Metabolic profiling by ion mobility mass spectrometry (IMMS)

Ion Mobility Mass Spectrometry (IMMS) was evaluated as an analytical method for metabolic profiling. The specific instrument used in these studies was a direct infusion (DI)-electrospray ionization (ESI)—ambient pressure ion mobility spectrometer (APIMS) coupled to a time-of-flight mass spectrometer (TOFMS). The addition of an ion mobility spectrometer to a mass spectrometer had several advantages over direct infusion electrospray mass spectrometry alone. This tandem instrument (ESI-IMMS) added a rapid separation step with high-resolution prior to mass spectrometric analysis of metabolite mixtures without extending sample preparation time or reducing the high through put potential of direct mass spectrometry. Further, IMMS also reduced the baseline noise common with ESI-MS analyses of complex samples and enabled rapid separation of isobaric metabolites. IMMS was used to analyze the metabolome of Escherichia coli (E. coli), containing a collection of extremely diverse chemical compounds including hydrophobic lipids, inorganic ions, volatile alcohols and ketones, amino and non-amino organic acids, and hydrophilic carbohydrates. IMMS data were collected as two-dimensional spectra showing both mobility and mass of each ion detected. Using direct infusion ESI-IMMS of a non-derivatized methanol extract of an E. coli culture, more than 500 features were detected, of which over 200 intracellular metabolites were tentatively assigned as E. coli metabolites. This analytical method also allowed simultaneous separation of isomeric metabolic features.     

Plant metabolomics reveals conserved and divergent metabolic responses to salinity

New metabolic profiling technologies provide data on a wider range of metabolites than traditional targeted approaches. Metabolomic technologies currently facilitate acquisition of multivariate metabolic data using diverse, mostly hyphenated, chromatographic detection systems, such as GC-MS or liquid chromatography coupled to mass spectrometry, Fourier-transformed infrared spectroscopy or NMR-based methods. Analysis of the resulting data can be performed through a combination of non-supervised and supervised statistical methods, such as independent component analysis and analysis of variance, respectively. These methods reduce the complex data sets to information, which is relevant for the discovery of metabolic markers or for hypothesis-driven, pathway-based analysis. Plant responses to salinity involve changes in the activity of genes and proteins, which invariably lead to changes in plant metabolism. Here, we highlight a selection of recent publications in the salt stress field, and use gas chromatography time-of-flight mass spectrometry profiles of polar fractions from the plant models, Arabidopsis thaliana, Lotus japonicus and Oryza sativa to demonstrate the power of metabolite profiling. We present evidence for conserved and divergent metabolic responses among these three species and conclude that a change in the balance between amino acids and organic acids may be a conserved metabolic response of plants to salt stress.     

One-pot microwave derivatization of target compounds relevant to metabolomics with comprehensive two-dimensional gas chromatography

Metabolomics has been defined as the quantitative measurement of all low molecular weight metabolites (sugars, amino acids, organic acids, fatty acids and others) in an organism's cells at a specified time under specific environmental/biological conditions. Currently, there is considerable interest in developing a single method of derivatization and separation that satisfies the needs for metabolite analysis while recognizing the many chemical classes that constitute the metabolome. Chemical derivatization considerably increases the sensitivity and specificity of gas chromatography–mass spectrometry for compounds that are polar and have derivatizable groups. Microwave-assisted derivatization (MAD) of a set of standards spanning a wide range of metabolites of interest demonstrates the potential of MAD for metabolic profiling. A final protocol of 150 W power for 90 s was selected as the derivatization condition, based upon the study of each chemical class. A study of the generation of partially derivatized components established the conditions where this could potentially be a problem; the use of greater volumes of reagent ensured this would not arise. All compounds analyzed by comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry in a standard mixture showed good area ratio reproducibility against a naphthalene internal standard (RSD < 10% in all but one case). Concentrations tested ranged from 1 μg/mL to 1000 μg/mL, and the calibration curves for the standard mixtures were satisfactory with regression coefficients generally better than 0.998. The application to gas chromatography–quadrupole mass spectrometry and comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry for a typical reference standard of relevance to metabolomics is demonstrated.     

Clarification of pathway-specific inhibition by Fourier transform ion cyclotron resonance/mass spectrometry-based metabolic phenotyping studies

We have developed a metabolic profiling scheme based on direct-infusion Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR/MS). The scheme consists of: (1) reproducible data collection under optimized FT-ICR/MS analytical conditions; (2) automatic mass-error correction and multivariate analyses for metabolome characterization using a newly developed metabolomics tool (DMASS software); (3) identification of marker metabolite candidates by searching a species-metabolite relationship database, KNApSAcK; and (4) structural analyses by an MS/MS method. The scheme was applied to metabolic phenotyping of Arabidopsis (Arabidopsis thaliana) seedlings treated with different herbicidal chemical classes for pathway-specific inhibitions. Arabidopsis extracts were directly infused into an electrospray ionization source on an FT-ICR/MS system. Acquired metabolomics data were comprised of mass-to-charge ratio values with ion intensity information subjected to principal component analysis, and metabolic phenotypes from the herbicide treatments were clearly differentiated from those of the herbicide-free treatment. From each herbicide treatment, candidate metabolites representing such metabolic phenotypes were found through the KNApSAcK database search. The database search and MS/MS analyses suggested dose-dependent accumulation patterns of specific metabolites including several flavonoid glycosides. The metabolic phenotyping scheme on the basis of FT-ICR/MS coupled with the DMASS program is discussed as a general tool for high throughput metabolic phenotyping studies.     

The yeast metabolome addressed by electrospray ionization mass spectrometry: Initiation of a mass spectral library and its applications for metabolic footprinting by direct infusion mass spectrometry

Mass spectrometry (MS) has been a major driver for metabolomics, and gas chromatography (GC)-MS has been one of the primary techniques used for microbial metabolomics. The use of liquid chromatography (LC)-MS has however been limited, but electrospray ionization (ESI) is very well suited for ionization of microbial metabolites without any previous derivatization needed. To address the capabilities of ESI-MS in detecting the metabolome of Saccharomyces cerevisiae, the in silico metabolome of this organism was used as a template to present a theoretical metabolome. This showed that in combination with the specificity of MS up to 84% of the metabolites can be identified in a high mass accuracy ESI-spectrum. A total of 66 metabolites were systematically analyzed by positive and negative ESI-MS/MS with the aim of initiating a spectral library for ESI of microbial metabolites. This systematic analysis gave insight into the ionization and fragmentation characteristics of the different metabolites. With this insight, a small study of metabolic footprinting with ESI-MS demonstrated that biological information can be extracted from footprinting spectra. Statistical analysis of the footprinting data revealed discriminating ions, which could be assigned using the in silico metabolome. By this approach metabolic footprinting can advance from a classification method that is used to derive biological information based on guilt-by-association, to a tool for extraction of metabolic differences, which can guide new targeted biological experiments. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Gas chromatographic metabolic profiling: A sensitive tool for functional microbial ecology

Microbial metabolomics, which consists of a non-targeted analysis of the metabolites released from (‘exometabolome’) or existing in (‘endometabolome’) a cell has mostly been used to study the metabolism of particular microbes. Metabolomes also represent a picture of microbial activity and we suggest that the exometabolome may also contain pertinent information for studying microbial interaction networks. Gas chromatography coupled to mass spectrometry is the most commonly used technique in metabolomics studies. It allows a wide range of metabolites to be detected but requires the derivatisation of compounds prior to detection. This type of non-targeted analysis can introduce biases to the detection and quantification of the different metabolites, particularly at the extraction and derivatisation steps. The aims of this study, therefore, were to quantify the sources of variability and to test the sensitivity of the GC metabolic profiling approach to small environmental changes such as shifts in temperature. The temperature sensitivity of metabolic profiles was compared with that of catabolic profiles obtained using Biolog® microplates. Analytical variability was compared with biological variability by incubating bacterial strains isolated from soil with fructose at 20 °C and by replicating each step of the protocol (incubation, extraction and derivatisation). For both the endo- and the exometabolome, more than 70% of the total variability was of biological origin and principal components analysis clearly separated the strains along the first ordination axis. The endometabolome distinguished bacterial strains at the species level only, whereas separation was evident at the species and group level with the exometabolome. Temperature had a significant but differential effect on the metabolite production of the bacterial strains whilst their catabolic profiles remained relatively unaffected. The exometabolome was more sensitive to temperature shifts than the endometabolome, suggesting that this pool may be of interest for studies in environmental functional ecology.     

Gas chromatographic metabolic profiling: a sensitive tool for functional microbial ecology

Microbial metabolomics, which consists of a non-targeted analysis of the metabolites released from ('exometabolome') or existing in ('endometabolome') a cell has mostly been used to study the metabolism of particular microbes. Metabolomes also represent a picture of microbial activity and we suggest that the exometabolome may also contain pertinent information for studying microbial interaction networks. Gas chromatography coupled to mass spectrometry is the most commonly used technique in metabolomics studies. It allows a wide range of metabolites to be detected but requires the derivatisation of compounds prior to detection. This type of non-targeted analysis can introduce biases to the detection and quantification of the different metabolites, particularly at the extraction and derivatisation steps. The aims of this study, therefore, were to quantify the sources of variability and to test the sensitivity of the GC metabolic profiling approach to small environmental changes such as shifts in temperature. The temperature sensitivity of metabolic profiles was compared with that of catabolic profiles obtained using Biolog microplates. Analytical variability was compared with biological variability by incubating bacterial strains isolated from soil with fructose at 20 degrees C and by replicating each step of the protocol (incubation, extraction and derivatisation). For both the endo- and the exometabolome, more than 70% of the total variability was of biological origin and principal components analysis clearly separated the strains along the first ordination axis. The endometabolome distinguished bacterial strains at the species level only, whereas separation was evident at the species and group level with the exometabolome. Temperature had a significant but differential effect on the metabolite production of the bacterial strains whilst their catabolic profiles remained relatively unaffected. The exometabolome was more sensitive to temperature shifts than the endometabolome, suggesting that this pool may be of interest for studies in environmental functional ecology.     

Metabolic source isotopic pair labeling and genome-wide association are complementary tools for the identification of metabolite–gene associations in plants

The optimal extraction of information from untargeted metabolomics analyses is a continuing challenge. Here, we describe an approach that combines stable isotope labeling, liquid chromatography– mass spectrometry (LC–MS), and a computational pipeline to automatically identify metabolites produced from a selected metabolic precursor. We identified the subset of the soluble metabolome generated from phenylalanine (Phe) in Arabidopsis thaliana, which we refer to as the Phe-derived metabolome (FDM) In addition to identifying Phe-derived metabolites present in a single wild-type reference accession, the FDM was established in nine enzymatic and regulatory mutants in the phenylpropanoid pathway. To identify genes associated with variation in Phe-derived metabolites in Arabidopsis, MS features collected by untargeted metabolite profiling of an Arabidopsis diversity panel were retrospectively annotated to the FDM and natural genetic variants responsible for differences in accumulation of FDM features were identified by genome-wide association. Large differences in Phe-derived metabolite accumulation and presence/absence variation of abundant metabolites were observed in the nine mutants as well as between accessions from the diversity panel. Many Phe-derived metabolites that accumulated in mutants also accumulated in non-Col-0 accessions and was associated to genes with known or suspected functions in the phenylpropanoid pathway as well as genes with no known functions. Overall, we show that cataloguing a biochemical pathway’s products through isotopic labeling across genetic variants can substantially contribute to the identification of metabolites and genes associated with their biosynthesis.

An isotopic labeling and LC–MS pipeline to identify metabolites produced from Phe and its integration with genome-wide association identifies genes associated with the phenylpropanoid pathway.     

Chemical derivatization and mass spectral libraries in metabolic profiling by GC/MS and LC/MS/MS

An overview is presented of gas chromatography/mass spectrometry (GC/MS) and liquid chromatography/mass spectrometry (LC/MS), the two major hyphenated techniques employed in metabolic profiling that complement direct 'fingerprinting' methods such as atmospheric pressure ionization (API) quadrupole time-of-flight MS, API Fourier transform MS, and NMR. In GC/MS, the analytes are normally derivatized prior to analysis in order to reduce their polarity and facilitate chromatographic separation. The electron ionization mass spectra obtained are reproducible and suitable for library matching, mass spectral collections being readily available. In LC/MS, derivatization and library matching are at an early stage of development and mini-reviews are provided. Chemical derivatization can dramatically increase the sensitivity and specificity of LC/MS methods for less polar compounds and provides additional structural information. The potential of derivatization for metabolic profiling in LC/MS is demonstrated by the enhanced analysis of plant extracts, including the potential to measure volatile acids such as formic acid, difficult to achieve by GC/MS. The important role of mass spectral library creation and usage in these techniques is discussed and illustrated by examples.     

Metabolomics unveils urinary changes in subjects with metabolic syndrome following 12-week nut consumption

Through an HPLC-Q-TOF-MS-driven nontargeted metabolomics approach, we aimed to discriminate changes in the urinary metabolome of subjects with metabolic syndrome (MetS), following 12 weeks of mixed nuts consumption (30 g/day), compared to sex- and age-matched individuals given a control diet. The urinary metabolome corresponding to the nut-enriched diet clearly clustered in a distinct group, and the multivariate data analysis discriminated relevant mass features in this separation. Metabolites corresponding to the discriminating ions (MS features) were then subjected to multiple tandem mass spectrometry experiments using LC-ITD-FT-MS, to confirm their putative identification. The metabolomics approach revealed 20 potential markers of nut intake, including fatty acid conjugated metabolites, phase II and microbial-derived phenolic metabolites, and serotonin metabolites. An increased excretion of serotonin metabolites was associated for the first time with nut consumption. Additionally, the detection of urinary markers of gut microbial and phase II metabolism of nut polyphenols confirmed the understanding of their bioavailability and bioactivity as a priority area of research in the determination of the health effects derived from nut consumption. The results confirmed how a nontargeted metabolomics strategy may help to access unexplored metabolic pathways impacted by diet, thereby raising prospects for new intervention targets.     

Gas chromatography/mass spectrometry in metabolic profiling of biological fluids

One of the objectives of metabonomics is to identify subtle changes in metabolite profiles between biological systems of different physiological or pathological states. Gas chromatography mass spectrometry (GC/MS) is a widely used analytical tool for metabolic profiling in various biofluids, such as urine and blood due to its high sensitivity, peak resolution and reproducibility. The availability of the GC/MS electron impact (EI) spectral library further facilitates the identification of diagnostic biomarkers and aids the subsequent mechanistic elucidation of the biological or pathological variations. With the advent of new comprehensive two dimensional GC (GCxGC) coupled to time-of-flight mass spectrometry (TOFMS), it is possible to detect more than 1200 compounds in a single analytical run. In this review, we discuss the applications of GC/MS in the metabolic profiling of urine and blood, and discuss its advances in methodologies and technologies.     

The human cerebrospinal fluid metabolome

With continuing improvements in analytical technology and an increased interest in comprehensive metabolic profiling of biofluids and tissues, there is a growing need to develop comprehensive reference resources for certain clinically important biofluids, such as blood, urine and cerebrospinal fluid (CSF). As part of our effort to systematically characterize the human metabolome we have chosen to characterize CSF as the first biofluid to be intensively scrutinized. In doing so, we combined comprehensive NMR, gas chromatography-mass spectrometry (GC-MS) and liquid chromatography (LC) Fourier transform-mass spectrometry (FTMS) methods with computer-aided literature mining to identify and quantify essentially all of the metabolites that can be commonly detected (with today's technology) in the human CSF metabolome. Tables containing the compounds, concentrations, spectra, protocols and links to disease associations that we have found for the human CSF metabolome are freely available at http://www.csfmetabolome.ca.     

气相色谱-质谱联用技术及其在代谢组学中的应用

代谢组学是以高通量、高灵敏度、高分辨率的现代仪器分析方法为手段,对细胞、体液、组织中所有代谢物进行无偏向的定性与定量分析的一门学科。气相色谱-质谱联用技术具有较高的检测灵敏度和鉴定准确度,通过标准谱图库的比对可对代谢物进行快速的鉴定,因此被广泛应用于生物样品的代谢产物的检测中。文中对近年来气相色谱-质谱联用技术的发展以及在代谢组学研究中取得的成果进行了综述。首先介绍了气相色谱-质谱联用技术的分类和常用的样品衍生化方法;继而从样品预处理、定性与定量分析、数据分析三方面介绍了气相色谱-质谱联用技术分析代谢物的方法,并系统地对该技术在微生物、植物、疾病诊断领域的应用实例进行了评述;最后提出了当前气相色谱-质谱联用技术在代谢组学研究中存在的问题并对后续的研究进行了展望。     

Metabolite profiling of Arabidopsis seedlings in response to exogenous sinalbin and sulfur deficiency

In order to determine how plant uptake of a sulfur-rich secondary metabolite, sinalbin, affects the metabolic profile of sulfur-deficient plants, gas chromatography time-of-flight mass spectrometry (GC-TOF-MS), in combination with liquid chromatography-mass spectrometry (LC-MS), was used to survey the metabolome of Arabidopsis seedlings grown in nutrient media under different sulfur conditions. The growth media had either sufficient inorganic sulfur for normal plant growth or insufficient inorganic sulfur in the presence or absence of supplementation with organic sulfur in the form of sinalbin (p-hydroxybenzylglucosinolate). A total of 90 metabolites were identified by GC-TOF-MS and their levels were compared across the three treatments. Of the identified compounds, 21 showed similar responses in plants that were either sulfur deficient or sinalbin supplemented compared to sulfur-sufficient plants, while 12 metabolites differed in abundance only in sulfur-deficient plants. Twelve metabolites accumulated to higher levels in sinalbin-supplemented than in the sulfur-sufficient plants. Secondary metabolites such as flavonol conjugates, sinapinic acid esters and glucosinolates, were identified by LC-MS and their corresponding mass fragmentation patterns were determined. Under sinalbin-supplemented conditions, sinalbin was taken up by Arabidopsis and contributed to the endogenous formation of glucosinolates. Additionally, levels of flavonol glycosides and sinapinic acid esters increased while levels of flavonol diglycosides with glucose attached to the 3-position were reduced. The exogenously administered sinalbin resulted in inhibition of root and hypocotyl growth and markedly influenced metabolite profiles, compared to control and sulfur-deficient plants. These results indicate that, under sulfur deficient conditions, glucosinolates can be a sulfur source for plants. This investigation defines an opportunity to elucidate the mechanism of glucosinolate degradation in vivo.     

Ab initio prediction of metabolic networks using Fourier transform mass spectrometry data

Fourier transform mass spectrometry has recently been introduced into the field of metabolomics as a technique that enables the mass separation of complex mixtures at very high resolution and with ultra high mass accuracy. Here we show that this enhanced mass accuracy can be exploited to predict large metabolic networks ab initio, based only on the observed metabolites without recourse to predictions based on the literature. The resulting networks are highly information-rich and clearly non-random. They can be used to infer the chemical identity of metabolites and to obtain a global picture of the structure of cellular metabolic networks. This represents the first reconstruction of metabolic networks based on unbiased metabolomic data and offers a breakthrough in the systems-wide analysis of cellular metabolism.