D-optimal design of an untargeted HS-SPME-GC-TOF metabolite profiling method

In recent times we have seen the development of many "-omics" technologies. One of the youngest is undoubtedly metabolomics, which aims to define the whole chemical fingerprint unique to each specific organism. The development and optimisation of an untargeted high-throughput method capable of investigating the volatile fraction of a biological system represents a crucial step for the success of such holistic approaches, and specific optimisation criteria must be developed in connection with suitable experimental designs. In this paper experimental designs (D-optimal) were applied for the first time as an automatic optimisation tool to an untargeted HS-SPME-GC-TOF method. In this case, optimal conditions correspond to a maximal number of detected features, in order to provide a fingerprint that is as complete as possible. The system under study is the grape berry. Four variables were considered: the type of fibre, extraction time, equilibration time and temperature. The results show that the D-optimal design methodology provides an easily interpretable assessment of experimental settings. This and other specific properties of the D-optimal design, such as the possibility to explicitly exclude certain experimental conditions, make it an extremely suitable strategy for method optimisation in untargeted metabolomics.     

Extending the breadth of metabolite profiling by gas chromatography coupled to mass spectrometry

Gas chromatography coupled to mass spectrometry (GC-MS) is one of the most frequently used tools for profiling primary metabolites. Instruments are mature enough to run large sequences of samples; novel advancements increase the breadth of compounds that can be analyzed, and improved algorithms and databases are employed to capture and utilize biologically relevant information. Around half the published reports on metabolite profiling by GC-MS focus on biological problems rather than on methodological advances. Applications span from comprehensive analysis of volatiles to assessment of metabolic fluxes for bioengineering. Method improvements emphasize extraction procedures, evaluations of quality control of GC-MS in comparison to other techniques and approaches to data processing. Two major challenges remain: rapid annotation of unknown peaks; and, integration of biological background knowledge aiding data interpretation.     

Enrichment tags for enhanced-resolution profiling of the polar metabolome

The field of metabolomics aims to develop and apply methods to study the full complement of endogenous small molecules in biological systems. One of the major challenges in metabolomics is obtaining adequate resolution of compounds with similar physicochemical properties. The resolution of polar metabolites can be exceptionally problematic as these compounds are often poorly retained with reverse phase matrices. Here, we describe an advanced chemoselective tagging strategy to enrich and profile highly polar metabolites. Metabolite-reactive tags were appended with a hydrophobic p-Cl-phenylalanine residue, which conferred enhanced retention and resolution upon labeled small-molecules. Notably, the increased resolution afforded by hydrophobic tags minimized overlap in tandem mass spectrometry profiles for polar metabolites, thereby facilitating their structure determination in complex biological samples. Additionally, the chlorine atom of the tag permitted the discrimination of tagged metabolites from background peaks (i.e., false positives) and the discovery of metabolites that possess multiple copies of the same functional group. These studies designate chemoselective small-molecule tags as versatile tools for enriching and profiling challenging fractions of the metabolome.     

Evaluation of the chronic intoxication of fluoride on human serum metabolome using untargeted metabolomics

Drinking water is the main source of fluoride intake for the human body and its regulated consumption helps in decreasing dental caries. However, excessive fluoride consumption over a prolonged time period causes fluorosis disease which adversely affects many tissues and organs of the body. This paper describes the evaluation of chronic intoxication of fluoride on human serum metabolome. The untargeted metabolomics approach using UPLC-QTOF-MS/MS is applied for metabolomic profiling, whereas the estimation of fluoride in serum samples was carried out using the ion-selective electrode (ISE). Fluoride concentration was found to be 0.16–1.25 mg/L in serum samples of 39 fluorosis patients and 0.008–0.045 mg/L in 20 healthy samples. A total of 47 metabolites were identified based on the high-resolution mass spectrometry analysis. A volcano plot was generated to discriminate features that are significantly different between the fluorosis and healthy groups at the probability of 0.05 and fold change ≥ 2. Among all identified metabolites, intensities of ten differential identified metabolites including inosine, α-linolenic acid, guanosine, octanoyl-L-carnitine, His-Trp, phytosphingosine, lauroyl-L-carnitine, hydrocortisone, deoxyinosine and dodecanedioic acid have been found altered in disease samples compared to healthy controls. Major pathways identified based on these metabolites include energy metabolism, fatty acid oxidation, purine degradation pathway, elevated protein degradation, and increased ω-6 fatty acid linoleate signatures were observed.     

Trend analysis of time-series data: A novel method for untargeted metabolite discovery

A new strategy for biomarker discovery is presented that uses time-series metabolomics data. Data sets from samples analysed at different time points after an intervention are searched for compounds that show a meaningful trend following the intervention. Obviously, this requires new data-analytical tools to distinguish such compounds from those showing only random variation. Two univariate methods, autocorrelation and curve-fitting, are used either as stand-alone methods or in combination to discover unknown metabolites in data sets originating from target-compound analysis. Both techniques reduce the long list of detected compounds in the kinetic sample set to include only those having a pre-defined interesting time profile. Thus, new metabolites may be discovered within data structures that are usually only used for target-compound analysis.The new strategy is tested on a sample set obtained from a gut fermentation study of a polyphenol-rich diet. For this study, the initial list of over 9000 potentially interesting features was reduced to less than 150, thus significantly reducing the expensive and time-consuming manual examination.     

Improved liquid chromatography-Online radioactivity detection for metabolite profiling

If very-high-pressure liquid chromatography (VHPLC) is to replace conventional HPLC as the ultimate separation tool for metabolism studies in development, coupling it efficiently with online radioactivity detection (RAD) is needed. We describe the successful combination of VHPLC/RAD, facilitated by improvements in online radioactivity detection, as well as in column loading and peak capacity. The sensitivity of (14)C detection was improved by the use of a variable scintillation flow achieved via a simple modification to the classical online radiochemical detection set-up. A modification of the flow-through cell design in which internal diameter of the tubing was reduced further increased the sensitivity and resolution by decreasing peak tailing. The injection of relatively large injection volumes was made possible by the use of columns packed at ultra-high pressure with 2.2 microm particles. Because of the reduced back pressure generated using these larger particle sizes, two 150 mm x 3 mm columns could be coupled, allowing 4-fold larger injection volumes and a 50% increase in theoretical plate number at a similar back pressure compared to a standard 150 mm x 2.1mm Waters UPLC column. The value of the methodology described was demonstrated by the analysis of in vitro and in vivo metabolism samples of (3)H- and (14)C-labeled compounds and compared with conventional radio-HPLC. We have shown that metabolite separation can be achieved with increased efficiency while maintaining a sensitivity comparable to that of conventional HPLC/RAD.     

Effects of freeze-drying of samples on metabolite levels in metabolome analyses

Freeze-drying (FD) is a useful technique for removing water from biological tissues, such as food samples. Cellular components freeze at once, and the ice sublimates under conditions of high vacuum and low temperatures. Because biological activity is restricted during FD, the degradation of cellular metabolites is often believed to be limited. However, the cellular structure is damaged by several factors, such as the increase in cell volume during freezing, and this has serious effects on the levels of some cellular metabolites. We studied these effects of FD on metabolite levels when using it as a sample preparation step in metabolome analysis. We observed significant decreases in the levels of some metabolites, such as succinate and choline, in Arabidopsis and pear, respectively. We also found that the effects of FD on certain metabolite levels differed between Arabidopsis plants and pear fruits. These results suggest that it is necessary to confirm the metabolite recovery in each sample species when FD is used for sample preparation.     

Target list building for volatile metabolite profiling of fruit

Fruit flavour is the combination of numerous biochemicals: sugars for sweetness, acids for sourness and volatile metabolites for aroma. The objective of this study was to establish a method to develop a target list of statistically relevant compounds for the characterization of melon from non-targeted data, while preserving the profile information. Five different varieties were sampled (sampling 12 biological replicates from 12 plants) using dynamic headspace extraction, then analysed by gas chromatography–mass spectrometry in full scan mode. Using Metalign and SIMCA-P software the raw data was spectrally aligned and then subjected to principal component analysis (PCA). The principal component analysis plot showed good separation of the five varieties based on their full scan GC–MS profile. Mass spectral data points responsible for the differences between varieties were highlighted by further statistical analysis. The mass spectra were then reconstructed and the corresponding chemicals identified using library search or reference standards were available to create a new target component list. To validate the new target list, the initial data set was re-processed using the targeted approach and the results subjected again to principal component analysis. The two representations showed excellent agreement on the separation of the five varieties. The new target list obtained from this study can be applied to differentiate and characterize the volatile profile of melon varieties using a list of statistically significant compounds.     

Interlaboratory comparison for quantitative primary metabolite profiling in Pichia pastoris

For the first time, an interlaboratory comparison was performed in the field of quantitative metabolite profiling in Pichia pastoris. The study was designed for the evaluation of different measurement platforms integrating different quantification strategies using internal standardization. Nineteen primary metabolites including amino acids and organic acids were selected for the study. Homogenous samples were obtained from chemostat fermentations after rapid sampling, quenching and filtration, and hot ethanol extraction. Laboratory 1 (BOKU) employed an in vivo-synthesized fully labeled U¹³C cell extracts of P. pastoris for immediate internal standardization upon cell extraction. Quantification was carried out using orthogonal reversed-phase (RP-LC) and hydrophilic interaction chromatography (HILIC) in combination with tandem mass spectrometry. Laboratory 2 (Biocrates) applied a metabolomics kit allowing fully automated, rapid derivatization, solid phase extraction and internal standardization in 96-well plates with immobilized isotopically enriched internal standards in combination with HILIC-MS-MS and RP-LC-MS-MS for organic acids and derivatized amino acids, respectively. In this study, the obtained intracellular concentrations ranged from 0.2 to 108 μmol?g^?1 cell dry weight. The total combined uncertainty was estimated including uncertainty contributions from the corresponding MS-based measurement and sample preparation for each metabolite. Evidently, the uncertainty contribution of sample preparation was lower for the values obtained by laboratory 1, implementing isotope dilution upon extraction. Total combined uncertainties (K?=?2) ranging from 21 to 48 % and from 30 to 57 % were assessed for the quantitative results obtained in laboratories 1 and 2, respectively. The major contribution arose from sample preparation, hence from repeatability precision of the extraction procedure. Finally, the laboratory intercomparison was successful as most of the investigated metabolites showed concentration levels agreeing within their total combined uncertainty, implying that accurate quantification was given. The application of isotope dilution upon extraction was an absolute prerequisite for the quantification of the redox-sensitive amino acid methionine, where no agreement between the two laboratories could be achieved.     

Stable isotopic labelling-assisted untargeted metabolic profiling reveals novel conjugates of the mycotoxin deoxynivalenol in wheat

An untargeted screening strategy for the detection of biotransformation products of xenobiotics using stable isotopic labelling (SIL) and liquid chromatography–high resolution mass spectrometry (LC-HRMS) is reported. The organism of interest is treated with a mixture of labelled and non-labelled precursor and samples are analysed by LC-HRMS. Raw data are processed with the recently developed MetExtract software for the automated extraction of corresponding peak pairs. The SIL-assisted approach is exemplified by the metabolisation of the Fusarium mycotoxin deoxynivalenol (DON) in planta. Flowering ears were inoculated with 100 μg of a 1?+?1 (v/v) mixture of non-labelled and fully labelled DON. Subsequent sample preparation, LC-HRMS measurements and data processing revealed a total of 57 corresponding peak pairs, which originated from ten metabolites. Besides the known DON and DON-3-glucoside, which were confirmed by measurement of authentic standards, eight further DON-biotransformation products were found by the untargeted screening approach. Based on a mass deviation of less than ±5 ppm and MS/MS measurements, one of these products was annotated as DON-glutathione (GSH) conjugate, which is described here for the first time for wheat. Our data further suggest that two DON-GSH-related metabolites, the processing products DON-S-cysteine and DON-S-cysteinyl-glycine and five unknown DON conjugates were formed in planta. Future MS/MS measurements shall reveal the molecular structures of the detected conjugates in more detail.     

Ferromagnetic micropallets for magnetic capture of single adherent cells

We present a magnetic micropallet array and demonstrate its capacity to recover specific, individual adherent cells from large populations and deliver them for downstream single cell analysis. A ferromagnetic photopolymer was formulated, characterized, and used to fabricate magnetic micropallets, which are microscale pedestals that provide demarcated cell growth surfaces with preservation of biophysical properties including photopatternability, biocompatibility, and optical clarity. Each micropallet holds a single adherent cell in culture, and hundreds of thousands of micropallets comprise a single micropallet array. Any micropallet in the array can be recovered on demand, carrying the adhered cell with it. We used this platform to recover selectively single cells, which were subsequently analyzed using single-cell RT-qPCR.     

Targeted serum metabolite profiling of nucleosides in esophageal adenocarcinoma

Nucleosides are indicators of the whole‐body turnover of transfer RNA. Based on the activity of cancer cells these molecules could potentially be used as cancer biomarkers, and several studies have determined that the metabolic levels of nucleosides are significantly altered in cancer patients compared to control groups. Here we report a targeted metabolite investigation of serum nucleosides in esophageal adenocarcinoma specimens. We quantified eight nucleosides using high‐performance liquid chromatography/triple quadrupole mass spectrometry (HPLC/TQMS) and determined that the metabolic levels of 1‐methyladenosine (p <2.14 × 10^?7), N²,N²‐dimethylguanosine (p <2.78 × 10^?7), N²‐methylguanosine (p <2.48 × 10^?6) and cytidine (p <6.98 × 10^?4) were significantly elevated while the concentration of uridine (p <3.74 × 10^?3) was significantly lowered in serum samples from cancer patients compared to those of control group. Our results suggest that nucleosides could potentially serve as useful biomarkers to identify esophageal adenocarcinoma. Copyright © 2010 John Wiley & Sons, Ltd.     

Comprehensive metabolite profiling in distinct chemotypes of Commiphora wightii

Commiphora wightii (Arn.) Bhandari, known as guggul, produces a medicinally important gum resin which is used extensively by Ayurvedic physicians to treat various ailments. However, most of the studies on C. wightii have been limited to its gum resin. Comprehensive metabolic profiling of leaves, stem and gum resin samples was undertaken to analyse aqueous and non-aqueous metabolites from three distinct chemotypes (NBRI-101, NBRI-102 and NBRI-103) shortlisted from different agro-climatic zones. GC-MS, HPLC and NMR spectroscopy were used for comprehensive metabolomics. Multivariate analysis showed characteristic variation in quinic and citric acids, myo-inositol and glycine (aqueous metabolites) and 2,6-di-tert-butyl-phenol, trans-farnesol and guggulsterones (non-aqueous metabolites) amongst the three chemotypes. Quinic acid, citric acid and myo-ionositol were detected in substantial quantities from leaves and stem samples which provide opportunities for novel nutraceutical and pharmaceutical formulations. Quinic acid, from the leaves, was identified as a marker metabolite for early selection of high guggulsterones-yielding cultivars.     

An ultrasonication-assisted extraction and derivatization protocol for GC/TOFMS-based metabolite profiling

Conventional chemical derivatization of metabolites in biological specimens is time-consuming, which limits the throughput and efficiency of metabolite profiling using a gas chromatography/time-of-flight mass spectrometry (GC/TOFMS) platform. We report an ultrasonication-assisted protocol which reduces the derivatization time from hours to about 30 min and significantly enhances the derivatization efficiency prior to a GC/TOFMS analysis. The protocol was evaluated using 40 compounds representing different classes of human metabolites, and demonstrated good analytical precision and accuracy. In comparison with the conventional method, the new protocol was able to increase the intensity of most of the identified peaks (71.0%) in the GC/TOFMS chromatograms of human serum samples. The detected compounds with increased intensity include most amino acids, keto-containing organic acids, carbonyl-containing carbohydrates, and unsaturated fatty acids. We applied this protocol in a metabolomic study of human serum samples obtained from 34 patients diagnosed with hypertension and 29 age- and gender-matched healthy subjects. Metabolite markers associated with hypertension, including glucosamine, D-sorbitol, 1-stearoylglycerol, and homocysteine, were identified and validated by statistical methods and use of reference standards. Our work highlights the potential of this novel approach for the large-scale metabolite profiling of samples generated from plant, animal, and clinical and epidemiological studies.     

High-performance isotope-labeling liquid chromatography mass spectrometry for investigating the effect of drinking Goji tea on urine metabolome profiling

Urine is a human biofluid that is widely used for metabolomics research on disease biomarker discovery.Ideally,the metabolome profiles generated from comparative groups of individuals should mainly consist of the endogenous human metabolites that reflect the healthy states of the individuals.However,external factors,such as diet,may alter the urine metabolome profile by either introducing a significant amount or variety of exogenous metabolites to urine or inducing changes of the metabolome profile.Thus,strict control of the external factors during the sample collection process is critical for urine metabolomics aimed at discovery of disease biomarkers.In this work,we describe a study to determine the effect of drinking Goji tea,which is considered a nutritional supplement drink in some regions of the world,on urine metabolome profile.The purpose of this work is not to determine the nutritional values of Goji tea,but to investigate whether drinking a moderate amount of Goji tea 1-3 h(short-term effect)or 12 h(longer-term effect)before urine collection can cause significant variations of urine metabolome profiles.A highly sensitive dansylation isotope labeling liquid chromatography mass spectrometry(LC-MS)method was used to determine the urine metabolomes before and after drinking Goji tea.From the studies of the short term(<3 h)and longer term(12 h)effects of drinking Goji tea,it is clear that the consumption of a moderate amount of Goji tea does not affect the urine metabolome significantly.Fasting for 12 h should be sufficient to remove any potential interference of Goji metabolites from the human urine metabolome profile.     

Seed metabolite profiling of Vicia species from China via GC-MS

In this study, we examined Vicia seeds using gas chromatography-mass spectrometry (GC-MS). The metabolic differences of seeds of twelve Vicia species were assessed. 184 metabolites were identified. Vicia species were classified via multivariate data analyses into four clusters. V. unijuga was most enriched in fatty acids and anthraquinones contents while highest levels of amino acids, alcohols and phenolic were in V. costata. Clustering analysis of biochemical profiles matched with the pervious phenotypic observation with all examined species from section Cracca grouped together under one sub-cluster, except for V. costata.     

Ion-pairing reversed-phase liquid chromatography fractionation in combination with isotope labeling reversed-phase liquid chromatography-mass spectrometry for comprehensive metabolome profiling

We report a novel two-dimensional (2D) separation strategy aimed at improving the detectability of liquid chromatography mass spectrometry (LC-MS) for metabolome analysis. It is based on the use of ion-pairing (IP) reversed-phase (RP) LC as the first dimension separation to fractionate the metabolites, followed by isotope labeling of individual fractions using dansylation chemistry to alter the physiochemical properties of the metabolites. The labeled metabolites having different hydrophobicity from their unlabeled counterparts are then separated and analyzed by on-line RPLC Fourier-transform ion-cyclotron resonance mass spectrometry (FTICR-MS). This off-line 2D-LC-MS strategy offers significant improvement over the one-dimensional (1D) RPLC MS technique in terms of the number of detectable metabolites. As an example, in the analysis of a human urine sample, 3564 13C-/12C-dansylated ion pairs or metabolites were detected from seven IP RPLC fractions, compared to 1218 metabolites found in 1D-RPLC-MS. Using a library of 220 amine- and phenol-containing metabolite standards, 167 metabolites were positively identified based on retention time and accurate mass matches, which was about 2.5 times the number metabolites identified by 1D-RPLC-MS analysis of the same urine sample.     

Fecal metabolome profiling of liver cirrhosis and hepatocellular carcinoma patients by ultra performance liquid chromatography-mass spectrometry

Fecal metabolome of healthy humans and patients suffering from liver cirrhosis and hepatocellular carcinoma (HCC) were studied using ultra performance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry (UPLC/Q-TOF MS). Metabolic features detected by the method were then statistically treated using partial least squares to latent structure-discriminant analysis (PLS-DA) models to discriminate between healthy and diseased states. PLS-DA was also used to discriminate between cirrhosis and HCC stressed fecal metabolomes and to identify potential biomarkers for cirrhosis and HCC that are expressed at significantly different amounts in fecal metabolomes. Score plots of pattern recognition analysis distinguished liver cirrhosis and HCC patients from healthy humans. Based on the variable of importance in the project (VIP) values and S-plots, six metabolites were considered as potential biomarkers with a strong increase in lysophosphatidylcholines and a dramatic decrease in bile acids and bile pigments in patients with liver cirrhosis and HCC in comparison with healthy humans. Results demonstrate the potential of UPLC-MS as an efficient and convenient method that can be applied to screen fecal samples and aid in the early diagnosis of cirrhosis and hepatocellular carcinoma.