Global metabolic profiling of Escherichia coli cultures: an evaluation of methods for quenching and extraction of intracellular metabolites

Metabolomics and systems biology require the acquisition of reproducible, robust, reliable, and homogeneous biological data sets. Therefore, we developed and validated standard operating procedures (SOPs) for quenching and efficient extraction of metabolites from Escherichia coli to determine the best methods to approach global analysis of the metabolome. E. coli was grown in chemostat culture so that cellular metabolism could be held in reproducible, steady-state conditions under a range of precisely defined growth conditions, thus enabling sufficient replication of samples. The metabolome profiles were generated using gas chromatography/time-of-flight mass spectrometry (GC/TOF-MS). We employed univariate and multivariate statistical analyses to determine the most suitable method. This investigation indicates that 60% cold (-48 degrees C) methanol solution is the most appropriate method to quench metabolism, and we recommend 100% methanol, also at -48 degrees C, with multiple freeze-thaw cycles for the extraction of metabolites. However, complementary extractions would be necessary for coverage of the entire complement of metabolites as detected by GC/TOF-MS. Finally, the observation that metabolite leakage was significant and measurable whichever quenching method is used indicates that methods should be incorporated into the experiment to facilitate the accurate quantification of intracellular metabolites.     

Capillary LC-MS for high sensitivity metabolomic analysis of single islets of Langerhans

Reversed-phase, packed capillary liquid chromatography interfaced by electrospray ionization to mass spectrometry was explored as an analytical method for determination of metabolites in microscale tissue samples using single islets of Langerhans as a model system. With the use of a 75 microm inner diameter column coupled to a quadrupole ion trap mass spectrometer in full scan mode, detection limits of 0.1-33 fmol were achieved for glycoloytic and tricarboxylic acid cycle metabolites. Reproducible processing of islets for analysis with little loss of metabolites was performed by rapid freezing followed by methanol-water extraction. The method yielded 20 microL of extract of which just 15 nL was injected suggesting the potential for performing multiple assays on the same islet. Approximately 200 presumed metabolites could be detected, of which 22 were identified by matching retention times and MS/MS spectra to standards. Relative standard deviations for peak detection was from 7 to 18% and was unaffected by storage for up to 11 days. The method was used to detect changes in metabolism associated with increasing extracellular islet glucose concentration from 3 to 20 mM yielding results largely consistent with known metabolism of islets. Because most previous studies of islet metabolism have only observed a few compounds at once and require far more tissue, this measurement method represents a significant advance for studies of metabolism of islets and other microscale samples.     

Differentiation of eight phenotypes and discovery of potential biomarkers for a single plant organ class using laser electrospray mass spectrometry and multivariate statistical analysis

Laser electrospray mass spectrometry (LEMS) coupled with offline multivariate statistical analysis is used to discriminate eight phenotypes from a single plant organ class and to find potential biomarkers. Direct analysis of the molecules from the flower petal is enabled by interfacing intense (10(13) W/cm(2)), nonresonant, femtosecond laser vaporization at ambient pressure with electrospray ionization for postionization of the vaporized analytes. The observed mass spectral signatures allowed for the discrimination of various phenotypes using principal component analysis (PCA) and either linear discriminant analysis (LDA) or K-nearest neighbor (KNN) classifiers. Cross-validation was performed using multiple training sets to evaluate the predictive ability of the classifiers, which showed 93.7% and 96.8% overall accuracies for the LDA and KNN classifiers, respectively. Linear combinations of significant mass spectral features were extracted from the PCA loading plots, demonstrating the capability to discover potential biomarkers from the direct analysis of tissue samples.     

Assessment of compatibility between extraction methods for NMR- and LC/MS-based metabolomics

Because of the wide range of chemically and structurally diverse metabolites, efforts to survey the complete metabolome rely on the implementation of multiplatform approaches based on nuclear magnetic resonance (NMR) and mass spectrometry (MS). Sample preparation disparities between NMR and MS, however, may limit the analysis of the same samples by both platforms. Specifically, deuterated solvents used in NMR strategies can complicate LC/MS analysis as a result of potential mass shifts, whereas acidic solutions typically used in LC/MS methods to enhance ionization of metabolites can severely affect reproducibility of NMR measurements. These intrinsically different sample preparation requirements result in the application of different procedures for metabolite extraction, which involve additional sample and unwanted variability. To address this issue, we investigated 12 extraction protocols in liver tissue involving different aqueous/organic solvents and temperatures that may satisfy the requirements for both NMR and LC/MS simultaneously. We found that deuterium exchange did not affect LC/MS results, enabling the measurement of metabolites by NMR and, subsequently, the direct analysis of the same samples by using LC/MS with no need for solvent exchange. Moreover, our results show that the choice of solvents rather than the temperature determined the extraction efficiencies of metabolites, a combination of methanol/chloroform/water and methanol/water being the extraction methods that best complement NMR and LC/MS analysis for metabolomic studies.     

Ambient molecular imaging and depth profiling of live tissue by infrared laser ablation electrospray ionization mass spectrometry

Mass spectrometry in conjunction with atmospheric pressure ionization methods enables the in vivo investigation of biochemical changes with high specificity and sensitivity. Laser ablation electrospray ionization (LAESI) is a recently introduced ambient ionization method suited for the analysis of biological samples with sufficient water content. With LAESI mass spectrometric analysis of chimeric Aphelandra squarrosa leaf tissue, we identify the metabolites characteristic for the green and yellow sectors of variegation. Significant parts of the related biosynthetic pathways (e.g., kaempferol biosynthesis) are ascertained from the detected metabolites and metabolomic databases. Scanning electron microscopy of the ablated areas indicates the feasibility of both two-dimensional imaging and depth profiling with a approximately 350 microm lateral and approximately 50 microm depth resolution. Molecular distributions of some endogenous metabolites show chemical contrast between the sectors of variegation and quantitative changes as the ablation reaches the epidermal and mesophyll layers. Our results demonstrate that LAESI mass spectrometry opens a new way for ambient molecular imaging and depth profiling of metabolites in biological tissues and live organisms.     

Annotation of the human adult urinary metabolome and metabolite identification using ultra high performance liquid chromatography coupled to a linear quadrupole ion trap-orbitrap mass spectrometer

Metabolic profiles of biofluids obtained by atmospheric pressure ionization mass spectrometry-based technologies contain hundreds to thousands of features, most of them remaining unknown or at least not characterized in analytical systems. We report here on the annotation of the human adult urinary metabolome and metabolite identification from electrospray ionization mass spectrometry (ESI-MS)-based metabolomics data sets. Features of biological interest were first of all annotated using the ESI-MS database of the laboratory. They were also grouped, thanks to software tools, and annotated using public databases. Metabolite identification was achieved using two complementary approaches: (i) formal identification by matching chromatographic retention times, mass spectra, and also product ion spectra (if required) of metabolites to be characterized in biological data sets to those of reference compounds and (ii) putative identification from biological data thanks to MS/MS experiments for metabolites not available in our chemical library. By these means, 384 metabolites corresponding to 1484 annotated features (659 in negative ion mode and 825 in positive ion mode) were characterized in human urine samples. Of these metabolites, 192 and 66 were formally and putatively identified, respectively, and 54 are reported in human urine for the first time. These lists of features could be used by other laboratories to annotate their ESI-MS metabolomics data sets.     

Metabolite projection analysis for fast identification of metabolites in metabonomics. Application in an amiodarone study

The assignment of significantly changed NMR signals, which were identified with the help of multivariate models, to individual metabolites in biofluids is a manual and tedious task requiring knowledge in chemometrics and NMR spectroscopy. Metabolite projection analysis, introduced in this work, allows automatic linking of multivariate models with metabolites by skipping the level of manual NMR signal identification. The method depends on the projection of sets of metabolite NMR spectra from a database into PCA or PLS models of NMR spectra of biofluid samples. Metabolites that are significantly changed can be identified graphically in metabolite projection plots or numerically as projected virtual concentration. The method is demonstrated together with a newly introduced algorithm for refined nonequidistant binning using a metabonomics study with amiodarone as administered drug. Amiodarone can induce phospholipidosis in the lung and liver, which is accompanied by associated organ toxicity in these organs. It is shown how metabolite projection analysis allows easy and fast tentative assignment of all structures of metabolites whose concentrations in the urine samples significantly changed upon dosage. These metabolites had also been identified previously by manually interpreting the multivariate models and spectra. Among these metabolites, phenylacetylglycine was also identified as being significantly increased. This metabolite has recently been proposed as urinary biomarker for phospholipidosis.     

In situ molecular analysis of plant tissues by live single-cell mass spectrometry

We report the development of a rapid, direct molecular analysis of live, single plant cells viewed under a video microscope in their natural environment. A nanoelectrospray tip was used to extract the contents of a single leaf, stem, or petal cell from Pelargonium zonale, and the samples were analyzed on an Orbitrap mass spectrometer by nanoelectrospray ionization. Around a thousand m/z peaks belonging to metabolites and other compounds in each sample were obtained and processed by using statistical tools to find the cell specific molecular peaks. Hybrid high-resolution mass spectrometry analysis was performed to confirm the structure of specific metabolites from the analyzed samples. This method is useful for identifying specific molecules in live single cells from plant tissue and will allow different cell types and stages from different sites in the plant to be compared with morphological observations.     

Analyzing the physiological signature of anabolic steroids in cattle urine using pyrolysis/metastable atom bombardment mass spectrometry and pattern recognition

Pyrolysis coupled to metastable atom bombardment (MAB) and time-of-flight mass spectrometry (TOFMS) is used for generating mass spectra from bovine urine samples obtained from cattle treated with anabolic steroids. These spectra constitute fingerprints, which can be discriminated by multivariate statistical analysis. Four main conclusions can be drawn from this work: (i) The use of different metastable gases, such as Xe*, Kr*, or N2*, as an energy-tunable ionization beamline allows control of the internal energy and the dissociation processes of the produced odd electron molecular ions, thus giving rise to complementary mass spectra fingerprints. (ii) A variable transformation depending on the biofluid matrix suitably contracts the frequency distribution of the generated data for low m/z ratios holding information related to endogenous metabolites encountered in urine. (iii) Coupling variable selection to statistical pattern recognition methods results in low error rates (< 1%) for predicting MAB mass fingerprints, especially using lineardiscriminant analysis (LDA). (iv) LDA discriminates controls from treated animals and also correlates to quantitative physiological responses induced by anabolic steroids. This work shows that Py-MAB-TOFMS could be a suitable method for complementary monitoring anabolic use in sports, medicine, and cattle breeding, as well as monitoring many other long-lasting although weak physiological disruptions.     

Building multivariate systems biology models

Systems biology methods using large-scale "omics" data sets face unique challenges: integrating and analyzing near limitless data space, while recognizing and removing systematic variation or noise. Herein we propose a complementary multivariate analysis workflow to both integrate "omics" data from disparate sources and analyze the results for specific and unique sample correlations. This workflow combines principal component analysis (PCA), orthogonal projections to latent structures discriminate analysis (OPLS-DA), orthogonal 2 projections to latent structures (O2PLS), and shared and unique structures (SUS) plots. The workflow is demonstrated using data from a study in which ApoE3Leiden mice were fed an atherogenic diet consisting of increasing cholesterol levels followed by therapeutic intervention (fenofibrate, rosuvastatin, and LXR activator T-0901317). The levels of structural lipids (lipidomics) and free fatty acids in liver were quantified via liquid chromatography-mass spectrometry (LC-MS). The complementary workflow identified diglycerides as key hepatic metabolites affected by dietary cholesterol and drug intervention. Modeling of the three therapeutics for mice fed a high-cholesterol diet further highlighted diglycerides as metabolites of interest in atherogenesis, suggesting a role in eliciting chronic liver inflammation. In particular, O2PLS-based SUS2 plots showed that treatment with T-0901317 or rosuvastatin returned the diglyceride profile in high-cholesterol-fed mice to that of control animals.     

Analysis of organoselenium and organic acid metabolites by laser desorption single photon ionization mass spectrometry

A method for analyzing organoselenium and organic acid metabolites using laser desorption from graphite surfaces coupled to vacuum ultraviolet single photon ionization mass spectrometry (LD/SPI MS) is described. The 1-10-fmol sensitivity and linear dynamic range allows quantitative detection of selenomethionine, trimethylselenonium ion, methylselenogalactosamine, and 1beta-methylseleno-N-acetyl-D-galactosamine in complex biological samples such as human urine. In addition, common urinary metabolites such as tartronic, glutaric, orotic, uric, suberic, and hydroxyhippuric acids, are readily detected. Screening and quantitative detection of these organoselenium and organic acid metabolites is achieved within minutes. The results are also consistent with those obtained using high-performance liquid chromatography tandem mass spectrometry techniques. The study demonstrates the viability of matrix-free LD/SPI MS for molecular characterization and quantitative analysis of biological metabolites in the m/z 10-500 range that are present in complex biological fluids.     

A strategy for identifying differences in large series of metabolomic samples analyzed by GC/MS

In metabolomics, the purpose is to identify and quantify all the metabolites in a biological system. Combined gas chromatography and mass spectrometry (GC/MS) is one of the most commonly used techniques in metabolomics together with 1H NMR, and it has been shown that more than 300 compounds can be distinguished with GC/MS after deconvolution of overlapping peaks. To avoid having to deconvolute all analyzed samples prior to multivariate analysis of the data, we have developed a strategy for rapid comparison of nonprocessed MS data files. The method includes baseline correction, alignment, time window determinations, alternating regression, PLS-DA, and identification of retention time windows in the chromatograms that explain the differences between the samples. Use of alternating regression also gives interpretable loadings, which retain the information provided by m/z values that vary between the samples in each retention time window. The method has been applied to plant extracts derived from leaves of different developmental stages and plants subjected to small changes in day length. The data show that the new method can detect differences between the samples and that it gives results comparable to those obtained when deconvolution is applied prior to the multivariate analysis. We suggest that this method can be used for rapid comparison of large sets of GC/MS data, thereby applying time-consuming deconvolution only to parts of the chromatograms that contribute to explain the differences between the samples.     

Simultaneous determination of anionic intermediates for Bacillus subtilis metabolic pathways by capillary electrophoresis electrospray ionization mass spectrometry

A method for simultaneous determination of anionic metabolites based on capillary electrophoresis (CE) coupled to electrospray ionization mass spectrometry is described. To prevent current drop by the system, electroosmotic flow (EOF) reversal by using a cationic polymer-coated capillary was indispensable. A mixture containing 32 standards including carboxylic acids, phosphorylated carboxylic acids, phosphorylated saccharides, nucleotides, and nicotinamide and flavin adenine coenzymes of glycolysis and the tricarboxylic acid cycle pathways were separated by CE and selectively detected by a quadrupole mass spectrometer with a sheath-flow electrospray ionization interface. Key to the analysis was EOF reversal using a cationic polymer-coated capillary and an electrolyte system consisting of 50 mM ammonium acetate, pH 9.0. The relative standard deviations of the method were better than 0.4% for migration times and between 0.9% and 5.4% for peak areas. The concentration detection limits for these metabolites were between 0.3 and 6.7 micromol/L with pressure injection of 50 mbar for 30 s (30 nL); i.e., mass detection limits ranged from 9 to 200 fmol, at a signal-to-noise ratio of 3. This method was applied to the comprehensive analysis of metabolic intermediates extracted from Bacillus subtilis, and 27 anionic metabolites could be directly detected and quantified.     

Mass spectrometry-based metabolomics: accelerating the characterization of discriminating signals by combining statistical correlations and ultrahigh resolution

A strategy combining autocorrelation matrices and ultrahigh resolution mass spectrometry (MS) was developed to optimize the characterization of discriminating ions highlighted by metabolomics. As an example, urine samples from rats treated with phenobarbital (PB) were analyzed by ultrahigh-pressure chromatography with two different eluting conditions coupled to time-of-flight mass spectrometric detection in both the positive and negative electrospray ionization modes. Multivariate data analyses were performed to highlight discriminating variables from several thousand detected signals: a few hundred signals were found to be affected by PB, whereas a few tenths of them were linked to its metabolism. Autocorrelation matrices were then applied to eliminate adduct and fragment ions. Finally, the characterization of the ions of interest was performed with ultrahigh-resolution mass spectrometry and sequential MS(n) experiments, by using a LC-LTQ-Orbitrap system. The use of different eluting conditions was shown to drastically impact on the chromatographic retention and ionization of compounds, thus providing a way to obtain more exhaustive metabolic fingerprints, whereas autocorrelation matrices allowed one to focus the identification work on the most relevant ions. By using such an approach, 14 PB metabolites were characterized in rat urines, some of which have not been reported in the literature.     

Analysis of amino acid isotopomers using FT-ICR MS

Fluxes through known metabolic pathways and the presence of novel metabolic reactions are often determined by feeding isotopically labeled substrate to an organism and then determining the isotopomer distribution in amino acids in proteins. However, commonly used techniques to measure the isotopomer distributions require derivatization prior to analysis (gas chromatography/mass spectrometry (GC/MS)) or large sample sizes (nuclear magnetic resonance (NMR) spectroscopy). Here, we demonstrate the use of Fourier transform-ion cyclotron resonance mass spectrometry with direct infusion via electrospray ionization to rapidly measure the amino acid isotopomer distribution in a biomass hydrolysate of the soil bacterium Desulfovibrio vulgaris Hildenborough. By applying high front-end resolution for the precursor ion selection followed by sustained off-resonance irradiation collision-induced dissociation, it was possible to determine exactly and unambiguously the specific locations of the labeled atoms in the amino acids, which usually requires a combination of 2-D 13C NMR spectroscopy and GC/MS. This method should be generally applicable to all biomass samples and will allow more accurate determination of metabolic fluxes with less work and less sample.     

Method for determining molar concentrations of metabolites in complex solutions from two-dimensional 1H-13C NMR spectra

One-dimensional (1D) (1)H nuclear magnetic resonance (NMR) spectroscopy is used extensively for high-throughput analysis of metabolites in biological fluids and tissue extracts. Typically, such spectra are treated as multivariate statistical objects rather than as collections of quantifiable metabolites. We report here a two-dimensional (2D) (1)H-(13)C NMR strategy (fast metabolite quantification, FMQ, by NMR) for identifying and quantifying the approximately 40 most abundant metabolites in biological samples. To validate this technique, we prepared mixtures of synthetic compounds and extracts from Arabidopsis thaliana, Saccharomyces cerevisiae, and Medicago sativa. We show that accurate (technical error 2.7%) molar concentrations can be determined in 12 min using our quantitative 2D (1)H-(13)C NMR strategy. In contrast, traditional 1D (1)H NMR analysis resulted in 16.2% technical error under nearly ideal conditions. We propose FMQ by NMR as a practical alternative to 1D (1)H NMR for metabolomics studies in which 50-mg (extract dry weight) samples can be obtained.     

In situ cell-by-cell imaging and analysis of small cell populations by mass spectrometry

Molecular imaging by mass spectrometry (MS) is emerging as a tool to determine the distribution of proteins, lipids, and metabolites in tissues. The existing imaging methods, however, mostly rely on predefined rectangular grids for sampling that ignore the natural cellular organization of the tissue. Here we demonstrate that laser ablation electrospray ionization (LAESI) MS can be utilized for in situ cell-by-cell imaging of plant tissues. The cell-by-cell molecular image of the metabolite cyanidin, the ion responsible for purple pigmentation in onion (Allium cepa) epidermal cells, correlated well with the color of cells in the tissue. Chemical imaging using single-cells as voxels reflects the spatial distribution of biochemical differences within a tissue without the distortion stemming from sampling multiple cells within the laser focal spot. Microsampling by laser ablation also has the benefit of enabling the analysis of very small cell populations for biochemical heterogeneity. For example, with a ～30 μm ablation spot we were able to analyze 3-4 achlorophyllous cells within an oil gland on a sour orange (Citrus aurantium) leaf. To explore cell-to-cell variations within and between tissues, multivariate statistical analysis on LAESI-MS data from epidermal cells of an A. cepa bulb and a C. aurantium leaf and from human buccal epithelial cell populations was performed using the method of orthogonal projections to latent structures discriminant analysis (OPLS-DA). The OPLS-DA analysis of mass spectra, containing over 300 peaks each, provided guidance in identifying a small number of metabolites most responsible for the variance between the cell populations. These metabolites can be viewed as promising candidates for biomarkers that, however, require further verification.     

Speciation and identification of organoselenium metabolites in human urine using inductively coupled plasma mass spectrometry and tandem mass spectrometry

A method for speciation and identification of organoselenium metabolites found in human urine samples using high performance liquid chromatography/inductively coupled plasma mass spectrometry (HPLC/ICP-MS) and tandem mass spectrometry (MS/MS) is described. Reversed-phase chromatographic separation was used for sample fractionation with the ICP-MS functioning as an element-selective detector, and six distinct selenium-containing species were detected in a human urine sample. Fractions were then collected and analyzed using a triple quadrupole mass spectrometer with electrospray ionization and collision-induced dissociation to obtain structural information. The first two fractions were identified specifically as selenomethionine and selenocystamine, estimated to be present at approximately 11 and 40 ppb, respectively. To the best of our knowledge, this is the first time these two metabolites have been positively identified in human urine.     

A liquid chromatography--tandem mass spectrometry multiresidue method for quantification of specific metabolites of organophosphorus pesticides, synthetic pyrethroids, selected herbicides, and deet in human urine

The ability to estimate low-dose human exposure to commonly used pesticides often is requested in epidemiologic studies. Therefore, fast and robust methods are necessary that can measure many analytes in the same sample. We have developed a method for high-throughput analysis of 19 markers of commonly used pesticides in human urine. The analytes were seven specific metabolites of organophosphorus pesticides, five metabolites of synthetic pyrethroids, six herbicides or their metabolites, and one insect repellant. Human urine (2 mL) was spiked with stable isotopically labeled analogues of the analytes, enzymatically hydrolyzed, extracted using solid-phase extraction, concentrated, and analyzed using high-performance liquid chromatography-tandem mass spectrometry. The sample was divided into two portions and analyzed on two different mass spectrometers, one using atmospheric pressure chemical ionization (APCI) and the other using turbo ion spray atmospheric pressure ionization (TIS). All analytes except the pyrethroid metabolites were analyzed using APCI. The detection limits for all analytes ranged from 0.1 to 1.5 ng/mL of urine, with the majority (17) below 0.5 ng/mL. The analytical precision for the different analytes, estimated as both the within-day and between-day variation, was 3-14 and 4-19%, respectively. The extraction recoveries of the analytes ranged from 68 to 114%. The throughput, including calibration standards and quality control samples, is approximately 50 samples a day. However, the analysis time with the TIS application is much shorter, and if only pyrethroid metabolite data are of interest, the throughput can be increased to 100-150 samples/day.