MSClust: a tool for unsupervised mass spectra extraction of chromatography-mass spectrometry ion-wise aligned data

Mass peak alignment (ion-wise alignment) has recently become a popular method for unsupervised data analysis in untargeted metabolic profiling. Here we present MSClust-a software tool for analysis GC-MS and LC-MS datasets derived from untargeted profiling. MSClust performs data reduction using unsupervised clustering and extraction of putative metabolite mass spectra from ion-wise chromatographic alignment data. The algorithm is based on the subtractive fuzzy clustering method that allows unsupervised determination of a number of metabolites in a data set and can deal with uncertain memberships of mass peaks in overlapping mass spectra. This approach is based purely on the actual information present in the data and does not require any prior metabolite knowledge. MSClust can be applied for both GC-MS and LC-MS alignment data sets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0368-2) contains supplementary material, which is available to authorized users.     

A gender-specific discriminator in Sprague-Dawley rat urine: the deployment of a metabolic profiling strategy for biomarker discovery and identification

The use of nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS) as complementary analytical techniques for open metabolic profiling is illustrated in the context of defining urinary biochemical discriminators between male and female Sprague-Dawley rats. Subsequent to the discovery of a female-specific urinary discriminator by LC-MS, further LC, MS, and NMR methods have been applied in a coordinated effort to identify this urinary component. Thereafter, the biological relevance and context of the identified component, in this case a steroid metabolite, has been achieved. This approach will be deployed in future studies of disease, drug efficacy, and toxicity to discover and identify biologically relevant markers.     

Discovering variation of secondary metabolite diversity and its relationship with disease resistance in Cornus florida L.

Understanding intraspecific relationships between genetic and functional diversity is a major goal in the field of evolutionary biology and is important for conserving biodiversity. Linking intraspecific molecular patterns of plants to ecological pressures and trait variation remains difficult due to environment‐driven plasticity. Next‐generation sequencing, untargeted liquid chromatography–mass spectrometry (LC‐MS) profiling, and interdisciplinary approaches integrating population genomics, metabolomics, and community ecology permit novel strategies to tackle this problem. We analyzed six natural populations of the disease‐threatened Cornus florida L. from distinct ecological regions using genotype‐by‐sequencing markers and LC‐MS‐based untargeted metabolite profiling. We tested the hypothesis that higher genetic diversity in C. florida yielded higher chemical diversity and less disease susceptibility (screening hypothesis), and we also determined whether genetically similar subpopulations were similar in chemical composition. Most importantly, we identified metabolites that were associated with candidate loci or were predictive biomarkers of healthy or diseased plants after controlling for environment. Subpopulation clustering patterns based on genetic or chemical distances were largely congruent. While differences in genetic diversity were small among subpopulations, we did observe notable similarities in patterns between subpopulation averages of rarefied‐allelic and chemical richness. More specifically, we found that the most abundant compound of a correlated group of putative terpenoid glycosides and derivatives was correlated with tree health when considering chemodiversity. Random forest biomarker and genomewide association tests suggested that this putative iridoid glucoside and other closely associated chemical features were correlated to SNPs under selection.     

Evaluation of the repeatability of ultra-performance liquid chromatography-TOF-MS for global metabolic profiling of human urine samples

The application of reversed-phase ultra-performance liquid chromatography, based on the use of sub 2 microm particles, combined with time-of-flight mass spectrometry has been investigated for the production of global metabolite profiles from human urine. The stability and repeatability of the methodology, which employed gradient elution, was determined by the repeat analysis of a pooled quality control (QC) sample. As seen in previous studies conducted with conventional LC-MS an element of system conditioning was required to obtain reproducible data, as the initial injections were unrepresentative. However, once the system had equilibrated excellent repeatability in terms of retention time, signal intensity and mass accuracy was seen providing confidence that for this matrix, the within-day repeatability of UPLC-TOF-MS was sufficient to assure data quality in global metabolic profiling applications.     

Global urinary metabolic profiling procedures using gas chromatography-mass spectrometry

The role of urinary metabolic profiling in systems biology research is expanding. This is because of the use of this technology for clinical diagnostic and mechanistic studies and for the development of new personalized health care and molecular epidemiology (population) studies. The methodologies commonly used for metabolic profiling are NMR spectroscopy, liquid chromatography mass spectrometry (LC/MS) and gas chromatography-mass spectrometry (GC/MS). In this protocol, we describe urine collection and storage, GC/MS and data preprocessing methods, chemometric data analysis and urinary marker metabolite identification. Results obtained using GC/MS are complementary to NMR and LC/MS. Sample preparation for GC/MS analysis involves the depletion of urea via treatment with urease, protein precipitation with methanol, and trimethylsilyl derivatization. The protocol described here facilitates the metabolic profiling of ～400-600 metabolites in 120 urine samples per week.     

Within-day reproducibility of an HPLC-MS-based method for metabonomic analysis: application to human urine

Self-evidently, research in areas supporting "systems biology" such as genomics, proteomics, and metabonomics are critically dependent on the generation of sound analytical data. Metabolic phenotyping using LC-MS-based methods is currently at a relatively early stage of development, and approaches to ensure data quality are still developing. As part of studies on the application of LC-MS in metabonomics, the within-day reproducibility of LC-MS, with both positive and negative electrospray ionization (ESI), has been investigated using a standard "quality control" (QC) sample. The results showed that the first few injections on the system were not representative, and should be discarded, and that reproducibility was critically dependent on signal intensity. On the basis of these findings, an analytical protocol for the metabonomic analysis of human urine has been developed with proposed acceptance criteria based on a step-by-step assessment of the data. Short-term sample stability for human urine was also assessed. Samples were stable for at least 20 h at 4 degrees C in the autosampler while queuing for analysis. Samples stored at either -20 or -80 degrees C for up to 1 month were indistinguishable on subsequent LC-MS analysis. Overall, by careful monitoring of the QC data, it is possible to demonstrate that the "within-day" reproducibility of LC-MS is sufficient to ensure data quality in global metabolic profiling applications.     

Untargeted plasma metabolite profiling reveals the broad systemic consequences of xanthine oxidoreductase inactivation in mice

A major challenge in systems biology is integration of molecular findings for individual enzyme activities into a cohesive high-level understanding of cellular metabolism and physiology/pathophysiology. However, meaningful prediction for how a perturbed enzyme activity will globally impact metabolism in a cell, tissue or intact organisms is precluded by multiple unknowns, including in vivo enzymatic rates, subcellular distribution and pathway interactions. To address this challenge, metabolomics offers the potential to simultaneously survey changes in thousands of structurally diverse metabolites within complex biological matrices. The present study assessed the capability of untargeted plasma metabolite profiling to discover systemic changes arising from inactivation of xanthine oxidoreductase (XOR), an enzyme that catalyzes the final steps in purine degradation. Using LC-MS coupled with a multivariate statistical data analysis platform, we confidently surveyed >3,700 plasma metabolites (50-1,000 Da) for differential expression in XOR wildtype vs. mice with inactivated XOR, arising from gene deletion or pharmacological inhibition. Results confirmed the predicted derangements in purine metabolism, but also revealed unanticipated perturbations in metabolism of pyrimidines, nicotinamides, tryptophan, phospholipids, Krebs and urea cycles, and revealed kidney dysfunction biomarkers. Histochemical studies confirmed and characterized kidney failure in xor-nullizygous mice. These findings provide new insight into XOR functions and demonstrate the power of untargeted metabolite profiling for systemic discovery of direct and indirect consequences of gene mutations and drug treatments.     

Development and validation of a UPLC/MS method for a nutritional metabolomic study of human plasma

In order to study the effect of a diet on metabolites found in body fluids such as plasma, we have developed and validated a UPLC/MS method. While methods using NMR have been well established to analyse different biological tissues, recent studies have described robust untargeted UPLC-MS methods for plasma analysis. One major concern when profiling plasma is the presence of an important quantity of proteins which have to be precipitated without any loss of metabolites prior to LC/MS analysis. The utilization of untargeted approaches in nutritional metabolomics still suffers from the lack of identification of specific biomarkers. We therefore suggest an alternative method still using a global approach but focusing at the same time on metabolites previously described in human plasma in order to detect biomarkers of metabolic dysregulations. Thus, to fulfil our objectives, analytical parameters were tested (i) the anticoagulant type for sample collection, (ii) the protein precipitation method and (iii) UPLC/MS analytical conditions. Three protein precipitation methods and two anticoagulants were tested and compared. The method utilizing blood collection on heparin and methanol precipitation was chosen for giving the most reproducible results while keeping the complexity of the sample. Finally, a validation was proposed to evaluate the stability of this analytical method applied to a large batch of samples for nutritional metabolomic studies.     

Automated analysis of urinary catecholamines by high-performance liquid chromatography and on-line sample pretreatment

A simple and automated solid-phase extraction for the selective and quantitative HPLC analysis of free catecholamines in urine is described. The urinary catecholamines react with diphenylboric acid, giving a complex at pH 8.5 which is strongly retained on a PLRP-S cartridge; elution is accomplished with the same mobile phase used for HPLC analysis. Separation is performed by ion-pair reversed-phase HPLC, with sodium heptanesulphate as counter-ion, and a totally end-capped C₁₈ analytical column. Quantitation is achieved with an electrochemical detector. A Spark Holland Prospekt system controls the on-line solid-phase extraction, preconcentration and direct elution to the LC column. Chromatography run-time is 10 min and the total time to process one urine sample is ca. 12 min.     

Global metabolic profiling analysis on human urine by UPLC-TOFMS: issues and method validation in nutritional metabolomics

Optimisation and method validation was assessed here for metabolic profiling analysis of urine samples using UPLC-TOFMS. A longer run time of 31 min revealed greater reproducibility, and the higher number of variables was identified as compared to shortened run times (10 and 26 min). We have also implemented two QC urine samples enabling the assessment of the quality and reproducibility of the data generated during the whole analytical workflow (retention time drift, mass precision and fluctuation of the ion responses over time). Based on the QC data, suitable standards for ensuring consistent analytical results for metabolomics applications using the UPLC-MS techniques are recommended.     

Quantitation of five nevirapine oxidative metabolites in human plasma using liquid chromatography-tandem mass spectrometry

A multiple-reaction-monitoring LC/MS/MS method for the analysis of nevirapine oxidative metabolites, 2-hydroxynevirapine, 3-hydroxynevirapine, 8-hydroxynevirapine, 12-hydroxynevirapine, and 4-carboxynevirapine, in human plasma was developed and validated. The metabolites were isolated from 50 microL heparinized plasma by enzymatic hydrolysis of the glucuronide conjugates to the free metabolite followed by protein precipitation with acetonitrile. Peaks were quantitated at 3.03 min for the 4-carboxynevirapine metabolite, at 3.72, 4.27, 5.27, and 5.73 min for the positional 2-hydroxynevirapine, 12-hydroxynevirapine, 3-hydroxynevirapine, and 8-hydroxynevirapine metabolites, respectively, and 2.30 min for the internal standard, pirenzepine. The assay was accurate and precise based on assay validation controls over the nominal range of 0.010-1.0 mg/L. The average accuracy at the lowest concentration quality control (QC) sample was 16% (difference from theoretical value) for 8-hydroxynevirapine, all others were closer to their known respective standards. Within- and between-day precisions were within 12% for quality control samples for all five metabolites. Repetitive thawing and freezing did not have an effect on any metabolite through a minimum of three cycles. Thawed samples, remaining in plasma for 4 h before extraction, were within 5% of theoretical value. Stability of the extracted samples on the autosampler at room temperature was evaluated for 48 h and was observed to be within 12% of a fresh analytical sample for 2-hydroxynevirapine and 3-hydroxynevirapine; other metabolites were within 6% of theoretical value. The utility of the analytical method was demonstrated using trough steady-state plasma samples collected from 48 patients in a hepatic impairment study.     

Improving mass and liquid chromatography based identification of proteins using bayesian scoring

We present a method for peptide and protein identification based on LC-MS profiling. The method identified peptides at high-throughput without expending the sequencing time necessary for CID spectra based identification. The measurable peptide properties of mass and liquid chromatographic elution conditions are used to characterize and differentiate peptide features, and these peptide features are matched to a reference database from previously acquired and archived LC-MS/MS experiments to generate sequence assignments. The matches are scored according to the probability of an overlap between the peptide feature and the database peptides resulting in a ranked list of possible peptide sequences for each peptide submitted. This method resulted in 6 times more peptide sequence identifications from a single LC-MS analysis of yeast than from shotgun peptide sequencing using LC-MS/MS.     

Quantifying reproducibility for differential proteomics: noise analysis for protein liquid chromatography-mass spectrometry of human serum

SUMMARY: Using replicated human serum samples, we applied an error model for proteomic differential expression profiling for a high-resolution liquid chromatography-mass spectrometry (LC-MS) platform. The detailed noise analysis presented here uses an experimental design that separates variance caused by sample preparation from variance due to analytical equipment. An analytic approach based on a two-component error model was applied, and in combination with an existing data driven technique that utilizes local sample averaging, we characterized and quantified the noise variance as a function of mean peak intensity. The results indicate that for processed LC-MS data a constant coefficient of variation is dominant for high intensities, whereas a model for low intensities explains Poisson-like variations. This result leads to a quadratic variance model which is used for the estimation of sample preparation noise present in LC-MS data.     

Nearline acquisition and processing of liquid chromatography-tandem mass spectrometry data

Liquid chromatography–mass spectrometry (LC–MS) is a commonly used analytical platform for non-targeted metabolite profiling experiments. Although data acquisition, processing and statistical analyses are almost routine in such experiments, further annotation and subsequent identification of chemical compounds are not. For identification, tandem mass spectra provide valuable information towards the structure of chemical compounds. These are typically acquired online, in data-dependent mode, or offline, using handcrafted acquisition methods and manually extracted from raw data. Here, we present several methods to fast-track and improve both the acquisition and processing of LC–MS/MS data. Our nearly online (nearline) data-dependent tandem MS strategy creates a minimal set of LC–MS/MS acquisition methods for relevant features revealed by a preceding non-targeted profiling experiment. Using different filtering criteria, such as intensity or ion type, the acquisition of irrelevant spectra is minimized. Afterwards, LC–MS/MS raw data are processed with feature detection and grouping algorithms. The extracted tandem mass spectra can be used for both library search and de-novo identification methods. The algorithms are implemented in the R package MetShot and support the export to Bruker, Agilent or Waters QTOF instruments and the vendor-independent TraML standard. We evaluate the performance of our workflow on a Bruker micrOTOF-Q by comparison of automatically acquired and extracted tandem mass spectra obtained from a mixture of natural product standards against manually extracted reference spectra. Using Arabidopsis thaliana wild-type and biosynthetic gene knockout plants, we characterize the metabolic products of a biosynthetic pathway and demonstrate the integration of our approach into a typical non-targeted metabolite profiling workflow.

     

Nearline acquisition and processing of liquid chromatography-tandem mass spectrometry data

Liquid chromatography–mass spectrometry (LC–MS) is a commonly used analytical platform for non-targeted metabolite profiling experiments. Although data acquisition, processing and statistical analyses are almost routine in such experiments, further annotation and subsequent identification of chemical compounds are not. For identification, tandem mass spectra provide valuable information towards the structure of chemical compounds. These are typically acquired online, in data-dependent mode, or offline, using handcrafted acquisition methods and manually extracted from raw data. Here, we present several methods to fast-track and improve both the acquisition and processing of LC–MS/MS data. Our nearly online (nearline) data-dependent tandem MS strategy creates a minimal set of LC–MS/MS acquisition methods for relevant features revealed by a preceding non-targeted profiling experiment. Using different filtering criteria, such as intensity or ion type, the acquisition of irrelevant spectra is minimized. Afterwards, LC–MS/MS raw data are processed with feature detection and grouping algorithms. The extracted tandem mass spectra can be used for both library search and de-novo identification methods. The algorithms are implemented in the R package MetShot and support the export to Bruker, Agilent or Waters QTOF instruments and the vendor-independent TraML standard. We evaluate the performance of our workflow on a Bruker micrOTOF-Q by comparison of automatically acquired and extracted tandem mass spectra obtained from a mixture of natural product standards against manually extracted reference spectra. Using Arabidopsis thaliana wild-type and biosynthetic gene knockout plants, we characterize the metabolic products of a biosynthetic pathway and demonstrate the integration of our approach into a typical non-targeted metabolite profiling workflow.     

Metabolic profiling of human saliva before and after induced physiological stress by ultra-high performance liquid chromatography–ion mobility–mass spectrometry

A method has been developed for metabolite profiling of the salivary metabolome based on protein precipitation and ultra-high performance liquid chromatography coupled with ion mobility-mass spectrometry (UHPLC–IM–MS). The developed method requires 0.5 mL of human saliva, which is easily obtainable by passive drool. Standard protocols have been established for the collection, storage and pre-treatment of saliva. The use of UHPLC allows rapid global metabolic profiling for biomarker discovery with a cycle time of 15 min. Mass spectrometry imparts the ability to analyse a diverse number of species reproducibly over a wide dynamic range, which is essential for profiling of biofluids. The combination of UHPLC with IM–MS provides an added dimension enabling complex metabolic samples to be separated on the basis of retention time, ion mobility and mass-to-charge ratio in a single chromatographic run. The developed method has been applied to targeted metabolite identification and untargeted metabolite profiling of saliva samples collected before and after exercise-induced physiological stress. δ-Valerolactam has been identified as a potential biomarker on the basis of retention time, MS/MS spectrum and ion mobility drift time.     

LC–MS based global metabolite profiling: the necessity of high data quality

LC–MS based global metabolite profiling currently lacks detailed guidelines to demonstrate that the obtained data is of high enough analytical quality. Insufficient data quality may result in the failure to generate a hypothesis, or in the worst case, a false or skewed hypothesis. After assessing the literature, it is apparent that an analytically focused summary and critical discussion related to this subject would be beneficial for both beginners and experts engaged in this field. A particular focus will be placed on data quality, which we here define as the degree to which a set of parameters fulfills predetermined criteria, similar to the established guidelines for targeted analysis. However, several of these parameters are difficult to assess since holistic approaches measure thousands of metabolites in parallel and seldom include predefined knowledge of which metabolites will differ between sample groups. In this review, the following parameters will be discussed in detail: reproducibility, selectivity, certainty of metabolite identification and metabolite coverage. The review systematically describes the generic workflow for LC–MS based global metabolite profiling and highlights how each separate part may affect data quality. The last part of the review describes how data quality can be evaluated as well as identifies areas where additional improvement is needed. In this review, we provide our own analytical opinions in regards to evaluation and, to some extent, improvement of data quality.     

A positive/negative ion-switching, targeted mass spectrometry-based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue

The revival of interest in cancer cell metabolism in recent years has prompted the need for quantitative analytical platforms for studying metabolites from in vivo sources. We implemented a quantitative polar metabolomics profiling platform using selected reaction monitoring with a 5500 QTRAP hybrid triple quadrupole mass spectrometer that covers all major metabolic pathways. The platform uses hydrophilic interaction liquid chromatography with positive/negative ion switching to analyze 258 metabolites (289 Q1/Q3 transitions) from a single 15-min liquid chromatography-mass spectrometry acquisition with a 3-ms dwell time and a 1.55-s duty cycle time. Previous platforms use more than one experiment to profile this number of metabolites from different ionization modes. The platform is compatible with polar metabolites from any biological source, including fresh tissues, cancer cells, bodily fluids and formalin-fixed paraffin-embedded tumor tissue. Relative quantification can be achieved without using internal standards, and integrated peak areas based on total ion current can be used for statistical analyses and pathway analyses across biological sample conditions. The procedure takes ～12 h from metabolite extraction to peak integration for a data set containing 15 total samples (～6 h for a single sample).     

In vitro metabolic stability and metabolite profiling of TJ0711 hydrochloride, a newly developed vasodilatory β-blocker, using a liquid chromatography-tandem mass spectrometry method

In this paper, a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed and validated for the simultaneous analysis of metabolic stability and metabolite profiling of 1-[4-(2-methoxyethyl) phenoxy]-3-[[2-(2-methoxyphenoxy) ethyl]amino]-2-propanol hydrochloride (TJ0711 HCl), a new vasodilatory β-blocker. Multiple reaction monitoring (MRM) was used as a survey scan to quantify the parent compound and to trigger the acquisition of enhanced product ions (EPI) for the identification of formed metabolites. In addition, comparison between MRM-only and MRM-information dependent acquisition-EPI (MRM-IDA-EPI) methods was conducted to determine analytical variables, including linearity, limit of detection (LOD), lower limit of quantification (LLOQ), as well as intra-day and inter-day accuracy and precision. Results demonstrated that MRM-IDA-EPI quantitative analysis was not affected by the addition of EPI scans to obtain qualitative information during the same chromatographic run, compared to MRM-only method. Thereafter, metabolic stability and metabolite identification of TJ0711 HCl were investigated using human liver microsomes (HLM) by the MRM-IDA-EPI method. The in vitro metabolic stability parameters were calculated and t(1/2), microsomal intrinsic clearance (CL(int)), as well as hepatic CL, were 13.0 min, 106.5 μL/min/mg microsomal protein, and 1082.2 mL/min, respectively. The major formed metabolites were also simultaneously monitored and the metabolite profiling data demonstrated that this MRM-IDA-EPI method was capable of targeting a large number of metabolites, in which demethylation and hydroxylation were the principle metabolism pathways during the in vitro incubation with HLM.     

SuperHirn - a novel tool for high resolution LC-MS-based peptide/protein profiling

Label-free quantification of high mass resolution LC-MS data has emerged as a promising technology for proteome analysis. Computational methods are required for the accurate extraction of peptide signals from LC-MS data and the tracking of these features across the measurements of different samples. We present here an open source software tool, SuperHirn, that comprises a set of modules to process LC-MS data acquired on a high resolution mass spectrometer. The program includes newly developed functionalities to analyze LC-MS data such as feature extraction and quantification, LC-MS similarity analysis, LC-MS alignment of multiple datasets, and intensity normalization. These program routines extract profiles of measured features and comprise tools for clustering and classification analysis of the profiles. SuperHirn was applied in an MS1-based profiling approach to a benchmark LC-MS dataset of complex protein mixtures with defined concentration changes. We show that the program automatically detects profiling trends in an unsupervised manner and is able to associate proteins to their correct theoretical dilution profile.