Metabolic profiling using Fourier-transform ion-cyclotron-resonance mass spectrometry

With the aid of the extreme resolving power of Fourier-transform ion-cyclotron-resonance mass spectrometry (FT-ICR/MS), we have developed a metabolomics platform for high-throughput metabolic profiling and metabolite candidate identification integrating a data-processing system, the Dr.DMASS program (), and a metabolite-species database, KNApSAcK (). We discuss the potential of this FT-ICR/MS-based metabolic profiling scheme as a general metabolomics tool by clarification of plant metabolic disorders and specific metabolite accumulation patterns caused by herbicidal enzyme inhibitors.     

Use of ultra-performance liquid chromatography/time-of-flight mass spectrometry with nozzle-skimmer fragmentation for comprehensive quantitative analysis of secondary metabolites in Arabidopsis thaliana

This study sought to develop techniques for LC/MS-based metabolomics and to verify that an MS/MS spectral tag (MS2T) could be used in practical secondary metabolite profiling. The retention time (RT), precursor ions, and fragment ions generated by nozzle-skimmer fragmentation were determined using ultra-performance liquid chromatography/time-of-flight mass spectrometry (UPLC/TOF-MS) and compared with the MS2T. A standard mix was analyzed with UPLC/TOF-MS under the same conditions as were used to construct the MS2T. The difference in RT for the standards was less than 0.15 min and the average RSD was about 2.8%, suggesting that the analysis was highly repeatable. Both precursor ions and fragment ions were observed when the cone voltage was 75 V. Experimental data and fragmentation pattern in the MS2T annotation list were highly similar. Wild-type and cas-1 mutant Arabidopsis thaliana samples treated with an elicitor were analyzed using UPLC/TOF-MS. Sixty-five peaks were successfully annotated. Fragment ions were observed with nozzle-skimmer fragmentation in 50 of 65 (77%) peaks. The reliability of annotation may have increased as a result of fragment ions. Results of multivariate analysis suggested that cas-1 was related to induction of the biosynthesis of these flavonoids. The devised method facilitated practical secondary metabolite profiling.     

Semi‐quantitative and structural metabolic phenotyping by direct infusion ion trap mass spectrometry and its application in genetical metabolomics

The identification of quantitative trait loci (QTL) for plant metabolites requires the quantitation of these metabolites across a large range of progeny. We developed a rapid metabolic profiling method using both untargeted and targeted direct infusion tandem mass spectrometry (DIMSMS) with a linear ion trap mass spectrometer yielding sufficient precision and accuracy for the quantification of a large number of metabolites in a high‐throughput environment. The untargeted DIMSMS method uses top‐down data‐dependent fragmentation yielding MS² and MS³ spectra. We have developed software tools to assess the structural homogeneity of the MS² and MS³ spectra hence their utility for phenotyping and genetical metabolomics. In addition we used a targeted DIMS(MS) method for rapid quantitation of specific compounds. This method was compared with targeted LC/MS/MS methods for these compounds. The DIMSMS methods showed sufficient precision and accuracy for QTL discovery. We phenotyped 200 individual Lolium perenne genotypes from a mapping population harvested in two consecutive years. Computational and statistical analyses identified 246 nominal m/z bins with sufficient precision and homogeneity for QTL discovery. Comparison of the data for specific metabolites obtained by DIMSMS with the results from targeted LC/MS/MS analysis showed that quantitation by this metabolic profiling method is reasonably accurate. Of the top 100 MS¹ bins, 22 ions gave one or more reproducible QTL across the 2 years. Copyright © 2009 John Wiley & Sons, Ltd.     

Rapid screening and characterization of drug metabolites using multiple ion monitoring dependent product ion scan and postacquisition data mining on a hybrid triple quadrupole‐linear ion trap mass spectrometer

Multiple ion monitoring (MIM)‐dependent acquisition with a triple quadrupole‐linear ion trap mass spectrometer (Q‐trap) was previously developed for drug metabolite profiling. In the analysis, multiple predicted metabolite ions are monitored in both Q1 and Q3 regardless of their fragmentations. The collision energy in Q2 is set to a low value to minimize fragmentation. Once an expected metabolite is detected by MIM, enhanced product ion (EPI) spectral acquisition of the metabolite is triggered. To analyze in vitro metabolites, MIM‐EPI retains the sensitivity and selectivity similar to that of multiple reaction monitoring (MRM)‐EPI in the analysis of in vitro metabolites. Here we present an improved approach utilizing MIM‐EPI for data acquisition and multiple data mining techniques for detection of metabolite ions and recovery of their MS/MS spectra. The postacquisition data processing tools included extracted ion chromatographic analysis, product ion filtering and neutral loss filtering. The effectiveness of this approach was evaluated by analyzing oxidative metabolites of indinavir and glutathione (GSH) conjugates of clozapine and 4‐ethylphenol in liver microsome incubations. Results showed that the MIM‐EPI‐based data mining approach allowed for comprehensive detection of metabolites based on predicted protonated molecules, product ions or neutral losses without predetermination of the parent drug MS/MS spectra. Additionally, it enabled metabolite detection and MS/MS acquisition in a single injection. This approach is potentially useful in high‐throughout screening of metabolic soft spots and reactive metabolites at the drug discovery stage. Copyright © 2009 John Wiley & Sons, Ltd.     

Utilization of metabolomics to identify serum biomarkers for hepatocellular carcinoma in patients with liver cirrhosis

Characterizing the metabolic changes pertaining to hepatocellular carcinoma (HCC) in patients with liver cirrhosis is believed to contribute towards early detection, treatment, and understanding of the molecular mechanisms of HCC. In this study, we compare metabolite levels in sera of 78 HCC cases with 184 cirrhotic controls by using ultra performance liquid chromatography coupled with a hybrid quadrupole time-of-flight mass spectrometry (UPLC–QTOF MS). Following data preprocessing, the most relevant ions in distinguishing HCC cases from patients with cirrhosis are selected by parametric and non-parametric statistical methods. Putative metabolite identifications for these ions are obtained through mass-based database search. Verification of the identities of selected metabolites is conducted by comparing their MS/MS fragmentation patterns and retention time with those from authentic compounds. Quantitation of these metabolites is performed in a subset of the serum samples (10 HCC and 10 cirrhosis) using isotope dilution by selected reaction monitoring (SRM) on triple quadrupole linear ion trap (QqQLIT) and triple quadrupole (QqQ) mass spectrometers. The results of this analysis confirm that metabolites involved in sphingolipid metabolism and phospholipid catabolism such as sphingosine-1-phosphate (S-1-P) and lysophosphatidylcholine (lysoPC 17:0) are up-regulated in sera of HCC vs. those with liver cirrhosis. Down-regulated metabolites include those involved in bile acid biosynthesis (specifically cholesterol metabolism) such as glycochenodeoxycholic acid 3-sulfate (3-sulfo-GCDCA), glycocholic acid (GCA), glycodeoxycholic acid (GDCA), taurocholic acid (TCA), and taurochenodeoxycholate (TCDCA). These results provide useful insights into HCC biomarker discovery utilizing metabolomics as an efficient and cost-effective platform. Our work shows that metabolomic profiling is a promising tool to identify candidate metabolic biomarkers for early detection of HCC cases in high risk population of cirrhotic patients.     

Metabolite profiling and beyond: approaches for the rapid processing and annotation of human blood serum mass spectrometry data

In this paper, we describe data processing and metabolite identification approaches which lead to a rapid and semi-automated interpretation of metabolomics experiments. Data from metabolite fingerprinting using LC-ESI-Q-TOF/MS were processed with several open-source software packages, including XCMS and CAMERA to detect features and group features into compound spectra. Next, we describe the automatic scheduling of tandem mass spectrometry (MS) acquisitions to acquire a large number of MS/MS spectra, and the subsequent processing and computer-assisted annotation towards identification using the R packages MetShot, Rdisop, and the MetFusion application. We also implement a simple retention time prediction model using predicted lipophilicity logD, which predicts retention times within 42 s (6 min gradient) for most compounds in our setup. We putatively identified 44 common metabolites including several amino acids and phospholipids at metabolomics standards initiative (MSI) levels two and three and confirmed the majority of them by comparison with authentic standards at MSI level one. To aid both data integration within and data sharing between laboratories, we integrated data from two labs and mapped retention times between the chromatographic systems. Despite the different MS instrumentation and different chromatographic gradient programs, the mapped retention times agree within 26 s (20 min gradient) for 90 % of the mapped features.

Evaluation of ion mobility in capillary electrophoresis coupled to mass spectrometry for the identification in metabolomics

Currently, feature annotation remains one of the main challenges in untargeted metabolomics. In this context, the information provided by high-resolution mass spectrometry (HRMS) in addition to accurate mass can improve the quality of metabolite annotation, and MS/MS fragmentation patterns are widely used. Accurate mass and a separation index, such as retention time or effective mobility (μ_eff), in chromatographic and electrophoretic approaches, respectively, must be used for unequivocal metabolite identification. The possibility of measuring collision cross-section (CCS) values by using ion mobility (IM) is becoming increasingly popular in metabolomic studies thanks to the new generation of IM mass spectrometers. Based on their similar separation mechanisms involving electric field and the size of the compounds, the complementarity of ^DTCCS_N2 and μ_eff needs to be evaluated. In this study, a comparison of ^DTCCS_N2 and μ_eff was achieved in the context of feature identification ability in untargeted metabolomics by capillary zone electrophoresis (CZE) coupled with HRMS. This study confirms the high correlation of ^DTCCS_N2 with the mass of the studied metabolites as well as the orthogonality between accurate mass and μ_eff, making this combination particularly interesting for the identification of several endogenous metabolites. The use of IM-MS remains of great interest for facilitating the annotation of neutral metabolites present in the electroosmotic flow (EOF) that are poorly or not separated by CZE.     

CE-MS for metabolomics: Developments and applications in the period 2018–2020

Capillary electrophoresis-mass spectrometry (CE-MS) is now a mature analytical technique in metabolomics, notably for the efficient profiling of polar and charged metabolites. Over the past few years, (further) progress has been made in the design of improved interfacing techniques for coupling CE to MS; also, in the development of CE-MS approaches for profiling metabolites in volume-restricted samples, and in strategies that further enhance the metabolic coverage. In this article, which is a follow-up of a previous review article covering the years 2016–2018 (Electrophoresis 2019, 40, 165–179), the main (technological) developments in CE-MS methods and strategies for metabolomics are discussed covering the literature from July 2018 to June 2020. Representative examples highlight the utility of CE-MS in the fields of biomedical, clinical, microbial, plant and food metabolomics. A complete overview of recent CE-MS-based metabolomics studies is given in a table, which provides information on sample type and pretreatment, capillary coatings, and MS detection mode. Finally, some general conclusions and perspectives are given.     

Pressurized CEC coupled with QTOF‐MS for urinary metabolomics

Pressurized CEC (pCEC) coupled with ESI‐QTOF‐MS using a sheathless interface was applied for metabolomics to develop an alternative analytical method for metabolic profiling of complex biofluid samples such as urine. The hyphenated system was investigated with mixed standards and pooled urine samples to evaluate its precision, repeatability, linearity, sensitivity, and selectivity. The applied voltage, mobile phase, and gradient elution were optimized and applied for the analysis of urinary metabolites. Multivariate data analysis was subsequently performed and used to distinguish lung cancer patients from healthy controls successfully. High separation efficiency has been achieved in pCEC due to the EOF. For metabolite identification, the pCEC‐MS separation mechnism was helpful for discriminating the fragment ions of glutamine conjugates from co‐eluted metabolites. Three glutamine conjugates, including phenylacetylglutamine, acylglutamine C8:1, and acylglutamine C6:1 were identified among 16 differential urinary metabolites of lung cancer. Receiver‐operating‐characteristic analysis of acylglutamine C8:1 resulted in an area‐under‐curve value of 0.882. Overall, this work suggests that this pCEC‐ESI‐QTOF‐MS method may provide a novel and useful platform for metabolomic studies due to its superior separation and identification.