucidation, quantification, profiling or MS imaging of compounds within complex matrices. Two main derivatization approaches, namely the introduction of permanent charge-fixed or highly proton affinitive residues into analytes are critically evaluated. In situ charge-generation, charge-switch and charge-transfer derivatizations are considered separately. The potential of using reactive matrices in MALDI-MS and chemical labeling in MS-based omics sciences is given.

High Coverage Profiling of Carboxylated Metabolites in HepG2 Cells Using Secondary Amine-Assisted Ultrahigh-Performance Liquid Chromatography Coupled to High-Resolution Mass Spectrometry

Carboxylic metabolites are an important class of metabolites, which widely exist in mammals with various types. Chemical isotope labeling liquid chromatography-mass spectrometry (CIL-LC-MS) has been widely used for the detection of carboxylated metabolites. However, high coverage analysis of carboxylated metabolites in biological samples is still challenging due to improper reactivity and selectivity of labeling reagents to carboxylated metabolites. In this study, we used N-methylphenylethylamine (MPEA) to label various types of carboxylated metabolites including short-chain fatty acids (SCFAs), medium-chain fatty acids (MCFAs), long-chain fatty acids (LCFAs), polycarboxylic acids (polyCAs), amino acids (AAs), and aromatic acids. Additionally, metabolites containing other functional groups, such as phenol, sulfhydryl, and phosphate groups, could not be labeled under the conditions of MPEA labeling. After MPEA labeling, the detection sensitivity of carboxylic acids was increased by 1-2 orders of magnitude, and their chromatographic retention on a reversed-phase (RP) column was enhanced (RT > 3 min). Under optimized labeling conditions, we used MPEA and d₃-N-methylphenylethylamine (d₃-MPEA) for high coverage screening of carboxylated metabolites in HepG2 cells by ultrahigh-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS). As a result, a total of 403 potential carboxylated metabolites were obtained of which 68 were confirmed based on our established in-house chemically labeled metabolite database (CLMD). SCFAs, MCFAs, LCFAs, polyCAs, AAs, and aromatic acids were all detected in HepG2 cell extracts. Due to the successful identification of AAs, the current method increased the coverage of carboxylated metabolites compared with our previous work. Moreover, 133 and 109 carboxylated metabolites with changed contents were obtained in HepG2 cells incubated with curcumin and R-3-hydroxybutyric acid, respectively. In general, our established method realized high coverage analysis of carboxylated metabolites in HepG2 cells.

STRATEGIES AND CHALLENGES IN METHOD DEVELOPMENT AND VALIDATION FOR THE ABSOLUTE QUANTIFICATION OF ENDOGENOUS BIOMARKER METABOLITES USING LIQUID CHROMATOGRAPHY-TANDEM MASS SPECTROMETRY

Metabolomics is a dynamically evolving field, with a major application in identifying biomarkers for drug development and personalized medicine. Numerous metabolomic studies have identified endogenous metabolites that, in principle, are eligible for translation to clinical practice. However, few metabolomic-derived biomarker candidates have been qualified by regulatory bodies for clinical applications. Such interruption in the biomarker qualification process can be largely attributed to various reasons including inappropriate study design and inadequate data to support the clinical utility of the biomarkers. In addition, the lack of robust assays for the routine quantification of candidate biomarkers has been suggested as a potential bottleneck in the biomarker qualification process. In fact, the nature of the endogenous metabolites precludes the application of the current validation guidelines for bioanalytical methods. As a result, there have been individual efforts in modifying existing guidelines and/or developing alternative approaches to facilitate method validation. In this review, three main challenges for method development and validation for endogenous metabolites are discussed, namely matrix effects evaluation, alternative analyte-free matrices, and the choice of internal standards (ISs). Some studies have modified the equations described by the European Medicines Agency for the evaluation of matrix effects. However, alternative strategies were also described; for instance, calibration curves can be generated in solvents and in biological samples and the slopes can be compared through ratios, relative standard deviation, or a modified Stufour suggested approaches while quantifying mainly endogenous metabolitesdent t-test. ISs, on the contrary, are diverse; in which seven different possible types, used in metabolomics-based studies, were identified in the literature. Each type has its advantages and limitations; however, isotope-labeled ISs and ISs created through isotope derivatization show superior performance. Finally, alternative matrices have been described and tested during method development and validation for the quantification of endogenous entities. These alternatives are discussed in detail, highlighting their advantages and shortcomings. The goal of this review is to compare, apprise, and debate current knowledge and practices in order to aid researchers and clinical scientists in developing robust assays needed during the qualification process of candidate metabolite biomarkers. © 2019 John Wiley & Sons Ltd. Mass Spec Rev.

MMEASE: Online meta-analysis of metabolomic data by enhanced metabolite annotation, marker selection and enrichment analysis

Large-scale and long-term metabolomic studies have attracted widespread attention in the biomedical studies yet remain challenging despite recent technique progresses. In particular, the ineffective way of experiment integration and limited capacity in metabolite annotation are known issues. Herein, we constructed an online tool MMEASE enabling the integration of multiple analytical experiments with an enhanced metabolite annotation and enrichment analysis (https://idrblab.org/mmease/). MMEASE was unique in capable of (1) integrating multiple analytical blocks; (2) providing enriched annotation for >330 thousands of metabolites; (3) conducting enrichment analysis using various categories/sub-categories. All in all, MMEASE aimed at supplying a comprehensive service for large-scale and long-term metabolomics, which might provide valuable guidance to current biomedical studies. SIGNIFICANCE: To facilitate the studies of large-scale and long-term metabolomic analysis, MMEASE was developed to (1) achieve the online integration of multiple datasets from different analytical experiments, (2) provide the most diverse strategies for marker discovery, enabling performance assessment and (3) significantly amplify metabolite annotation and subsequent enrichment analysis. MMEASE aimed at supplying a comprehensive service for long-term and large-scale metabolomics, which might provide valuable guidance to current biomedical studies.

Comprehensive Lipidomic and Metabolomic Analysis for Studying Metabolic Changes in Lung Tissue Induced by a Vaccine against Respiratory Syncytial Virus

Respiratory syncytial virus (RSV) is the leading cause of acute lower respiratory infections in young children. Although the disease may be severe in immunocompromised, young, and elderly people, there is currently no approved vaccine. We previously reported the development and immunological assessment of a novel intranasal vaccine formulation consisting of a truncated version of the RSV fusion protein (ΔF) combined with a three-component adjuvant (TriAdj). Now, we aim to investigate the mechanism of action of the ΔF/TriAdj formulation by searching for metabolic alterations caused by intranasal immunization and the RSV challenge. We carried out untargeted lipidomics and submetabolome profiling (carboxylic acids and amine/phenol-containing metabolites) of lung tissue from ΔF/TriAdj-immunized and nonimmunized, RSV-challenged mice. We observed significant changes of lipids involved in the lung surfactant layer for the nonimmunized animals compared to healthy controls but not for the immunized mice. Metabolic pathways involving the synthesis and regulation of amino acids and unsaturated fatty acids were also modulated by immunization and the RSV challenge. This study illustrates that lipidomic and metabolomic profiling could provide a more comprehensive understanding of the immunological and metabolic alterations caused by RSV and the modulation effected by the ΔF/TriAdj formulation.

Quantification of microbiota-related phenols and aromatic acids in mouse feces of a diabetic nephropathy model by simultaneous BDAPE derivatization using ultra-performance liquid chromatography-tandem mass spectrometry

In the intestine, several phenols and aromatic acids are generated by microbiota and are highly related to the formation of uremic toxins. Herein, we developed a new derivatization reagent, 2-bromo-1-[4-(dimethylamino)phenyl] ethyl ketone (BDAPE), that reacted simultaneously with phenols and aromatic acids. Following a reaction within 2 h at 60 °C in the presence of 200 mM potassium carbonate (K₂CO₃), the obtained BDAPE derivatives were separated on a reversed-phase C18 column and quantified by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) in positive electrospray ionization mode. This method allowed a lower limit of quantification (LLOQ) of 0.090 μΜ for 3-indolepropionic acid (3IPA), indole-3-acetic acid (3IAA), p-cresol (PC), benzoic acid (BA), and phenol (PN); 0.30 μΜ for phenylacetic acid (PAA); 0.45 μΜ for 4-hydroxyphenylacetic acid (4HPAA); and 0.60 μΜ for 3-phenylpropionic acid (PPA). Methodological validation further demonstrated acceptable accuracy (%RE < 16.1) and precision (%RSD < 16.2), suggesting that this is a sensitive and robust method for simultaneous quantification of phenols and aromatic acids. The method was successfully applied to analyze these microbiota-related analytes in mouse feces of a diabetic nephropathy model. Graphical abstract.

Development of a new quantification method for organic acids in urine as potential biomarkers for respiratory illness

Asthma and chronic obstructive pulmonary disease (COPD) are common respiratory disorders that have similar clinical presentation and misdiagnosis may lead to improper treatment. There is a need for a better, non-invasive test for the differentiation of asthma and COPD. In this study, we developed a new validated LC-MS/MS method for 17 urinary organic acids that could serve as potential biomarkers. Human urine samples were collected from adults with asthma or COPD. LC-MS/MS was performed using the differential isotope labeling approach. 4-(Dimethylamino) phenacyl bromide (DmPA) was used for derivatization using two different carbon isotopes, allowing for the formation of internal standard for each metabolite. Gradient elution was employed on a C₁₈ column while the LC-MS/MS operated in the multiple reaction monitoring mode (MRM). Regulatory guidelines were used for method validation. Partial Least Squares Discriminative Analysis (PLS-DA) was applied to the log-transformed values of metabolites in each group of asthma and COPD subjects. Full validation in targeted metabolomics is scarce with usually limited number of metabolites, unlike fit-for-purpose approach. Due to the endogenous nature of the metabolites, numerous challenges were encountered during method development and validation, such as the lactic acid interference from the surrounding environment. The required specificity, accuracy and precision was successfully achieved. The method was fully validated, ensuring robustness and reproducibility when analyzing patient samples. The method was applied to analyze human urine samples and PLS-DA analysis showed differentiation of asthma and COPD subjects (R2 0.89, Q2 0.68). As targeted metabolomics is expanding to the clinical sphere, more validated methods/strategies are needed. Our work will expand the current knowledge-base regarding targeted metabolomics.

Extractive Ratio Analysis NMR Spectroscopy for Metabolite Identification in Complex Biological Mixtures

The complexity of biological mixtures continues to challenge efforts aimed at unknown metabolite identification in the metabolomics field. To address this challenge, we provide a new method to identify related peaks from individual metabolites in complex NMR spectra. Extractive ratio analysis NMR spectroscopy (E-RANSY) builds on our previously described ratio analysis method [ Wei et al. Anal. Chem. 2011 , 83 , 7616 - 7623 ] and exploits the simplified NMR spectra provided by the extraction of metabolites under varied pH conditions. Under such conditions, metabolites from the same biological specimen are extracted differentially, and the resulting NMR spectra exhibit characteristics favorable for unraveling unknown metabolite peaks using ratio analysis. We demonstrate the utility of the E-RANSY method by extracting carboxylic acid containing metabolites from human urine, one of the highly complex biological mixtures encountered in the metabolomics field. E-RANSY performs better than STOCSY and the original RANSY method and offers new avenues to identify unknown metabolites in complex biological mixtures.

Assessing the Effectiveness of Direct Data Merging Strategy in Long-Term and Large-Scale Pharmacometabonomics

Because of the extended period of clinic data collection and huge size of analyzed samples, the long-term and large-scale pharmacometabonomics profiling is frequently encountered in the discovery of drug/target and the guidance of personalized medicine. So far, integration of the results (ReIn) from multiple experiments in a large-scale metabolomic profiling has become a widely used strategy for enhancing the reliability and robustness of analytical results, and the strategy of direct data merging (DiMe) among experiments is also proposed to increase statistical power, reduce experimental bias, enhance reproducibility and improve overall biological understanding. However, compared with the ReIn, the DiMe has not yet been widely adopted in current metabolomics studies, due to the difficulty in removing unwanted variations and the inexistence of prior knowledges on the performance of the available merging methods. It is therefore urgently needed to clarify whether DiMe can enhance the performance of metabolic profiling or not. Herein, the performance of DiMe on 4 pairs of benchmark datasets was comprehensively assessed by multiple criteria (classification capacity, robustness and false discovery rate). As a result, integration/merging-based strategies (ReIn and DiMe) were found to perform better under all criteria than those strategies based on single experiment. Moreover, DiMe was discovered to outperform ReIn in classification capacity and robustness, while the ReIn showed superior capacity in controlling false discovery rate. In conclusion, these findings provided valuable guidance to the selection of suitable analytical strategy for current metabolomics.

Metabolic profiling associated with autophagy of human placenta-derived mesenchymal stem cells by chemical isotope labeling LC-MS

Autophagy has been reported to have a pivotal role in maintaining stemness, regulating immunomodulation and enhancing the survival of mesenchymal stem cells (MSCs). However, the effect of autophagy on MSC metabolism is largely unknown. Here, we report a workflow for examining the impact of autophagy on human placenta-derived MSC (hPMSC) metabolome profiling with chemical isotope labeling (CIL) LC-MS. Rapamycin or 3-methyladenine was successfully used to induce or inhibit autophagy, respectively. Then, ¹²C- and ¹³C-dansylation labeling LC-MS were used to profile the amine/phenol submetabolome. A total of 935 peak pairs were detected and 50 metabolites were positively identified using the dansylation metabolite standards library, and 669 metabolites were putatively identified based on an accurate mass match in metabolome databases. ¹²C/¹³C-p-dimethylaminophenacyl bromide labeling LC-MS was used to analyze the carboxylic acid submetabolome; 4736 peak pairs were detected, among which 33 metabolites were positively identified in the dimethylaminophenacyl metabolite standards library, and 3007 metabolites were putatively identified. PCA/OPLS-DA analysis combined with volcano plots and Venn diagrams was used to determine the significant metabolites. Metabolites pathway analysis demonstrated that hPMSCs appeared to generate more ornithine with the arginine and proline metabolism pathway and utilized more pantothenic acid to synthesize acetyl-CoA in the beta-alanine metabolism pathway when autophagy was activated. Meanwhile, acetyl-CoA conversion to fatty acids led to accumulation in the fatty acid biosynthesis pathway. In contrast, when autophagy was suppressed, a reduction in metabolites demonstrated weakened metabolic activity in these metabolic pathways. Our research provides a more comprehensive understanding of hPMSC metabolism associated with autophagy.

Improving metabolome coverage and data quality: advancing metabolomics and lipidomics for biomarker discovery

This Feature Article highlights some of the key challenges within the field of metabolomics and examines what role separation and analytical sciences can play to improve the use of metabolomics in biomarker discovery and personalized medicine. Recent progress in four key areas is highlighted: (i) improving metabolite coverage, (ii) developing accurate methods for unstable metabolites including in vivo global metabolomics methods, (iii) advancing inter-laboratory studies and reference materials and (iv) improving data quality, standardization and quality control of metabolomics studies.

Metabolomic analysis of oxidative stress: Superoxide dismutase mutation and paraquat induced stress in Drosophila melanogaster

Oxidative stress results in substantial biochemical and physiological perturbations in essentially all organisms. To determine the broad metabolic effects of oxidative stress, we have quantified the response in Drosophila melanogaster to both genetically and environmentally derived oxidative stress. Flies were challenged with loss of Superoxide dismutase activity or chronic or acute exposure to the oxidizing chemical paraquat. Metabolic changes were then quantified using a recently developed chemical isotope labeling (CIL) liquid chromatography - mass spectrometry (LC-MS) platform that targets the carboxylic acid and amine/phenol submetabolomes with high metabolic coverage. We discovered wide spread changes in both submetabolomes in response to all three types of stresses including: changes to the urea cycle, tryptophan metabolism, porphyrin metabolism, as well as a series of metabolic pathways involved in glutathione synthesis. Strikingly, while there are commonalities across the conditions, all three resulted in different metabolomic responses, with the greatest difference between the genetic and environmental responses. Genetic oxidative stress resulted in substantially more widespread effects, both in terms of the percent of the metabolome altered, and the magnitude of changes in individual metabolites. Chronic and acute environmental stress resulted in more similar responses although both were distinct from genetic stress. Overall, these results indicate that the metabolomic response to oxidative stress is complex, reaching across multiple metabolic pathways, with some shared features but with more features unique to different, specific stressors.

Profiling of potential brassinosteroids in different tissues of rape flower by stable isotope labeling - liquid chromatography/mass spectrometry analysis

Brassinosteroids (BRs) play crucial roles in a variety of physiological processes in plants. The full elucidation of the functions of RBs relies on sensitive detection and accurate measurement of BRs in plants. However, the identification and quantification of BRs are challenging due to their low abundance as well as poor ionization efficiencies during mass spectrometry-based analysis. Herein, we developed a highly sensitive and selective strategy for profiling potential BRs in plants by stable isotope labeling liquid chromatography multiple reaction monitoring scan mass spectrometry (SIL-LC-MRM-MS) analysis. In the strategy, we used a pair of stable isotope labeling reagents 4-phenylaminomethyl-benzeneboronic acid (4-PAMBA) and d₅-4-phenylaminomethyl-benzeneboronic acid (4-PAMBA-d₅) that can react with C22-C23 cis-diol on BRs for profiling potential BRs in plant tissues. The 4-PAMBA and 4-PAMBA-d₅ labeled BRs could generate two characteristic neutral loss under collision induced dissociation (CID), respectively, which is used to establish the MRM-based detection and screening. The precursor ions of BRs labeled with 4-PAMBA and 4-PAMBA-d₅ were set according to the reported structures of BRs, and the corresponding product ions were predicted by subtracting the lost neutral loss. In this respect, corresponding precursor ions and product ions in MRM transitions are formed. The peak pairs with a fixed mass difference, similar retention times and intensities were assigned as potential BRs. Using the developed SIL-LC-MRM-MS strategy, we successfully found 13 potential BR in different tissues of rape flower. Taken together, the SIL-LC-MRM-MS analytical strategy is promising for profiling potential BRs as well as other compounds that have the same functional moiety from complex biological samples.

Elevated acetyl-CoA by amino acid recycling fuels microalgal neutral lipid accumulation in exponential growth phase for biofuel production

Microalgal neutral lipids [mainly in the form of triacylglycerols (TAGs)], feasible substrates for biofuel, are typically accumulated during the stationary growth phase. To make microalgal biofuels economically competitive with fossil fuels, generating strains that trigger TAG accumulation from the exponential growth phase is a promising biological approach. The regulatory mechanisms to trigger TAG accumulation from the exponential growth phase (TAEP) are important to be uncovered for advancing economic feasibility. Through the inhibition of pyruvate dehydrogenase kinase by sodium dichloroacetate, acetyl-CoA level increased, resulting in TAEP in microalga Dunaliella tertiolecta. We further reported refilling of acetyl-CoA pool through branched-chain amino acid catabolism contributed to an overall sixfold TAEP with marginal compromise (4%) on growth in a TAG-rich D. tertiolecta mutant from targeted screening. Herein, a three-step α loop-integrated metabolic model is introduced to shed lights on the neutral lipid regulatory mechanism. This article provides novel approaches to compress lipid production phase and heightens lipid productivity and photosynthetic carbon capture via enhancing acetyl-CoA level, which would optimize renewable microalgal biofuel to fulfil the demanding fuel market.

Mass spectrometric based approaches in urine metabolomics and biomarker discovery

Urine metabolomics has recently emerged as a prominent field for the discovery of non-invasive biomarkers that can detect subtle metabolic discrepancies in response to a specific disease or therapeutic intervention. Urine, compared to other biofluids, is characterized by its ease of collection, richness in metabolites and its ability to reflect imbalances of all biochemical pathways within the body. Following urine collection for metabolomic analysis, samples must be immediately frozen to quench any biogenic and/or non-biogenic chemical reactions. According to the aim of the experiment; sample preparation can vary from simple procedures such as filtration to more specific extraction protocols such as liquid-liquid extraction. Due to the lack of comprehensive studies on urine metabolome stability, higher storage temperatures (i.e. 4°C) and repetitive freeze-thaw cycles should be avoided. To date, among all analytical techniques, mass spectrometry (MS) provides the best sensitivity, selectivity and identification capabilities to analyze the majority of the metabolite composition in the urine. Combined with the qualitative and quantitative capabilities of MS, and due to the continuous improvements in its related technologies (i.e. ultra high-performance liquid chromatography [UPLC] and hydrophilic interaction liquid chromatography [HILIC]), liquid chromatography (LC)-MS is unequivocally the most utilized and the most informative analytical tool employed in urine metabolomics. Furthermore, differential isotope tagging techniques has provided a solution to ion suppression from urine matrix thus allowing for quantitative analysis. In addition to LC-MS, other MS-based technologies have been utilized in urine metabolomics. These include direct injection (infusion)-MS, capillary electrophoresis-MS and gas chromatography-MS. In this article, the current progresses of different MS-based techniques in exploring the urine metabolome as well as the recent findings in providing potentially diagnostic urinary biomarkers are discussed. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:115-134, 2017.

Discovery of urine biomarkers for bladder cancer via global metabolomics

Bladder cancer (BC) is latent in its early stage and lethal in its late stage. Therefore, early diagnosis and intervention are essential for successful BC treatment. Considering the limitations of current diagnostic tools, noninvasive biomarkers that are both highly sensitive and specific are needed to improve the overall survival and quality of life of patients. With the advent of systems biology, "-omics" technologies have been developed over the past few decades. As a promising member, global metabolomics has increasingly been found to have clear potential for biomarker discovery. However, urinary metabolomics studies related to BC have lagged behind those of other urinary cancers, and major findings have not been systematically reported. The objective of this review is to comprehensively list the currently identified potential urinary metabolite biomarkers for BC.

Methods used to increase the comprehensive coverage of urinary and plasma metabolomes by MS

Metabolomics, focusing on comprehensive analysis of all the metabolites in a biological system, provides a direct signature of biochemical activity. Using emerging technologies in MS, it is possible to simultaneously and rapidly analyze thousands of metabolites. However, due to the chemical and physical diversity of metabolites, it is difficult to acquire a comprehensive and reliable profiling of the whole metabolome. Here, we summarize the state of the art in metabolomics research, focusing on efforts to provide a more comprehensive metabolome coverage via improvements in two fundamental processes: sample preparation and MS analysis. Additionally, the reliable analysis is also highlighted via the combinations of multiple methods (e.g., targeted and untargeted approaches), and analytical quality control and calibration methods.

High-Performance Chemical Isotope Labeling Liquid Chromatography-Mass Spectrometry for Profiling the Metabolomic Reprogramming Elicited by Ammonium Limitation in Yeast

Information about how yeast metabolism is rewired in response to internal and external cues can inform the development of metabolic engineering strategies for food, fuel, and chemical production in this organism. We report a new metabolomics workflow for the characterization of such metabolic rewiring. The workflow combines efficient cell lysis without using chemicals that may interfere with downstream sample analysis and differential chemical isotope labeling liquid chromatography mass spectrometry (CIL LC-MS) for in-depth yeast metabolome profiling. Using (12)C- and (13)C-dansylation (Dns) labeling to analyze the amine/phenol submetabolome, we detected and quantified a total of 5719 peak pairs or metabolites. Among them, 120 metabolites were positively identified using a library of 275 Dns-metabolite standards, and 2980 metabolites were putatively identified based on accurate mass matches to metabolome databases. We also applied (12)C- and (13)C-dimethylaminophenacyl (DmPA) labeling to profile the carboxylic acid submetabolome and detected over 2286 peak pairs, from which 33 metabolites were positively identified using a library of 188 DmPA-metabolite standards, and 1595 metabolites were putatively identified. Using this workflow for metabolomic profiling of cells challenged by ammonium limitation revealed unexpected links between ammonium assimilation and pantothenate accumulation that might be amenable to engineering for better acetyl-CoA production in yeast. We anticipate that efforts to improve other schemes of metabolic engineering will benefit from application of this workflow to multiple cell types.

Profiling of aldehyde-containing compounds by stable isotope labelling-assisted mass spectrometry analysis

We developed a strategy for non-targeted profiling of aldehyde-containing compounds by stable isotope labelling in combination with liquid chromatography-double neutral loss scan-mass spectrometry (SIL-LC-DNLS-MS) analysis. A pair of stable isotope labelling reagents (4-(2-(trimethylammonio)ethoxy)benzenaminium halide, 4-APC and d4-4-(2-(trimethylammonio)ethoxy)benzenaminium halide, 4-APC-d4) that can selectively label aldehyde-containing compounds were synthesized. The 4-APC and 4-APC-d4 labelled compounds were capable of generating two characteristic neutral fragments of 87 Da and 91 Da, respectively, under collision induced dissociation (CID). Therefore, double neutral loss scans were carried out simultaneously to record the signals of the potential aldehyde-containing compounds. In this respect, the aldehyde-containing compounds from two samples labelled with 4-APC and 4-APC-d4 were ionized at the same time but recorded separately by mass spectrometry. The peak pairs with characteristic mass differences (n × 4 Da) can be readily extracted from the DNLS spectra and assigned as potential aldehyde-containing candidates, which facilitates the identification of the target aldehydes. 4-APC and 4-APC-d4 labelling also dramatically increased detection sensitivities of the derivatives. Using the SIL-LC-DNLS-MS strategy, we successfully profiled the aldehyde-containing compounds in human urine and white wine. Our results showed that 16 and 19 potential aldehyde-containing compounds were discovered in human urine and white wine, respectively. In addition, 5 and 4 aldehyde-containing compounds in human urine and white wine were further identified by comparison with aldehyde standards. Altogether, SIL-LC-DNLS-MS demonstrated to be a promising approach in the identification and relative quantification of aldehyde-containing compounds from complex samples.

A short review of applications of liquid chromatography mass spectrometry based metabolomics techniques to the analysis of human urine

Mass spectrometry based metabolomics profiling.

Effects of sample injection amount and time-of-flight mass spectrometric detection dynamic range on metabolome analysis by high-performance chemical isotope labeling LC-MS

The effect of sample injection amount on metabolome analysis in a chemical isotope labeling (CIL) liquid chromatography-mass spectrometry (LC-MS) platform was investigated. The performance of time-of-flight (TOF) mass spectrometers with and without a high-dynamic-range (HD) detection system was compared in the analysis of (12)C2/(13)C2-dansyl labeled human urine samples. An average of 1635 ± 21 (n = 3) peak pairs or putative metabolites was detected using the HD-TOF-MS, compared to 1429 ± 37 peak pairs from a conventional or non-HD TOF-MS. In both instruments, signal saturation was observed. However, in the HD-TOF-MS, signal saturation was mainly caused by the ionization process, while in the non-HD TOF-MS, it was caused by the detection process. To extend the MS detection range in the non-HD TOF-MS, an automated switching from using (12)C to (13)C-natural abundance peaks for peak ratio calculation when the (12)C peaks are saturated has been implemented in IsoMS, a software tool for processing CIL LC-MS data. This work illustrates that injecting an optimal sample amount is important to maximize the metabolome coverage while avoiding the sample carryover problem often associated with over-injection. A TOF mass spectrometer with an enhanced detection dynamic range can also significantly increase the number of peak pairs detected.

BIOLOGICAL SIGNIFICANCE

In chemical isotope labeling (CIL) LC-MS, relative metabolite quantification is done by measuring the peak ratio of a (13)C2-/(12)C2-labeled peak pair for a given metabolite present in two comparative samples. The dynamic range of peak ratio measurement does not need to be very large, as only subtle changes of metabolite concentrations are encountered in most metabolomic studies where relative metabolome quantification of different groups of samples is performed. However, the absolute concentrations of different metabolites can be very different, requiring a technique to provide a wide detection dynamic range to allow the detection of as many peak pairs as possible. In this work, we demonstrated that controlling the sample injection amount into LC-MS was critical to achieve the optimal detectability while avoiding sample carry-over problem. In addition, the use of a high-dynamic-range TOF system increased the number of peak pairs detected, compared to a conventional TOF system. We also investigated the ionization and detection saturation factors limiting the dynamic range of detection. This article is part of a Special Issue entitled: Protein dynamics in health and disease. Guest Editors: Pierre Thibault and Anne-Claude Gingras.

Derivatization of the tricarboxylic acid intermediates with O-benzylhydroxylamine for liquid chromatography-tandem mass spectrometry detection

The tricarboxylic acid (TCA) cycle is an interface among glycolysis, lipid metabolism, and amino acid metabolism. Increasing interest in cancer metabolism has created a demand for rapid and sensitive methods for quantifying the TCA cycle intermediates and related organic acids. We have developed a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method to quantify the TCA cycle intermediates in a 96-well format after O-benzylhydroxylamine (O-BHA) derivatization under aqueous conditions. This method was validated for quantitation of all common TCA cycle intermediates with good sensitivity, including α-ketoglutarate, malate, fumarate, succinate, 2-hydroxyglutarate, citrate, oxaloacetate, pyruvate, isocitrate, and lactate using a 8-min run time in cancer cells and tissues. The method was used to detect and quantify changes in metabolite levels in cancer cells and tumor tissues treated with a pharmacological inhibitor of nicotinamide phosphoribosyl transferase (NAMPT). This method is rapid, sensitive, and reproducible, and it can be used to assess metabolic changes in cancer cells and tumor samples.