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Identification of the metabolites is an essential step in metabolomics study to interpret the regulatory mechanism of pathological and physiological processes. However, it is still difficult in LC-MSn-based studies because of the complexity of mass spectrometry, chemical diversity of metabolites, and deficiency of standards database. In this work, a comprehensive strategy is developed for accurate and batch metabolite identification in nontargeted metabolomics studies. First, a well-defined procedure was applied to generate reliable and standard LC-MS2 data, including tR, MS1, and MS2 information at a standard operational procedure. An in-house database including about 2000 metabolites was constructed and used to identify the metabolites in nontargeted metabolic profiling by retention time calibration using internal standards, precursor ion alignment and ion fusion, auto-MS2 information extraction and selection, and database batch searching and scoring. As an application example, a pooled serum sample was analyzed to deliver the strategy, and 202 metabolites were identified in the positive ion mode. It shows our strategy is useful for LC-MSn-based nontargeted metabolomics study. [ABSTRACT FROM AUTHOR]

Gut microbiota plays important roles in the host health. The host and symbiotic gut microbiota coproduce a large number of metabolites during the metabolism of food and xenobiotics. The analysis of fecal metabolites can provide a noninvasive manner to study the outcome of the host-gut microbiota interaction. Herein, we reported the comprehensive profiling of fecal metabolome of mice by an integrated chemical isotope labeling combined with liquid chromatography-mass spectrometry (CIL-LC-MS) analysis. The metabolites are categorized into several submetabolomes based on the functional moieties (i.e., carboxyl, carbonyl, amine, and thiol) and then analysis of the individual submetabolome was performed. The combined data from the submetabolome form the metabolome with relatively high coverage. To this end, we synthesized stable isotope labeling reagents to label metabolites with different groups, including carboxyl, carbonyl, amine, and thiol groups. We detected 2302 potential metabolites, among which, 1388 could be positively or putatively identified in feces of mice. We then further confirmed 308 metabolites based on our established library of chemically labeled standards and tandem mass spectrometry analysis. With the identified metabolites in feces of mice, we established mice fecal metabolome database, which can be used to readily identify metabolites from feces of mice. Furthermore, we discovered 211 fecal metabolites exhibited significant difference between Alzheimer's disease (AD) model mice and wild type (WT) mice, which suggests the close correlation between the fecal metabolites and AD pathology and provides new potential biomarkers for the diagnosis of AD. [ABSTRACT FROM AUTHOR]

When using liquid chromatography/mass spectrometry (LC/MS) to perform untargeted metabolomics, it is now routine to detect tens of thousands of features from biological samples. Poor understanding of the data, however, has complicated interpretation and masked the number of unique metabolites actually being measured in an experiment. Here we place an upper bound on the number of unique metabolites detected in Escherichia coli samples analyzed with one untargeted metabolomics method. We first group multiple features arising from the same analyte, which we call "degenerate features", using a context-driven annotation approach. Surprisingly, this analysis revealed thousands of previously unreported degeneracies that reduced the number of unique analytes to ~2961. We then applied an orthogonal approach to remove nonbiological features from the data using the 13C-based credentialing technology. This further reduced the number of unique analytes to less than 1000. Our 90% reduction in data is 5- fold greater than previously published studies. On the basis of the results, we propose an alternative approach to untargeted metabolomics that relies on thoroughly annotated reference data sets. To this end, we introduce the creDBle database (http:// creDBle.wustl.edu), which contains accurate mass, retention time, and MS/MS fragmentation data as well as annotations of all credentialed features. [ABSTRACT FROM AUTHOR]

Quantitative metabolomics approaches can significantly improve the repeatability and reliability of metabolomics investigations but face critical technical challenges, owing to the vast number of unknown endogenous metabolites and the lack of authentic standards. The present study contributes to the development of a novel method known as "data-independent targeted quantitative metabolomics" (DITQM), which was used to investigate the label-free quantitative metabolomics of multiple known and unknown metabolites in biofluid samples. This approach initially involved the acquisition of MS/MS data for all metabolites in biosamples using a sequentially stepped targeted MS/MS (sst-MS/MS) method, in which multiple product ion scans were performed by selecting all ions in the targeted mass ranges as the precursor ions. Subsequently, scheduled multiple reaction monitoring (MRM) by LC-MS/MS of the metabolome was established for 1658 characteristic ion pairs of 1324 metabolites. For sensitive and accurate quantification of these metabolites, mixed calibration curves were generated using sequentially diluted standard reference plasma samples using established MRM methods. Relative concentrations of all metabolites in each sample were calculated without using individual authentic standards. To evaluate the reliability and applicability of this new method, the performance of DITQM was validated by comparison to absolute quantification of 12 acylcarnitines using authentic standards and traditional metabolomics analysis for lung cancer. The results proved that the DITQM protocol is more reliable and can significantly improve clustering effects and repeatability in biomarker discovery. In this study, we established a novel methodology to standardize and quantify large-scale metabolome, providing a new choice for metabolomics research and its clinical applications. [ABSTRACT FROM AUTHOR]

Analyzing mass spectrometry-based metabolomics data presents a major challenge to metabolism researchers, as it requires downloading and processing large data volumes through complex "pipelines", even in cases where only a single metabolite or peak is of interest. This presents a significant hurdle for data sharing, reanalysis, or meta-analysis of existing data sets, whether locally stored or available from public repositories. Here we introduce mzAccess, a software system that provides interactive, online access to primary mass spectrometry data in real-time via a Web service protocol, circumventing the need for bulk data processing. mzAccess allows querying instrument data for spectra, chromatograms, or two-dimensional MZ-RT areas in either profile or centroid modes through a simple, uniform interface that is independent of vendor or instrument type. Using a cache mechanism, mzAccess achieves response times in the millisecond range for typical liquid chromatography--mass spectrometry (LC--MS) peaks, enabling real-time browsing of large data sets with hundreds or even thousands of samples. By simplifying access to metabolite data, we hope that this system will help enable data sharing and reanalysis in the metabolomics field. [ABSTRACT FROM AUTHOR]

Untargeted metabolomics by liquid chromatography-mass spectrometry generates data-rich chromatograms in the form of m/z-retention time features. Managing such datasets is a bottleneck. Many popular data processing tools, including XCMS-online and MZmine2, yield numerous false-positive peak detections. Flagging and removing such false peaks manually is a time-consuming task and prone to human error. We present a web application, Mass Spectral Feature List Optimizer (MS-FLO), to improve the quality of feature lists after initial processing to expedite the process of data curation. The tool utilizes retention time alignments, accurate mass tolerances, Pearson's correlation analysis, and peak height similarity to identify ion adducts, duplicate peak reports, and isotopic features of the main monoisotopic metabolites. Removing such erroneous peaks reduces the overall number of metabolites in data reports and improves the quality of subsequent statistical investigations. To demonstrate the effectiveness of MS-FLO, we processed 28 biological studies and uploaded raw and results data to the Metabolomics Workbench website (www.metabolomicsworkbench.org), encompassing 1481 chromatograms produced by two different data processing programs used in-house (MZmine2 and later MS-DIAL). Post-processing of datasets with MS-FLO yielded a 7.8% automated reduction of total peak features and flagged an additional 7.9% of features, per dataset, for review by the user. When manually curated, 87% of these additional flagged features were verified false positives. MS-FLO is an open source web application that is freely available for use at http://msflo.fiehnlab.ucdavis.edu. [ABSTRACT FROM AUTHOR]

Global urine metabolomics is a rapidly expanding field with the potential to discover biomarkers of disease and exposure. To date, most methods focus on rapid sample preparation, using neat or diluted urine, together with high-throughput analyses, and are poorly suited for detection of low abundance metabolites present in urine samples. In this study, novel methods have been developed to analyze urine by splitless nanoflowUHPLC-nanoESI-TOFMS (nUHPLC-nESI-TOFMS) after preconcentration by solid-phase extraction (SPE), thus enabling significant improvements in analytical sensitivity and coverage of the urinary metabolome. In initial work, urine samples were extracted by both anion and cation exchange mixed-mode polymeric SPE cartridges and qualitatively compared with those using conventional sample preparations using UHPLC-ESI-TOFMS analyses. Compared with neat or diluted urine samples, SPE concentration of urine resulted in detection of additional metabolites including bile acids, lipids, pharmaceuticals, and markers of lifestyle, with little loss of other components of the metabolome. Analyses of SPE preparations by nUHPLC-nESI-TOFMS revealed excellent retention time repeatability with <1% coefficient of variation (CV) for 96% of analyzed peaks. The repeatability of the MS response was <30% CV for >79% of MS features in both negative and positive nESI modes. Compared with UHPLC-ESI-TOFMS, analysis by the nanoplatform enabled detection of signaling molecules important in disease processes including sex steroids, glucocorticoids, eicosanoids, and neurotransmitter metabolites. The significant improvement in sensitivity arising from use of splitless nUHPLC-nESI-TOFMS analyses of SPE-concentrated samples represents a step change in coverage of the urinary metabolome, thereby increasing the potential for biomarker discovery. [ABSTRACT FROM AUTHOR]

Human fecal samples contain endogenous human metabolites, gut microbiota metabolites, and other compounds. Profiling the fecal metabolome can produce metabolic information that may be used not only for disease biomarker discovery, but also for providing an insight about the relationship of the gut microbiome and human health. In this work, we report a chemical isotope labeling liquid chromatography-mass spectrometry (LC–MS) method for comprehensive and quantitative analysis of the amine- and phenol-containing metabolites in fecal samples. Differential 13C2/12C2-dansyl labeling of the amines and phenols was used to improve LC separation efficiency and MS detection sensitivity. Water, methanol, and acetonitrile were examined as an extraction solvent, and a sequential water-acetonitrile extraction method was found to be optimal. A step-gradient LC–UV setup and a fast LC–MS method were evaluated for measuring the total concentration of dansyl labeled metabolites that could be used for normalizing the sample amounts of individual samples for quantitative metabolomics. Knowing the total concentration was also useful for optimizing the sample injection amount into LC-MS to maximize the number of metabolites detectable while avoiding sample overloading. For the first time, dansylation isotope labeling LC–MS was performed in a simple time-of-flight mass spectrometer, instead of high-end equipment, demonstrating the feasibility of using a low-cost instrument for chemical isotope labeling metabolomics. The developed method was applied for profiling the amine/phenol submetabolome of fecal samples collected from three families. An average of 1785 peak pairs or putative metabolites were found from a 30 min LC–MS run. From 243 LC–MS runs of all the fecal samples, a total of 6200 peak pairs were detected. Among them, 67 could be positively identified based on the mass and retention time match to a dansyl standard library, while 581 and 3197 peak pairs could be putatively identified based on mass match using MyCompoundID against a Human Metabolome Database and an Evidence-based Metabolome Library, respectively. This represents the most comprehensive profile of the amine/phenol submetabolome ever detected in human fecal samples. The quantitative metabolome profiles of individual samples were shown to be useful to separate different groups of samples, illustrating the possibility of using this method for fecal metabolomics studies. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel liquid chromatography/mass spectrometry (LC/MS) data processing and analysis platform, MET-COFEA (METabolite COmpound Feature Extraction and Annotation). MET-COFEA detects and dusters chromatographic peak features for each metabolite compound by first comprehensively evaluating retention time and peak shape criteria and then annotating the associations between each peak's observed m/z value with the corresponding metabolite compound's molecular mass. MET-COFEA integrates a series of innovative approaches, including novel mass trace based extracted-ion chromatogram (EIC) extraction, continuous wavelet transform (CWT)-based peak detection, and compound-associated peak clustering and peak annotation algorithms. On the basis of the deduced neutral molecular mass and retention time, we have also developed a new alignment algorithm that uses compound-associated peak groups instead of individual peaks to align the same metabolite compound across samples from different electrospray ionization (ESI) modes, different instruments, even different experimental conditions. MET-COFEA has been systematically tested on a series of LC/MS profiles of mixed standards at different concentrations as well as real untargeted LC/MS plant metabolomics data. We compared the performances of MET-COFEA with the existing publicly available tools at LC/MS peak analysis level and demonstrated its excellent performance in this arena. MET-COFEA is freely available at http://bioinfo.noble.org/manuscript-support/met-cofea/. [ABSTRACT FROM AUTHOR]

Liquid chromatography–mass spectrometry (LC-MS)-based metabolomic datasets consist of different features including (de)protonated molecules, fragments, adducts, and isotopes that may show high correlation values related to a high level of collinearity. There have been described several sources of these high correlation patterns regarding metabolomic datasets. Among these sources, it should be highlighted the high level of correlation computed between features coming from the same metabolite. It is well-known that soft ionization methods (such as electrospray) produce several mass features from a particular compound (i.e., metabolite spectrum). Typically, the statistical methods used in metabolomics consider spectral peaks as variables. However, it has been reported that a high collinearity between variables might be the responsible for high uncertainty values in the predictors of a regression. In this context, this technical note proposes a new strategy based on the application of the so-called peak aggregation methods (NMF Reduction, PCA Decomposition, Maximum Peak, and Spectrum Mean) to take advantage of the variable collinearity and solve the issue of high variable collinearity. A set of real samples obtained after human nutritional intervention with placebo or polyphenol-rich beverages was used to test this methodology. The results showed that applying any peak aggregation method (especially NMF and PCA) improves the statistical prediction power of class pertinence independently of the nature of the classifier (linear PLS-DA or nonlinear SVM). Overall, the introduction of this new approach resulted in a reduction of the dimensionality of the data and, in addition, in a significant increase in the overall predictive power of the data. [ABSTRACT FROM AUTHOR]

Recent progress in metabolomics and the development of increasingly sensitive analytical techniques have renewed interest in global profiling, i.e., semiquantitative monitoring of all chemical constituents of biological fluids. In this work, we have performed global profiling of NIST SRM 1950, "Metabolites in Human Plasma", using GC-MS, LC-MS, and NMR. Metabolome coverage, difficulties, and reproducibility of the experiments on each platform are discussed. A total of 353 metabolites have been identified in this material. GC-MS provides 65 unique identifications, and most of the identifications from NMR overlap with the LC-MS identifications, except for some small sugars that are not directly found by LC-MS. Also, repeatability and intermediate precision analyses show that the SRM 1950 profiling is reproducible enough to consider this material as a good choice to distinguish between analytical and biological variability. Clinical laboratory data shows that most results are within the reference ranges for each assay. In-house computational tools have been developed or modified for MS data processing and interactive web display. All data and programs are freely available online at http://peptidejiist.gov/ and http:// srmdjiist.gov/. [ABSTRACT FROM AUTHOR]

Designer synthetic cannabinoids like JWH-018 and AM2201 have unique clinical toxicity. Cytochrome-P450-mediated metabolism of each leads to the generation of pharmacologically active (ω)- and (ω-1)- monohydroxyl metabolites that retain high affinity for cannabinoid type-1 receptors, exhibit Δ9-THC-like effects in rodents, and are conjugated with glucuronic acid prior to excretion in human urine. Previous studies have not measured the contribution of the specific (ω-1)-monohydroxyl enantiomers in human metabolism and toxicity. This study uses a chiral liquid chromatography-tandem mass spectroscopy approach (LC-MS/MS) to quantify each specific enantiomer and other nonchiral, human metabolites of JWH-018 and AM2201 in human urine. The accuracy (average % RE = 18.6) and reproducibility (average CV = 15.8%) of the method resulted in low-level quantification (average LLQ = 0.99 ng/mL) of each metabolite. Comparisons with a previously validated nonchiral method showed strong correlation between the two approaches (average r2 = 0.89). Pilot data from human urine samples demonstrate enantiospecific excretion patterns. The (S)-isomer of the JWH-018-(ω-1)-monohydroxyl metabolite was predominantly excreted (>87%) in human urine as the glucuronic acid conjugate, whereas the relative abundance of the corresponding AM2201-(ω-1)-metabolite was low (<5%) and did not demonstrate enantiospecificity (approximate 50:50 ratio of each enantiomer). The new chiral method provides a comprehensive, targeted metabolomic approach for studying the human metabolism of JWH-018 and AM2201. Preliminary evaluations of specific enantiomeric contributions support the use of this approach in future studies designed to understand the pharmacokinetic properties of JWH-018 and/or AM2201. [ABSTRACT FROM AUTHOR]