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High-throughput methods to assess radiation exposure are a priority due to concerns that include nuclear power accidents, the spread of nuclear weapon capability, and the risk of terrorist attacks. Metabolomics, the assessment of small molecules in an easily accessible sample, is the most recent method to be applied for the identification of biomarkers of the biological radiation response with a useful dose-response profile. Profiling for biomarker identification is frequently done using an LC-MS platform which has limited throughput due to the time-consuming nature of chromatography. We present here a chromatography-free simplified method for quantitative analysis of seven metabolites in urine with radiation dose-response using urine samples provided from the Pannkuk et al. (2015) study of long-term (7-day) radiation response in nonhuman primates (NHP). The stable isotope dilution (SID) analytical method consists of sample preparation by strong cation exchange-solid phase extraction (SCX-SPE) to remove interferences and concentrate the metabolites of interest, followed by differential mobility spectrometry (DMS) ion filtration to select the ion of interest and reduce chemical background, followed by mass spectrometry (overall SID-SPE-DMS-MS). Since no chromatography is used, calibration curves were prepared rapidly, in under 2 h (including SPE) for six simultaneously analyzed radiation biomarkers. The seventh, creatinine, was measured separately after 2500× dilution. Creatinine plays a dual role, measuring kidney glomerular filtration rate (GFR), and indicating kidney damage at high doses. The current quantitative method using SID-SPE-DMS-MS provides throughput which is 7.5 to 30 times higher than that of LC-MS and provides a path to pre-clinical radiation dose estimation.Graphical Abstract [ABSTRACT FROM AUTHOR]

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]

The goal of metabolomics is to measure the entire range of small organic molecules in biological samples. In liquid chromatography-mass spectrometry-based metab-olomics, formidable analytical challenges remain in removing the nonbiological factors that affect chromatographic peak areas. These factors include sample matrix-induced ion suppression, chromatographic quality, and analytical drift. The combination of these factors is referred to as obscuring variation. Some metabolomics samples can exhibit intense obscuring variation due to matrix-induced ion suppression, rendering large amounts of data unreliable and difficult to interpret. Existing normalization techniques have limited applicability to these sample types. Here we present a data normalization method to minimize the effects of obscuring variation. We normalize peak areas using a batch-specific normalization process, which matches measured metabolites with isotope-labeled internal standards that behave similarly during the analysis. This method, called best-matched internal standard (B-MIS) normalization, can be applied to targeted or untargeted metabolomics data sets and yields relative concentrations. We evaluate and demonstrate the utility of B-MIS normalization using marine environmental samples and laboratory grown cultures of phytoplankton. In untargeted analyses, B-MIS normalization allowed for inclusion of mass features in downstream analyses that would have been considered unreliable without normalization due to obscuring variation. B-MIS normalization for targeted or untargeted metabolomics is freely available at https://github.com/IngallsLabUW/B-MIS-normalization. [ABSTRACT FROM AUTHOR]

Acyl-coenzyme A (CoA) is a pivotal metabolic intermediate in numerous biological processes. However, comprehensive analysis of acyl- CoAs is still challenging as the properties of acyl-CoAs greatly vary with different carbon chains. Here, we designed a two-dimensional liquid chromatography method coupled with high-resolution mass spectrometry (2D LC/HRMS) to cover all short-, medium-, and long-chain acyl-CoAs within one analytical run. Complex acyl-CoAs were separated into two fractions according to their acyl chains by the first dimensional prefractionation. Then, two fractions containing short-chain acyl-CoAs or medium- and long-chain acyl-CoAs were further separated by the two parallel columns in the second dimension. Nineteen representative standards were chosen to optimize the analytical conditions of the 2D LC/HRMS method. Resolution and sensitivity were demonstrated to be improved greatly, and lowly abundant acyl-CoAs and acyl-CoA isomers could be detected and distinguished. By using the 2D LC/HRMS method, 90 acyl-CoAs (including 21 acyl-dephospho-CoAs) were identified from liver extracts, which indicated that our method was one of the most powerful approaches for obtaining comprehensive profiling of acyl-CoAs so far. The method was further employed in the metabolomics study of malignant glioma cells with an isocitrate dehydrogenase 1 (IDH1) mutation to explore their metabolic differences. A total of 46 acyl-CoAs (including 2 acyl-dephospho-CoAs) were detected, and 12 of them were dysregulated in glioma cells with the IDH1 mutation. These results demonstrated the practicability and the superiority of the established method. Therefore, the 2D LC/HRMS method provides a robust and reproducible approach to the comprehensive analysis of acyl-CoAs in tissues, cells, and other biological samples. [ABSTRACT FROM AUTHOR]

Liquid chromatography-mass spectrometry (LC—MS) methods are most often used for untargeted metabolomics and lipidomics. However, methods have not been standardized as accepted “best practice” documents, and reports lack harmonization with respect to quantitative data that enable interstudy comparisons. Researchers use a wide variety of high-resolution mass spectrometers under different operating conditions, and it is unclear if results would yield different biological conclusions depending on the instrument performance. To this end, we used 126 identical human plasma samples and 29 quality control samples from a nutritional intervention study. We investigated lipidomic data acquisitions across nine different MS instruments ( l single TOF, 1 Q/orbital ion trap, and 7 QTOF instruments). Sample preparations, chromatography conditions, and data processing methods were kept identical. Single-point internal standard calibrations were used to estimate absolute concentrations for 307 unique lipids identified by accurate mass, MS/MS spectral match, and retention times. Quantitative results were highly comparable between the LC—MS platforms tested. Using partial least-squares discriminant analysis (PLS-DA) to compare results between platforms, a 92% overlap for the most discriminating lipids based on variable importance in projection (VIP) scores was achieved for all lipids that were detected by at least two instrument platforms. Importantly, even the relative positions of individual samples on the PLS-DA projections were identical. The key for success in harmonizing results was to avoid ion saturation by carefully evaluating linear dynamic ranges using serial dilutions and adjusting the resuspension volume and/or injection volume before running actual study samples. [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]

Considering the physicochemical diversity of the metabolome, untargeted metabolomics will inevitably discriminate against certain compound classes. Efforts are nevertheless made to maximize the metabolome coverage. Contrary to the main steps of a typical liquid chromatography-mass spectrometry (LC-MS) metabolomics workflow, such as metabolite extraction, the sample reconstitution step has not been optimized for maximal metabolome coverage. This sample concentration step typically occurs after metabolite extraction, when dried samples are reconstituted in a solvent for injection on column. The aim of this study was to evaluate the impact of the sample reconstitution solvent composition on metabolome coverage in untargeted LC-MS metabolomics. Lysogeny Broth medium samples reconstituted in MeOH/H2O ratios ranging from 0 to 100% MeOH and analyzed with untargeted reversed phase LC-MS showed that the highest number of metabolite features (n = 1500) was detected in samples reconstituted in 100% H2O. As compared to a commonly used reconstitution solvent mixture of 50/50 MeOH/H2O, our results indicate that the small fraction of compounds increasing in peak area response by the addition of MeOH to H2O, 5%, is outweighed by the fraction of compounds with decreased response, 57%. We evaluated our results on human serum samples from lymphoma patients and healthy control subjects. Reconstitution in 100% H2O resulted in a higher number of significant metabolites discriminating between these two groups than both 50% and 100% MeOH. These findings show that the sample reconstitution step has a clear impact on the metabolome coverage of MeOH extracted biological samples, highlighting the importance of the reconstitution solvent composition for untargeted discovery metabolomics. [ABSTRACT FROM AUTHOR]

Distilling accurate quantitation information on metabolites from liquid chromatography coupled with mass spectrometry (LC-MS) data sets is crucial for further statistical analysis and biomarker identification. However, it is still challenging due to the complexity of biological systems. The concept of pure ion chromatograms (PICs) is an effective way of extracting meaningful ions, but few toolboxes provide a full processing workflow for LC-MS data sets based on PICs. In this study, an integrated framework, KPIC2, has been developed for metabolomics studies, which can detect pure ions accurately, align PICs across samples, group PICs to identify isotope and potential adducts, fill missing peaks and do multivariate pattern recognition. To evaluate its performance, MM48, metabolomics quantitation, and Soybean seeds data sets have been analyzed using KPIC2, XCMS, and MZmine2. KPIC2 can extract more true ions with fewer detecting features, have good quantification ability on a metabolomics quantitation data set, and achieve satisfactory classification on a soybean seeds data set through kernel-based OPLS-DA and random forest. It is implemented in R programming language, and the software, user guide, as well as example scripts and data sets are available as an open source package at https://github.com/hcji/KPIC2. [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 omics analyses aim to comprehensively characterize biomolecules within a biological system. Changes in the presence or quantity of these biomolecules can indicate important biological perturbations, such as those caused by disease. With current technological advancements, the entire genome can now be sequenced; however, in the burgeoning fields of lipidomics, only a subset of lipids can be identified. The recent emergence of high resolution tandem mass spectrometry (HR-MS/MS), in combination with ultra-high performance liquid chromatography, has resulted in an increased coverage of the lipidome. Nevertheless, identifications from MS/MS are generally limited by the number of precursors that can be selected for fragmentation during chromatographic elution. Therefore, we developed the software IE-Omics to automate iterative exclusion (IE), where selected precursors using data-dependent topN analyses are excluded in sequential injections. In each sequential injection, unique precursors are fragmented until HR-MS/MS spectra of all ions above a user-defined intensity threshold are acquired. IE-Omics was applied to lipidomic analyses in Red Cross plasma and substantia nigra tissue. Coverage of the lipidome was drastically improved using IE. When applying IE-Omics to Red Cross plasma and substantia nigra lipid extracts in positive ion mode, 69% and 40% more molecular identifications were obtained, respectively. In addition, applying IE-Omics to a lipidomics workflow increased the coverage of trace species, including odd-chained and short-chained diacylglycerides and oxidized lipid species. By increasing the coverage of the lipidome, applying IE to a lipidomics workflow increases the probability of finding biomarkers and provides additional information for determining etiology of disease. [ABSTRACT FROM AUTHOR]

Milk is a complex sample containing a variety of proteins, lipids, and metabolites. Studying the milk metabolome represents an important application of metabolomics in the general area of nutritional research. However, comprehensive and quantitative analysis of milk metabolites is a challenging task due to the wide range of variations in chemical/physical properties and concentrations of these metabolites. We report an analytical workflow for in-depth profiling of the milk metabolome based on chemical isotope labeling (CIL) and liquid chromatography mass spectrometry (LC-MS) with a focus of using dansylation labeling to target the amine/phenol submetabolome. An optimal sample preparation method, including the use of methanol at a 3:1 ratio of solvent to milk for protein precipitation and dichloromethane for lipid removal, was developed to detect and quantify as many metabolites as possible. This workflow was found to be generally applicable to profile milk metabolomes of different species (cow, goat, and human) and types. Results from experimental replicate analysis (n = 5) of 1:1, 2:1, and 1:2 12C-/13C-labeled cow milk samples showed that 95.7%, 94.3%, and 93.2% of peak pairs, respectively, had ratio values within ±50% accuracy range and 90.7%, 92.6%, and 90.8% peak pairs had RSD values of less than 20%. In the metabolomic analysis of 36 samples from different categories of cow milk (brands, batches, and fat percentages) with experimental triplicates, a total of 7104 peak pairs or metabolites could be detected with an average of 4573 ± 505 (n = 108) pairs detected per LC-MS run. Among them, 3820 peak pairs were commonly detected in over 80% of the samples with 70 metabolites positively identified by mass and retention time matches to the dansyl standard library and 2988 pairs with their masses matched to the human metabolome libraries. This unprecedentedly high coverage of the amine/phenol submetabolome illustrates the complexity of the milk metabolome. Since milk and milk products are consumed in large quantities on a daily basis, the intake of these milk metabolites even at low concentrations can be cumulatively high. The high-coverage analysis of the milk metabolome using CIL LC-MS should be very useful in future research involving the study of the effects of these metabolites on human health. It should also be useful in the dairy industry in areas such as improving milk production, developing new processing technologies, developing improved nutritional products, quality control, and milk product authentication. [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]

In recent years, the number of investigations based on nontargeted metabolomics has increased, although often without a thorough assessment of analytical strategies applied to acquire data. Following published guidelines for metabolomics experiments, we report a validated nontargeted metabolomics strategy with pipeline for unequivocal identification of metabolites using the MSMLS molecule library. We achieved an in-house database containing accurate m/z values, retention times, isotopic patterns, full MS, and MS/MS spectra. A UHPLC-HRMS Q-Exactive method was developed, and experimental variations were determined within and between 3 experimental days. The extraction efficiency as well as the accuracy, precision, repeatability, and linearity of the method were assessed, the method demonstrating good performances. The methodology was further blindly applied to plasma from remote ischemic pre-conditioning (RIPC) rats. Samples, previously analyzed by targeted metabolomics using completely different protocol, analytical strategy, and platform, were submitted to our analytical pipeline. A combination of multivariate and univariate statistical analyses was employed. Selection of putative biomarkers from OPLS-DA model and S-plot was combined to jack-knife confidence intervals, metabolites' VIP values, and univariate statistics. Only variables with strong model contribution and highly statistical reliability were selected as discriminated metabolites. Three biomarkers identified by the previous targeted metabolomics study were found in the current work, in addition to three novel metabolites, emphasizing the efficiency of the current methodology and its ability to identify new biomarkers of clinical interest, in a single sequence. The biomarkers were identified to level 1 according to the metabolomics standard initiative and confirmed by both RPLC and HILIC-HRMS. [ABSTRACT FROM AUTHOR]

Untargeted liquid-chromatography-mass spectrometry (LC-MS)-based metabolomics analysis of human biospecimens has become among the most promising strategies for probing the underpinnings of human health and disease. Analysis of spectral data across population scale cohorts, however, is precluded by day-to-day nonlinear signal drifts in LC retention time or batch effects that complicate comparison of thousands of untargeted peaks. To date, there exists no efficient means of visualization and quantitative assessment of signal drift, correction of drift when present, and automated filtering of unstable spectral features, particularly across thousands of data files in population scale experiments. Herein, we report the development of a set of R-based scripts that allow for pre- and postprocessing of raw LC-MS data. These methods can be integrated with existing data analysis workflows by providing initial preprocessing bulk nonlinear retention time correction at the raw data level. Further, this approach provides postprocessing visualization and quantification of peak alignment accuracy, as well as peak-reliability-based parsing of processed data through hierarchical clustering of signal profiles. In a metabolomics data set derived from ~3000 human plasma samples, we find that application of our alignment tools resulted in substantial improvement in peak alignment accuracy, automated data filtering, and ultimately statistical power for detection of metabolite correlates of clinical measures. These tools will enable metabolomics studies of population scale cohorts. [ABSTRACT FROM AUTHOR]

Improved analytical technologies and data extraction algorithms enable detection of >10,000 reproducible signals by liquid chromatography-high-resolution mass spectrometry, creating a bottleneck in chemical identification. In principle, measurement of more than one million chemicals would be possible if algorithms were available to facilitate utilization of the raw mass spectrometry data, especially low-abundance metabolites. Here we describe an automated computational framework to annotate ions for possible chemical identity using a multistage clustering algorithm in which metabolic pathway associations are used along with intensity profiles, retention time characteristics, mass defect, and isotope/adduct patterns. The algorithm uses high-resolution mass spectrometry data for a series of samples with common properties and publicly available chemical, metabolic, and environmental databases to assign confidence levels to annotation results. Evaluation results show that the algorithm achieves an F1-measure of 0.8 for a data set with known targets and is more robust than previously reported results for cases when database size is much greater than the actual number of metabolites. MS/MS evaluation of a set of randomly selected 210 metabolites annotated using xMSannotator in an untargeted metabolomics human data set shows that 80% of features with high or medium confidence scores have ion dissociation patterns consistent with the xMSannotator annotation. The algorithm has been incorporated into an R package, xMSannotator, which includes utilities for querying local or online databases such as ChemSpider, KEGG, HMDB, T3DB, and LipidMaps. [ABSTRACT FROM AUTHOR]

A key step in metabolomics is to perform accurate relative quantification of the metabolomes in comparative samples with high coverage. Hydroxyl-containing metabolites are an important class of the metabolome with diverse structures and physical/chemical properties; however, many of them are difficult to detect with high sensitivity. We present a high-performance chemical isotope labeling liquid chromatography mass spectrometry (LC-MS) technique for in-depth profiling of the hydroxyl submetabolome, which involves the use of acidic liquid--liquid extraction to enrich hydroxyl metabolites into ethyl acetate from an aqueous sample. After drying and then redissolving in acetonitrile, the metabolite extract is labeled using a base-activated 12C- or I3Cdansylation reaction. A fast step-gradient LC-UV method is used to determine the total concentration of labeled metabolites. On the basis of the concentration information, a 12C-labeled individual sample is mixed with an equal mole amount of a 13C-labeled pool or control for relative metabolite quantification. The 12C-/13C-labeled mixtures are individually analyzed by LC-MS, and the resultant peak pairs of labeled metabolites in MS are measured for relative quantification and metabolite identification. A standard library of 85 hydroxyl compounds containing MS, retention time, and MS/MS information was constructed for positive metabolite identification based on matches of two or all three of these parameters with those of an unknown. Using human urine as an example, we analyzed samples of 1:1 12C-/13C-labeled urine in triplicate with triplicate runs per sample and detected an average of 3759 ± 45 peak pairs or metabolites per run and 3538 ±71 pairs per sample with 3093 pairs in common (n = 9). Out of the 3093 peak pairs, 2304 pairs (75%) could be positively or putatively identified based on metabolome database searches, including 20 pairs positively identified using the dansylated hydroxyl standards library. The majority of detected metabolites were those containing hydroxyl groups. This technique opens a new avenue for the detailed characterization of the hydroxyl submetabolome in metabolomics research. [ABSTRACT FROM AUTHOR]

Liquid chromatography coupled to electrospray ionization-mass spectrometry (LC-ESI-MS) is a versatile and robust platform for metabolomic analysis. However, while ESI is a soft ionization technique, in-source phenomena including multimerization, nonproton cation adduction, and in-source fragmentation complicate interpretation of MS data. Here, we report chromatographic and mass spectrometric behavior of 904 authentic standards collected under conditions identical to a typical nontargeted profiling experiment. The data illustrate that the often high level of complexity in MS spectra is likely to result in misinterpretation during the annotation phase of the experiment and a large overestimation of the number of compounds detected. However, our analysis of this MS spectral library data indicates that in-source phenomena are not random but depend at least in part on chemical structure. These nonrandom patterns enabled predictions to be made as to which in-source signals are likely to be observed for a given compound. Using the authentic standard spectra as a training set, we modeled the in-source phenomena for all compounds in the Human Metabolome Database to generate a theoretical in-source spectrum and retention time library. A novel spectral similarity matching platform was developed to facilitate efficient spectral searching for nontargeted profiling applications. Taken together, this collection of experimental spectral data, predictive modeling, and informatic tools enables more efficient, reliable, and transparent metabolite annotation. [ABSTRACT FROM AUTHOR]

Human sweat can be noninvasively collected and used as a media for diagnosis of certain diseases as well as for drug detection. However, because of very low concentrations of endogenous metabolites present in sweat, metabolomic analysis of sweat with high coverage is difficult, making it less widely used for metabolomics research. In this work, a high-performance method for profiling the human sweat submetabolome based on chemical isotope labeling (CIL) liquid chromatography-mass spectrometry (LC-MS) is reported. Sweat was collected using a gauze sponge style patch, extracted from the gauze by centrifugation, and then derivatized using CIL. Differential 12C- and 13C-dansylation labeling was used to target the amine/phenol submetabolome. Because of large variations in the total amount of sweat metabolites in individual samples, sample amount normalization was first performed using liquid chromatography with UV detection (LC-UV) after dansylation. The 12C-labeled individual sample was then mixed with an equal amount of 13C-labeled pooled sample. The mixture was subjected to LC-MS analysis. Over 2707 unique metabolites were detected across 54 sweat samples collected from six individuals with an average of 2002 ± 165 metabolites detected per sample from a total of 108 LC-MS runs. Using a dansyl standard library, we were able to identify 83 metabolites with high confidence; many of them have never been reported to be present in sweat. Using accurate mass search against human metabolome libraries, we putatively identified an additional 2411 metabolites. Uni- and multivariate analyses of these metabolites showed significant differences in the sweat submetabolomes between male and female, as well as between early and late exercise. These results demonstrate that the CIL LC-MS method described can be used to profile the human sweat submetabolome with high metabolomic coverage and high quantification accuracy to reveal metabolic differences in different sweat samples, thereby allowing the use of sweat as another human biofluid for comprehensive and quantitative metabolomics research. [ABSTRACT FROM AUTHOR]

Isotopic ratio outlier analysis (IROA) is a 13C metabolomics profiling method that eliminates sample to sample variance, discriminates against noise and artifacts, and improves identification of compounds, previously done with accurate mass liquid chromatography/mass spectrometry (LC/MS). This is the first report using IROA technology in combination with accurate mass gas chromatography/time-of-flight mass spectrometry (GC/TOF-MS), here used to examine the S. cerevisiae metabolome. S. cerevisiae was grown in YNB media, containing randomized 95% 13C, or 5%13C glucose as the single carbon source, in order that the isotopomer pattern of all metabolites would mirror the labeled glucose. When these IROA experiments are combined, the abundance of the heavy isotopologues in the 5%13C extracts, or light isotopologues in the 95%13C extracts, follows the binomial distribution, showing mirrored peak pairs for the molecular ion. The mass difference between the 12C monoisotopic and the 13C monoisotopic equals the number of carbons in the molecules. The IROA-GC/MS protocol developed, using both chemical and electron ionization, extends the information acquired from the isotopic peak patterns for formulas generation. The process that can be formulated as an algorithm, in which the number of carbons, as well as the number of methoximations and silylations are used as search constraints. In electron impact (EI/IROA) spectra, the artifactual peaks are identified and easily removed, which has the potential to generate "clean" EI libraries. The combination of chemical ionization (CI) IROA and EI/IROA affords a metabolite identification procedure that enables the identification of coeluting metabolites, and allowed us to characterize 126 metabolites in the current study. [ABSTRACT FROM AUTHOR]

High-performance chemical isotope labeling (CIL) liquid chromatography--mass spectrometry (LC--MS) is an enabling technology based on rational design of labeling reagents to target a class of metabolites sharing the same functional group (e.g., all the amine-containing metabolites or the amine submetabolome) to provide concomitant improvements in metabolite separation, detection, and quantification. However, identification of labeled metabolites remains to be an analytical challenge. In this work, we describe a library of labeled standards and a search method for metabolite identification in CIL LC--MS. The current library consists of 273 unique metabolites, mainly amines and phenols that are individually labeled by dansylation (Dns). Some of them produced more than one Dns-derivative (isomers or multiple labeled products), resulting in a total of 315 dansyl compounds in the library. These metabolites cover 42 metabolic pathways, allowing the possibility of probing their changes in metabolomics studies. Each labeled metabolite contains three searchable parameters: molecular ion mass, MS/MS spectrum, and retention time (RT). To overcome RT variations caused by experimental conditions used, we have developed a calibration method to normalize RTs of labeled metabolites using a mixture of RT calibrants. A search program, DnsID, has been developed in www.MyCompoundID.org for automated identification of dansyl labeled metabolites in a sample based on matching one or more of the three parameters with those of the library standards. Using human urine as an example, we illustrate the workflow and analytical performance of this method for metabolite identification. This freely accessible resource is expandable by adding more amine and phenol standards in the future. In addition, the same strategy should be applicable for developing other labeled standards libraries to cover different classes of metabolites for comprehensive metabolomics using CIL LC--MS. [ABSTRACT FROM AUTHOR]

Liquid chromatography/mass spectrometry (LC/MS) metabolite profiling has been widely used in comparative metabolomics studies; however, LC/MS-based comparative metabolomics currently faces several critical challenges. One of the greatest challenges is how to effectively align metabolites across different LC/MS profiles; a single metabolite can give rise to multiple peak features, and the grouped peak features that can be used to construct a spectrum pattern of single metabolite can vary greatly between biochemical experiments and even between instrument runs. Another major challenge is that the observed retention time for a single metabolite can also be significantly affected by experimental conditions. To overcome these two key challenges, we present a novel metabolite-based alignment approach entitled MET-XAlign to align metabolites across LC/MS metabolomics profiles. MET-XAlign takes the deduced molecular mass and estimated compound retention time information that can be extracted by our previously published tool, MET-COFEA, and aligns metabolites based on this information. We demonstrate that MET-XAlign is able to cross-align metabolite compounds, either known or unknown, in LC/MS profiles not only across different samples but also across different biological experiments and different electrospray ionization modes. Therefore, our proposed metabolite-based cross-alignment approach is a great step forward and its implementation, MET-XAlign, is a very useful tool in LC/MS-based comparative metabolomics. MET-XAlign has been successfully implemented with core algorithm coding in C++, making it very efficient, and visualization interface coding in the Microsoft.NET Framework. [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]

Modifications of genes and proteins have been extensively studied in systems biology using comprehensive analytical strategies. Although metabolites are frequently modified, these modifications have not been studied using -omics approaches. Here a general strategy for the nontargeted profiling of modified metabolites, which we call "nontargeted modification-specific metabolomics", is reported. A key aspect of this strategy was the combination of in-source collision-induced dissociation liquid chromatography- mass spectrometry (LC-MS) and global nontargeted LC-MS-based metabolomics. Characteristic neutral loss fragments that are specific for acetylation, sulfation, glucuronidation, glucosidation, or ribose conjugation were reproducibly detected using human urine as a model specimen for method development. The practical application of this method was demonstrated by profiling urine samples from liver cirrhosis patients. Approximately 900 features were identified as modified endogenous metabolites and xenobiotics. Moreover, this strategy supports the identification of compounds not included in traditional metabolomics databases (HMDB, Metlin, and KEGG), which are currently referred to as "unknowns" in metabolomics projects. Nontargeted modification-specific metabolomics opens a new perspective in systems biology. [ABSTRACT FROM AUTHOR]

Liquid chromatography coupled to tandem mass spectrometry is one of the most widely used analytical platforms for profiling analysis in metabolomics. One weakness of untargeted metabolomic analysis, however, is the difficulty of identifying metabolites. In fact, the process typically involves mass-based searching of LC-MS and LC-MS/MS data and requires using MS/MS data for unequivocal identification. Current strategies use LC-MS analysis in the scan mode prior to acquiring MS/MS information about targeted metabolites or the "auto MS/MS" mode to fragment automatically the most intense precursor ions. Therefore, in both cases additional injections are required to obtain MS/MS data after data treatment to identify significant compounds whose signals are not so intense. Because an additional procedure is needed to enhance the fraction of metabolites with MS/MS data, in this work, the effectiveness of utilizing different MS/MS parameters across an analytical batch or repetitions of the same sample by using exclusion or inclusion criteria to select precursor ions is assessed. The procedure, known as "gas-phase fractionation (GPF)", was used here for untargeted analysis of serum. The joint use of four methods with a different mass range for selection of precursor ions each provided useful MS/MS information for at least 80% of all molecular entities detected in the MS scan replicates. By contrast, the conventional "auto MS/MS" mode of data acquisition provided MS/MS data for only 48-57% of entities and was therefore less effective toward identifying metabolites. The additional use of GPF improved the detection and annotation of metabolite families such as phospholipids, amino acids, bile acids, carnitines, and fatty acids and their derivatives. [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]

Quantitative metabolomics and proteomics technologies are powerful approaches to explore cellular metabolic regulation. Unfortunately, combining the two technologies typically requires different LC-MS setups for sensitive measurement of metabolites and peptides. One approach to enhance the analysis of certain classes of metabolites is by derivatization with various types of tags to increase ionization and chromatographic efficiency. We demonstrate here that derivatization of amine metabolites with tandem mass tags (TMT), typically used in multiplexed peptide quantitation, facilitates amino acid analysis by standard nanoflow reversed-phase LC-MS setups used for proteomics. We demonstrate that this approach offers the potential to perform experiments at the MSl-level using duplex tags or at the MS2-Ievel using novel 10-plex reporter ion-containing isobaric tags for multiplexed amine metabolite analysis. We also demonstrate absolute quantitative measurements of amino acids conducted in parallel with multiplexed quantitative proteomics, using similar LC-MS setups to explore cellular amino acid regulation. We further show that the approach can also be used to determine intracellular metabolic labeling of amino acids from glucose carbons. [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]

Studies of isotopically labeled compounds have been fundamental to understanding metabolic pathways and fluxes. They have traditionally, however, been used in conjunction with targeted analyses that identify and quantify a limited number of labeled downstream metabolites. Here we describe an alternative workflow that leverages recent advances in untargeted metabolomic technologies to track the fates of isotopically labeled metabolites in a global, unbiased manner. This untargeted approach can be applied to discover novel biochemical pathways and characterize changes in the fates of labeled metabolites as a function of altered biological conditions such as disease. To facilitate the data analysis, we introduce X13CMS, an extension of the widely used mass spectrometry-based metabolomic software package XCMS. X13CMS uses the XCMS platform to detect metabolite peaks and perform retention-time alignment in liquid chromatography/mass spectrometry (LC/MS) data. With the use of the XCMS output, the program then identifies isotopologue groups that correspond to isotopically labeled compounds. The retrieval of these groups is done without any a priori knowledge besides the following input parameters: (i) the mass difference between the unlabeled and labeled isotopes, (ii) the mass accuracy of the instrument used in the analysis, and (iii) the estimated retention-time reproducibility of the chromatographic method. Despite its name, X13CMS can be used to track any isotopic label. Additionally, it detects differential labeling patterns in biological samples collected from parallel control and experimental conditions. We validated the ability of X13CMS to accurately retrieve labeled metabolites from complex biological matrices both with targeted LC/MS/MS analysis of a subset of the hits identified by the program and with labeled standards spiked into cell extracts. We demonstrate the full functionality of X13CMS with an analysis of cultured rat astrocytes treated with uniformly labeled (U-)13C-glucose during lipopolysaccharide (LPS) challenge. Our results show that out of 223 isotopologue groups enriched from U-13C-glucose, 95 have statistically significant differential labeling patterns in astrocytes challenged with LPS compared to unchallenged control cells. Only two of these groups overlap with the 32 differentially regulated peaks identified by XCMS, indicating that X13CMS uncovers different and complementary information from untargeted metabolomic studies. Like XCMS, X13CMS is implemented in R. It is available from our laboratory website at http://pattilab.wustl.edu/x13cms.php. [ABSTRACT FROM AUTHOR]

Metabolomics, the global study of small molecules in a particular system, has in the past few years risen to become a primary -omics platform for the study of metabolic processes. With the ever-increasing pool of quantitative data yielded from metabolomic research, specialized methods and tools with which to analyze and extract meaningful conclusions from these data are becoming more and more crucial. Furthermore, the depth of knowledge and expertise required to undertake a metabolomics oriented study is a daunting obstacle to investigators new to the field. As such, we have created a new statistical analysis workflow, MetaboLyzer, which aims to both simplify analysis for investigators new to metabolomics, as well as provide experienced investigators the flexibility to conduct sophisticated analysis. MetaboLyzer's workflow is specifically tailored to the unique characteristics and idiosyncrasies of postprocessed liquid chromatography-mass spectrometry (LC-MS)-based metabolomic data sets. It utilizes a wide gamut of statistical tests, procedures, and methodologies that belong to classical biostatistics, as well as several novel statistical techniques that we have developed specifically for metabolomics data. Furthermore, MetaboLyzer conducts rapid putative ion identification and putative biologically relevant analysis via incorporation of four major small molecule databases: KEGG, HMDB, Lipid Maps, and BioCyc. MetaboLyzer incorporates these aspects into a comprehensive workflow that outputs easy to understand statistically significant and potentially biologically relevant information in the form of heatmaps, volcano plots, 3D visualization plots, correlation maps, and metabolic pathway hit histograms. For demonstration purposes, a urine metabolomics data set from a previously reported radiobiology study in which samples were collected from mice exposed to γ radiation was analyzed. MetaboLyzer was able to identify 243 statistically significant ions out of a total of 1942. Numerous putative metabolites and pathways were found to be biologically significant from the putative ion identification workflow. [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]

Untargeted analysis performed using full-scan mass spectrometry (MS) coupled with liquid chromatography (LC) is commonly used in metabolomics. Although they are commonly employed, full-scan MS methods such as quadrupole-time-of-flight (Q-TOF) MS have been restricted by various factors including their limited linear range and complicated data processing. LC coupled with triple quadrupole (QQQ) MS operated in the multiple reaction monitoring (MRM) mode is the gold standard for metabolite quantification; however, only known metabolites are generally quantified, limiting its applications in metabolomic analysis. In this study, a pseudotargeted approach was proposed to perform serum metabolomic analysis using an ultra high-performance liquid chromatography (UHPLC)/QQQ MS system operated in the MRM mode, for which the MRM ion pairs were acquired from the serum samples through untargeted tandem MS using UHPLC/Q-TOF MS. The UHPLC/QQQ MRM MS-based pseudotargeted method displayed better repeatability and wider linear range than the traditional UHPLC/Q-TOF MS-based untargeted metabolomics method, and no complicated peak alignment was required. The developed method was applied to discover serum biomarkers for patients with hepatocellular carcinoma (HCC). Patients with HCC had decreased lysophosphatidylcholine, increased long-chain and decreased medium-chain acylcarnitines, and increased aromatic and decreased branched-chain amino acid levels compared to healthy controls. The novelty of this work is that it provides an approach to acquire MRM ion pairs from real samples, is not limited to metabolite standards, and it provides a foundation to achieve pseudotargeted metabolomic analysis on the widely used LC/QQQ MS platform. [ABSTRACT FROM AUTHOR]

It is essential to choose one preprocessing method for liquid chromatography--mass spectrometry (LC-MS)-based metabolomics studies of urine samples in order to overcome their variability. However, the commonly used normalization methods do not substantially reduce the high variabilities arising from differences in urine concentration, especially for signal saturation (abundant metabolites exceed the dynamic range of the instrumentation) or missing values. Herein, a simple preacquisition strategy based on differential injection volumes calibrated by creatinine (to reduce the concentration differences between the samples), combined with normalization to "total useful MS signals" or "all MS signals", is proposed to overcome urine variabilities. This strategy was first systematically compared with other popular normalization methods by application to serially diluted urine samples. Then, the method has been verified using rat urine samples of pre- and postinoculation of Walker 256 carcinoma cells. The results showed that the calibration of injection volumes based on creatinine values could effectively eliminate intragroup differences caused by variations in the concentrations of urinary metabolites, thus giving better parallelism and clustering effects. In addition, peak area normalization could further eliminate intraclass differences. Therefore, the strategy of combining peak area normalization with calibration of injection volumes of urine samples based on their creatinine values is effective for solving problems associated with urinary metabolomics. [ABSTRACT FROM AUTHOR]

Metabolomics is the study of metabolic changes in biological systems and provides the small molecule fingerprints related to the disease. Extracting biomedical information from large metabolomics data sets by multivariate data analysis is of considerable complexity. Therefore, more efficient and optimizing metabolomics data processing technologies are needed to improve mass spectrometry applications in biomarker discovery. Here, we report the findings of urine metabolomic investigation of hepatitis C virus (HCV) patients by high-throughput ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) coupled with pattern recognition methods (principal component analysis, partial least-squares, and OPLS-DA) and network pharmacology. A total of 20 urinary differential metabolites (13 upregulated and 7 downregulated) were identified and contributed to HCV progress, involve several key metabolic pathways such as taurine and hypotaurine metabolism, glycine, serine and threonine metabolism, histidine metabolism, arginine and proline metabolism, and so forth. Metabolites identified through metabolic profiling may facilitate the development of more accurate marker algorithms to better monitor disease progression. Network analysis validated close contact between these metabolites and implied the importance of the metabolic pathways. Mapping altered metabolites to KEGG pathways identified alterations in a variety of biological processes mediated through complex networks. These findings may be promising to yield a valuable and noninvasive tool that insights into the pathophysiology of HCV and to advance the early diagnosis and monitor the progression of disease. Overall, this investigation illustrates the power of the UPLC-MS platform combined with the pattern recognition and network analysis methods that can engender new insights into HCV pathobiology. [ABSTRACT FROM AUTHOR]

We describe a time-saving protocol for the processing of LC-MS-based metabolomics data by optimizing parameter settings in XCMS and threshold settings for removing noisy and low-intensity peaks using design of experiment (DoE) approaches including Plackett-Burman design (PBD) for screening and central composite design (CCD) for optimization. A reliability index, which is based on evaluation of the linear response to a dilution series, was used as a parameter for the assessment of data quality. After identifying the significant parameters in the XCMS software by PBD, CCD was applied to determine their values by maximizing the reliability and group indexes. Optimal settings by DoE resulted in improvements of 19.4% and 54.7% in the reliability index for a standard mixture and human urine, respectively, as compared with the default setting, and a total of 38 h was required to complete the optimization. Moreover, threshold settings were optimized by using CCD for further improvement. The approach combining optimal parameter setting and the threshold method improved the reliability index about 9.5 times for a standards mixture and 14.5 times for human urine data, which required a total of 41 h. Validation results also showed improvements in the reliability index of about 5-7 times even for urine samples from different subjects. It is concluded that the proposed methodology can be used as a time-saving approach for improving the processing of LC-MS-based metabolomics data. [ABSTRACT FROM AUTHOR]

Although the objective of any 'omic science is broad measurement of its constituents, such coverage has been challenging in metabolomics because the metabolome is comprised of a chemically diverse set of small molecules with variable physical properties. While extensive studies have been performed to identify metabolite isolation and separation methods, these strategies introduce bias toward lipophilic or water-soluble metabolites depending on whether reversed-phase (RP) or hydrophilic interaction liquid chromatography (HILIC) is used, respectively. Here we extend our consideration of metabolome isolation and separation procedures to integrate RPLC/MS and HILIC/MS profiling. An aminopropyl-based HILIC/MS method was optimized on the basis of mobile-phase additives and pH, followed by evaluation of reproducibility. When applied to the untargeted study of perturbed bacterial metabolomes, the HILIC method enabled the accurate assessment of key, dysregulated metabolites in central carbon pathways (e.g., amino acids, organic acids, phosphorylated sugars, energy currency metabolites), which could not be retained by RPLC. To demonstrate the value of the integrative approach, bacterial cells, human plasma, and cancer cells were analyzed by combined RPLC/HILIC separation coupled to ESI positive/negative MS detection. The combined approach resulted in the observation of metabolites associated with lipid and central carbon metabolism from a single biological extract, using 80% organic solvent (ACN:MeOH:H2O 2:2:1). It enabled the detection of more than 30,000 features from each sample type, with the highest number of uniquely detected features by RPLC in ESI positive mode and by HILIC in ESI negative mode. Therefore, we conclude that when time and sample are limited, the maximum amount of biological information related to lipid and central carbon metabolism can be acquired by combining RPLC ESI positive and HILIC ESI negative mode analysis. [ABSTRACT FROM AUTHOR]

Quantitative and comprehensive profiling of cellular metabolites is currently a challenging task in bacterial metabolomics. In this work, a simple and robust method for profiling the amine- and phenol-containing metabolome of bacterial cells is described. The overall workflow consists of methanol-based cell lysis and metabolite extraction with ultrasonication, differential isotope dansylation labeling of cellular metabolites, and analysis of the labeled metabolites by liquid chromatography-mass spectrometry (LC-MS). Over a thousand peak pairs or putative metabolites can be detected from bacterial cells in a 25 min LC-MS run and near 2500 putative metabolites can be found in one bacterium from combined results of multiple analyses. After careful examination and optimization of the sample preparation process, this method is shown to be effective for both Gram-positive and Gram-negative bacteria. An idea of applying LC-ultraviolet (UV) detection to quantify the total amount of labeled metabolites is shown to be effective for normalizing the amounts of metabolites present in different samples for metabolome comparison. The use of differential isotopic labeling allows relative quantification of each individual metabolite, which facilitates comparative metabolomics studies and the generation of a metabolic fingerprint of a bacterium. Finally, this method is demonstrated to be useful for the differentiation of three bacterial species in cultured media and spiked human urine samples. [ABSTRACT FROM AUTHOR]

Identification of unknown metabolites is a major challenge in metabolomics. Without the identities of the metabolites, the metabolome data generated from a biological sample cannot be readily linked with the proteomic and genomic information for studies in systems biology and medicine. We have developed a web-based metabolite identification tool (http://www.mycompoundid.org) that allows searching and interpreting mass spectrometry (MS) data against a newly constructed metabolome library composed of 8?021 known human endogenous metabolites and their predicted metabolic products (375?809 compounds from one metabolic reaction and 10?583?901 from two reactions). As an example, in the analysis of a simple extract of human urine or plasma and the whole human urine by liquid chromatography-mass spectrometry and MS/MS, we are able to identify at least two times more metabolites in these samples than by using a standard human metabolome library. In addition, it is shown that the evidence-based metabolome library (EML) provides a much superior performance in identifying putative metabolites from a human urine sample, compared to the use of the ChemPub and KEGG libraries. [ABSTRACT FROM AUTHOR]

A study combining high resolution mass spectrometry (liquid chromatography-quadrupole time-of-flight-mass spectrometry, UPLC-QTof-MS) and chemometrics for the analysis of post-mortem brain tissue from subjects with Alzheimer's disease (AD) (n = 15) and healthy age-matched controls (n = 15) was undertaken. The huge potential of this metabolomics approach for distinguishing AD cases is underlined by the correct prediction of disease status in 94-97% of cases. Predictive power was confirmed in a blind test set of 60 samples, reaching 100% diagnostic accuracy. The approach also indicated compounds significantly altered in concentration following the onset of human AD. Using orthogonal partial least-squares discriminant analysis (OPLS-DA), a multivariate model was created for both modes of acquisition explaining the maximum amount of variation between sample groups (Positive Mode-R2 = 97%; Q2 = 93%; root mean squared error of validation (RMSEV) = 13%; Negative Mode-R2 = 99%; Q2 = 92%; RMSEV = 15%). In brain extracts, 1264 and 1457 ions of interest were detected for the different modes of acquisition (positive and negative, respectively). Incorporation of gender into the model increased predictive accuracy and decreased RMSEV values. High resolution UPLC-QTof-MS has not previously been employed to biochemically profile post-mortem brain tissue, and the novel methods described and validated herein prove its potential for making new discoveries related to the etiology, pathophysiology, and treatment of degenerative brain disorders. [ABSTRACT FROM AUTHOR]

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is widely used for characterizing multiple samples of complex mixtures with similar compositions. This article addresses a data acquisition strategy for collecting a maximal number of unique, high-quality MS/MS during LC-MS/MS analysis of multiple samples. Based on the concept that a component only needs to be identified once when analyzing multiple samples with similar compositions, an automated intersample data-dependent acquisition strategy was developed. The strategy is based on precursor ion exclusion (PIE) and is implemented in MassAnalyzer in an automated fashion for Thermo Scientific (San Jose, CA, USA) mass spectrometers. In this method, MassAnalyzer submits one sample at a time to the sample queue. After data acquisition of each sample, MassAnalyzer automatically analyzes the data to generate a PIE list based on the MS/MS precursor ions, merges this list with the list generated from previous runs, adds the list to the MS method file, and submits the next sample to the queue. The PIE list contains both m/z value and time window for each precursor ion, and is generated intelligently so that if an MS/MS is insufficient for identifying the peak of interest, it will be collected again near the top of the peak in the next run. Therefore, the strategy maximizes both quality and the number of unique MS/MS. When automated PIE was used to acquire LC-MS/MS data of an antibody tryptic digest and a soy hydrolysate sample, the number of identified ions increased by 52 % and 93 %, respectively, compared with data acquired without using PIE. [ABSTRACT FROM AUTHOR]