Automated combustion accelerator mass spectrometry for the analysis of biomedical samples in the low attomole range.

The increasing role of accelerator mass spectrometry (AMS) in biomedical research necessitates modernization of the traditional sample handling process. AMS was originally developed and used for carbon dating, therefore focusing on a very high precision but with a comparably low sample throughput. Here, we describe the combination of automated sample combustion with an elemental analyzer (EA) online coupled to an AMS via a dedicated interface. This setup allows direct radiocarbon measurements for over 70 samples daily by AMS. No sample processing is required apart from the pipetting of the sample into a tin foil cup, which is placed in the carousel of the EA. In our system, up to 200 AMS analyses are performed automatically without the need for manual interventions. We present results on the direct total (14)C count measurements in <2 µL human plasma samples. The method shows linearity over a range of 0.65-821 mBq/mL, with a lower limit of quantification of 0.65 mBq/mL (corresponding to 0.67 amol for acetaminophen). At these extremely low levels of activity, it becomes important to quantify plasma specific carbon percentages. This carbon percentage is automatically generated upon combustion of a sample on the EA. Apparent advantages of the present approach include complete omission of sample preparation (reduced hands-on time) and fully automated sample analysis. These improvements clearly stimulate the standard incorporation of microtracer research in the drug development process. In combination with the particularly low sample volumes required and extreme sensitivity, AMS strongly improves its position as a bioanalysis method.

Identifying inhibitory compounds in lignocellulosic biomass hydrolysates using an exometabolomics approach.

BACKGROUND: Inhibitors are formed that reduce the fermentation performance of fermenting yeast during the pretreatment process of lignocellulosic biomass. An exometabolomics approach was applied to systematically identify inhibitors in lignocellulosic biomass hydrolysates. RESULTS: We studied the composition and fermentability of 24 different biomass hydrolysates. To create diversity, the 24 hydrolysates were prepared from six different biomass types, namely sugar cane bagasse, corn stover, wheat straw, barley straw, willow wood chips and oak sawdust, and with four different pretreatment methods, i.e. dilute acid, mild alkaline, alkaline/peracetic acid and concentrated acid. Their composition and that of fermentation samples generated with these hydrolysates were analyzed with two GC-MS methods. Either ethyl acetate extraction or ethyl chloroformate derivatization was used before conducting GC-MS to prevent sugars are overloaded in the chromatograms, which obscure the detection of less abundant compounds. Using multivariate PLS-2CV and nPLS-2CV data analysis models, potential inhibitors were identified through establishing relationship between fermentability and composition of the hydrolysates. These identified compounds were tested for their effects on the growth of the model yeast, Saccharomyces. cerevisiae CEN.PK 113-7D, confirming that the majority of the identified compounds were indeed inhibitors. CONCLUSION: Inhibitory compounds in lignocellulosic biomass hydrolysates were successfully identified using a non-targeted systematic approach: metabolomics. The identified inhibitors include both known ones, such as furfural, HMF and vanillin, and novel inhibitors, namely sorbic acid and phenylacetaldehyde.

Automated pipeline for de novo metabolite identification using mass-spectrometry-based metabolomics.

Metabolite identification is one of the biggest bottlenecks in metabolomics. Identifying human metabolites poses experimental, analytical, and computational challenges. Here we present a pipeline of previously developed cheminformatic tools and demonstrate how it facilitates metabolite identification using solely LC/MS(n) data. These tools process, annotate, and compare MS(n) data, and propose candidate structures for unknown metabolites either by identity assignment of identical mass spectral trees or by de novo identification using substructures of similar trees. The working and performance of this metabolite identification pipeline is demonstrated by applying it to LC/MS(n) data of urine samples. From human urine, 30 MS(n) trees of unknown metabolites were acquired, processed, and compared to a reference database containing MS(n) data of known metabolites. From these 30 unknowns, we could assign a putative identity for 10 unknowns by finding identical fragmentation trees. For 11 unknowns no similar fragmentation trees were found in the reference database. On the basis of elemental composition only, a large number of candidate structures/identities were possible, so these unknowns remained unidentified. The other 9 unknowns were also not found in the database, but metabolites with similar fragmentation trees were retrieved. Computer assisted structure elucidation was performed for these 9 unknowns: for 4 of them we could perform de novo identification and propose a limited number of candidate structures, and for the other 5 the structure generation process could not be constrained far enough to yield a small list of candidates. The novelty of this work is that it allows de novo identification of metabolites that are not present in a database by using MS(n) data and computational tools. We expect this pipeline to be the basis for the computer-assisted identification of new metabolites in future metabolomics studies, and foresee that further additions will allow the identification of even a larger fraction of the unknown metabolites.

Pre-processing liquid chromatography/high-resolution mass spectrometry data: extracting pure mass spectra by deconvolution from the invariance of isotopic distribution.

RATIONALE: Mass spectra obtained by deconvolution of liquid chromatography/high-resolution mass spectrometry (LC/HRMS) data can be impaired by non-informative mass-over-charge (m/z) channels. This impairment of mass spectra can have significant negative influence on further post-processing, like quantification and identification. METHODS: A metric derived from the knowledge of errors in isotopic distribution patterns, and quality of the signal within a pre-defined mass chromatogram block, has been developed to pre-select all informative m/z channels. RESULTS: This procedure results in the clean-up of deconvoluted mass spectra by maintaining the intensity counts from m/z channels that originate from a specific compound/molecular ion, for example, molecular ion, adducts, (13) C-isotopes, multiply charged ions and removing all m/z channels that are not related to the specific peak. The methodology has been successfully demonstrated for two sets of high-resolution LC/MS data. CONCLUSIONS: The approach described is therefore thought to be a useful tool in the automatic processing of LC/HRMS data. It clearly shows the advantages compared to other approaches like peak picking and de-isotoping in the sense that all information is retained while non-informative data is removed automatically.

The intracellular galactoglycome in Trichoderma reesei during growth on lactose.

Lactose (1,4-0-ß-D-galactopyranosyl-D-glucose) is used as a soluble carbon source for the production of cellulases and hemicellulases for-among other purposes-use in biofuel and biorefinery industries. The mechanism how lactose induces cellulase formation in T. reesei is enigmatic, however. Previous results from our laboratory raised the hypothesis that intermediates from the two galactose catabolic pathway may give rise to the accumulation of intracellular oligogalactosides that could act as inducer. Here we have therefore used high-performance anion-exchange chromatography-mass spectrometry to study the intracellular galactoglycome of T. reesei during growth on lactose, in T. reesei mutants impaired in galactose catabolism, and in strains with different cellulase productivities. Lactose, allo-lactose, and lactulose were detected in the highest amounts in all strains, and two trisaccharides (Gal-ß-1,6-Gal-ß-1,4-Glc/Fru and Gal-ß-1,4-Gal-ß-1,4-Glc/Fru) also accumulated to significant levels. Glucose and galactose, as well as four further oligosaccharides (Gal-ß-1,3/1,4/1,6-Gal; Gal-ß-1,2-Glc) were only detected in minor amounts. In addition, one unknown disaccharide (Hex-ß-1,1-Hex) and four trisaccharides were also detected. The accumulation of the unknown hexose disaccharide was shown to correlate with cellulase formation in the improved mutant strains as well as the galactose pathway mutants, and Gal-ß-1,4-Gal-ß-1,4-Glc/Fru and two other unknown hexose trisaccharides correlated with cellulase production only in the pathway mutants, suggesting that these compounds could be involved in cellulase induction by lactose. The nature of these oligosaccharides, however, suggests their formation by transglycosylation rather than by glycosyltransferases. Based on our results, the obligate nature of both galactose catabolic pathways for this induction must have another biochemical basis than providing substrates for inducer formation.

Quantitative proteomics and metabolomics analysis of normal human cerebrospinal fluid samples.

The analysis of cerebrospinal fluid (CSF) is used in biomarker discovery studies for various neurodegenerative central nervous system (CNS) disorders. However, little is known about variation of CSF proteins and metabolites between patients without neurological disorders. A baseline for a large number of CSF compounds appears to be lacking. To analyze the variation in CSF protein and metabolite abundances in a number of well-defined individual samples of patients undergoing routine, non-neurological surgical procedures, we determined the variation of various proteins and metabolites by multiple analytical platforms. A total of 126 common proteins were assessed for biological variations between individuals by ESI-Orbitrap. A large spread in inter-individual variation was observed (relative standard deviations [RSDs] ranged from 18 to 148%) for proteins with both high abundance and low abundance. Technical variation was between 15 and 30% for all 126 proteins. Metabolomics analysis was performed by means of GC-MS and nuclear magnetic resonance (NMR) imaging and amino acids were specifically analyzed by LC-MS/MS, resulting in the detection of more than 100 metabolites. The variation in the metabolome appears to be much more limited compared with the proteome: the observed RSDs ranged from 12 to 70%. Technical variation was less than 20% for almost all metabolites. Consequently, an understanding of the biological variation of proteins and metabolites in CSF of neurologically normal individuals appears to be essential for reliable interpretation of biomarker discovery studies for CNS disorders because such results may be influenced by natural inter-individual variations. Therefore, proteins and metabolites with high variation between individuals ought to be assessed with caution as candidate biomarkers because at least part of the difference observed between the diseased individuals and the controls will not be caused by the disease, but rather by the natural biological variation between individuals.

New figures of merit for comprehensive functional genomics data: the metabolomics case.

In the field of metabolomics, hundreds of metabolites are measured simultaneously by analytical platforms such as gas chromatography/mass spectrometry (GC/MS), liquid chromatography/mass spectrometry (LC/MS) and NMR to obtain their concentration levels in a reliable way. Analytical repeatability (intrabatch precision) is a common figure of merit for the measurement error of metabolites repeatedly measured in one batch on one platform. This measurement error, however, is not constant as its value may depend on the concentration level of the metabolite. Moreover, measurement errors may be correlated between metabolites. In this work, we introduce new figures of merit for comprehensive measurements that can detect these nonconstant correlated errors. Furthermore, for the metabolomics case we identified that these nonconstant correlated errors can result from sample instability between repeated analyses, instrumental noise generated by the analytical platform, or bias that results from data pretreatment.

Metabolomics as a tool for target identification in strain improvement: the influence of phenotype definition.

For the optimization of microbial production processes, the choice of the quantitative phenotype to be optimized is crucial. For instance, for the optimization of product formation, either product concentration or productivity can be pursued, potentially resulting in different targets for strain improvement. The choice of a quantitative phenotype is highly relevant for classical improvement approaches, and even more so for modern systems biology approaches. In this study, the information content of a metabolomics dataset was determined with respect to different quantitative phenotypes related to the formation of specific products. To this end, the production of two industrially relevant products by Aspergillus niger was evaluated: (i) the enzyme glucoamylase, and (ii) the more complex product group of secreted proteases, consisting of multiple enzymes. For both products, six quantitative phenotypes associated with activity and productivity were defined, also taking into account different time points of sampling during the fermentation. Both linear and nonlinear relationships between the metabolome data and the different quantitative phenotypes were considered. The multivariate data analysis tool partial least-squares (PLS) was used to evaluate the information content of the datasets for all the different quantitative phenotypes defined. Depending on the product studied, different quantitative phenotypes were found to have the highest information content in specific metabolomics datasets. A detailed analysis of the metabolites that showed strong correlation with these quantitative phenotypes revealed that various sugar derivatives correlated with glucoamylase activity. For the reduction of protease activity, mainly as-yet-unidentified compounds correlated.

The impact of delayed storage on the measured proteome and metabolome of human cerebrospinal fluid.

BACKGROUND: Because cerebrospinal fluid (CSF) is in close contact with diseased areas in neurological disorders, it is an important source of material in the search for molecular biomarkers. However, sample handling for CSF collected from patients in a clinical setting might not always be adequate for use in proteomics and metabolomics studies. METHODS: We left CSF for 0, 30, and 120 min at room temperature immediately after sample collection and centrifugation/removal of cells. At 2 laboratories CSF proteomes were subjected to tryptic digestion and analyzed by use of nano-liquid chromatography (LC) Orbitrap mass spectrometry (MS) and chipLC quadrupole TOF-MS. Metabolome analysis was performed at 3 laboratories by NMR, GC-MS, and LC-MS. Targeted analyses of cystatin C and albumin were performed by LC-tandem MS in the selected reaction monitoring mode. RESULTS: We did not find significant changes in the measured proteome and metabolome of CSF stored at room temperature after centrifugation, except for 2 peptides and 1 metabolite, 2,3,4-trihydroxybutanoic (threonic) acid, of 5780 identified peptides and 93 identified metabolites. A sensitive protein stability marker, cystatin C, was not affected. CONCLUSIONS: The measured proteome and metabolome of centrifuged human CSF is stable at room temperature for up to 2 hours. We cannot exclude, however, that changes undetectable with our current methodology, such as denaturation or proteolysis, might occur because of sample handling conditions. The stability we observed gives laboratory personnel at the collection site sufficient time to aliquot samples before freezing and storage at -80 °C.

Effect of bioprocessing of wheat bran in wholemeal wheat breads on the colonic SCFA production in vitro and postprandial plasma concentrations in men.

The health benefits of whole grain consumption can be partly attributed to the inclusion of the bran or outer-layers of the grain rich in dietary fibre. Fibre is fermented in the colon, leading to the production of beneficial metabolites, such as short-chain fatty acids (SCFA). The effect of five different types of bread on the SCFA production was studied in an in vitro model of human colon. Additionally, the postprandial effects of two selected breads on the SCFA plasma concentrations were investigated in men. A higher in vitro production of butyrate was induced by wholemeal wheat bread with bioprocessed bran than by native bran. The increase in butyrate seemed to be in exchange for propionate, whilst the total SCFA production remained similar. However, differences between the two breads in the postprandial butyrate concentrations could not be detected in peripheral blood of men, probably due to an effective utilisation by colonocytes.

Metabolic profiling of ultrasmall sample volumes with GC/MS: from microliter to nanoliter samples.

Profiling of metabolites is increasingly used to study the functioning of biological systems. For some studies the volume of available samples is limited to only a few microliters or even less, for fluids such as cerebrospinal fluid (CSF) of small animals like mice or the analysis of individual oocytes. Here we present an analytical method using in-liner silylation coupled to gas chromatography/mass spectrometry (GC/MS), that is suitable for metabolic profiling in ultrasmall sample volumes of 2 microL down to 10 nL. Method performance was assessed in various biosamples. Derivatization efficiencies for sugars, organic acids, and amino acids were satisfactory (105-120%), and repeatabilities were generally better than 15%, except for amino acids that had repeatabilities up to about 35-40%. For endogenous sugars and organic acids in fetal bovine serum, the response was linear for aliquots from 10 nL up to at least 1 microL. The developed GC/MS method was applied for the analysis of different sample matrixes, i.e., fetal bovine serum, mouse CSF, and aliquots of the intracellular content of Xenopus laevis oocytes. To the best of our knowledge, we present here the first comprehensive GC/MS metabolite profiles from mouse CSF and from the intracellular content of a single X. laevis oocyte.

Effects of organically and conventionally produced feed on biomarkers of health in a chicken model.

Consumers expect organic products to be healthier. However, limited research has been performed to study the effect of organic food on health. The present study aimed to identify biomarkers of health to enable future studies in human subjects. A feeding experiment was performed in two generations of three groups of chickens differing in immune responsiveness, which were fed identically composed feeds from either organic or conventional produce. The animals of the second generation were exposed to an immune challenge and sacrificed at 13 weeks of age. Feed and ingredients were analysed on macro- and micronutrients, i.e. vitamins, minerals, trace elements, heavy metals and microbes. The chickens were studied by general health and immune parameters, metabolomics, genomics and post-mortem evaluation. The organic and conventional feeds were comparable with respect to metabolisable energy. On average, the conventionally produced feeds had a 10 % higher protein content and some differences in micronutrients were observed. Although animals on both feeds were healthy, differences between the groups were found. The random control group of chickens fed conventional feed showed overall a higher weight gain during life span than the group on organic feed, although feed intake was mostly comparable. The animals on organic feed showed an enhanced immune reactivity, a stronger reaction to the immune challenge as well as a slightly stronger 'catch-up growth' after the challenge. Biomarkers for future research were identified in the parameters feed intake, body weight and growth rate, and in immunological, physiological and metabolic parameters, several of these differing most pronounced after the challenge.

The effect of preanalytical factors on stability of the proteome and selected metabolites in cerebrospinal fluid (CSF).

To standardize the use of cerebrospinal fluid (CSF) for biomarker research, a set of stability studies have been performed on porcine samples to investigate the influence of common sample handling procedures on proteins, peptides, metabolites and free amino acids. This study focuses at the effect on proteins and peptides, analyzed by applying label-free quantitation using microfluidics nanoscale liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (chipLC-MS) as well as matrix-assisted laser desorption ionization Fourier transform ion cyclotron resonance mass spectrometry (MALDI-FT-ICR-MS) and Orbitrap LC-MS/MS to trypsin-digested CSF samples. The factors assessed were a 30 or 120 min time delay at room temperature before storage at -80 degrees C after the collection of CSF in order to mimic potential delays in the clinic (delayed storage), storage at 4 degrees C after trypsin digestion to mimic the time that samples remain in the cooled autosampler of the analyzer, and repeated freeze-thaw cycles to mimic storage and handling procedures in the laboratory. The delayed storage factor was also analyzed by gas chromatography mass spectrometry (GC-MS) and liquid chromatography mass spectrometry (LC-MS) for changes of metabolites and free amino acids, respectively. Our results show that repeated freeze/thawing introduced changes in transthyretin peptide levels. The trypsin digested samples left at 4 degrees C in the autosampler showed a time-dependent decrease of peak areas for peptides from prostaglandin D-synthase and serotransferrin. Delayed storage of CSF led to changes in prostaglandin D-synthase derived peptides as well as to increased levels of certain amino acids and metabolites. The changes of metabolites, amino acids and proteins in the delayed storage study appear to be related to remaining white blood cells. Our recommendations are to centrifuge CSF samples immediately after collection to remove white blood cells, aliquot, and then snap-freeze the supernatant in liquid nitrogen for storage at -80 degrees C. Preferably samples should not be left in the autosampler for more than 24 h and freeze/thaw cycles should be avoided if at all possible.

Quantitative profiling of bile acids in biofluids and tissues based on accurate mass high resolution LC-FT-MS: compound class targeting in a metabolomics workflow.

We report a sensitive, generic method for quantitative profiling of bile acids and other endogenous metabolites in small quantities of various biological fluids and tissues. The method is based on a straightforward sample preparation, separation by reversed-phase high performance liquid-chromatography mass spectrometry (HPLC-MS) and electrospray ionisation in the negative ionisation mode (ESI-). Detection is performed in full scan using the linear ion trap Fourier transform mass spectrometer (LTQ-FTMS) generating data for many (endogenous) metabolites, not only bile acids. A validation of the method in urine, plasma and liver was performed for 17 bile acids including their taurine, sulfate and glycine conjugates. The method is linear in the 0.01-1 microM range. The accuracy in human plasma ranges from 74 to 113%, in human urine 77 to 104% and in mouse liver 79 to 140%. The precision ranges from 2 to 20% for pooled samples even in studies with large number of samples (n>250). The method was successfully applied to a multi-compartmental APOE*3-Leiden mouse study, the main goal of which was to analyze the effect of increasing dietary cholesterol concentrations on hepatic cholesterol homeostasis and bile acid synthesis. Serum and liver samples from different treatment groups were profiled with the new method. Statistically significant differences between the diet groups were observed regarding total as well as individual bile acid concentrations.

Analysis of oligosaccharides in lignocellulosic biomass hydrolysates by high-performance anion-exchange chromatography coupled with mass spectrometry (HPAEC-MS).

The carbohydrate composition of lignocellulosic biomass hydrolysates is highly complex. High performance anion exchange chromatography coupled with pulsed amperometric detection (HPAEC-PAD), a widely used method for carbohydrate analysis, provides limited chemical information on the detected peaks. To improve the detection and increase the chemical information of the carbohydrates, HPAEC was coupled with mass spectrometry (MS). Using a pooled hydrolysate sample, it was shown that HPAEC-MS can separate and detect many oligosaccharides in one experimental run based on retention time and mass. The method was validated on its linearity, reproducibility and response factors. The analysis of a group of different biomass hydrolysates revealed that remaining disaccharides was the bottleneck of the hydrolysis process. As an analytical tool, HPAEC-MS provides information for the improvement of hydrolysate pretreatment method and enzyme cocktail quality. Besides, the consumption ability of microbial host strains for various mono- and oligosaccharides in hydrolysates can be assessed.

Instrument and process independent binning and baseline correction methods for liquid chromatography-high resolution-mass spectrometry deconvolution.

Setting appropriate bin sizes to aggregate hyphenated high-resolution mass spectrometry data, belonging to similar mass over charge (m/z) channels, is vital to metabolite quantification and further identification. In a high-resolution mass spectrometer when mass accuracy (ppm) varies as a function of molecular mass, which usually is the case while reading m/z from low to high values, it becomes a challenge to determine suitable bin sizes satisfying all m/z ranges. Similarly, the chromatographic process within a hyphenated system, like any other controlled processes, introduces some process driven systematic behavior that ultimately distorts the mass chromatogram signal. This is especially seen in liquid chromatogram-mass spectrometry (LC-MS) measurements where the gradient of the solvent and the washing step cycle-part of the chromatographic process, produce a mass chromatogram with a non-uniform baseline along the retention time axis. Hence prior to any automatic signal decomposition techniques like deconvolution, it is a equally vital to perform the baseline correction step for absolute metabolite quantification. This paper will discuss an instrument and process independent solution to the binning and the baseline correction problem discussed above, seen together, as an effective pre-processing step toward liquid chromatography-high resolution-mass spectrometry (LC-HR-MS) data deconvolution.

OMG: Open Molecule Generator.

Computer Assisted Structure Elucidation has been used for decades to discover the chemical structure of unknown compounds. In this work we introduce the first open source structure generator, Open Molecule Generator (OMG), which for a given elemental composition produces all non-isomorphic chemical structures that match that elemental composition. Furthermore, this structure generator can accept as additional input one or multiple non-overlapping prescribed substructures to drastically reduce the number of possible chemical structures. Being open source allows for customization and future extension of its functionality. OMG relies on a modified version of the Canonical Augmentation Path, which grows intermediate chemical structures by adding bonds and checks that at each step only unique molecules are produced. In order to benchmark the tool, we generated chemical structures for the elemental formulas and substructures of different metabolites and compared the results with a commercially available structure generator. The results obtained, i.e. the number of molecules generated, were identical for elemental compositions having only C, O and H. For elemental compositions containing C, O, H, N, P and S, OMG produces all the chemically valid molecules while the other generator produces more, yet chemically impossible, molecules. The chemical completeness of the OMG results comes at the expense of being slower than the commercial generator. In addition to being open source, OMG clearly showed the added value of constraining the solution space by using multiple prescribed substructures as input. We expect this structure generator to be useful in many fields, but to be especially of great importance for metabolomics, where identifying unknown metabolites is still a major bottleneck.

Interpretation and visualization of non-linear data fusion in kernel space: study on metabolomic characterization of progression of multiple sclerosis.

BACKGROUND: In the last decade data fusion has become widespread in the field of metabolomics. Linear data fusion is performed most commonly. However, many data display non-linear parameter dependences. The linear methods are bound to fail in such situations. We used proton Nuclear Magnetic Resonance and Gas Chromatography-Mass Spectrometry, two well established techniques, to generate metabolic profiles of Cerebrospinal fluid of Multiple Sclerosis (MScl) individuals. These datasets represent non-linearly separable groups. Thus, to extract relevant information and to combine them a special framework for data fusion is required. METHODOLOGY: The main aim is to demonstrate a novel approach for data fusion for classification; the approach is applied to metabolomics datasets coming from patients suffering from MScl at a different stage of the disease. The approach involves data fusion in kernel space and consists of four main steps. The first one is to extract the significant information per data source using Support Vector Machine Recursive Feature Elimination. This method allows one to select a set of relevant variables. In the next step the optimized kernel matrices are merged by linear combination. In step 3 the merged datasets are analyzed with a classification technique, namely Kernel Partial Least Square Discriminant Analysis. In the final step, the variables in kernel space are visualized and their significance established. CONCLUSIONS: We find that fusion in kernel space allows for efficient and reliable discrimination of classes (MScl and early stage). This data fusion approach achieves better class prediction accuracy than analysis of individual datasets and the commonly used mid-level fusion. The prediction accuracy on an independent test set (8 samples) reaches 100%. Additionally, the classification model obtained on fused kernels is simpler in terms of complexity, i.e. just one latent variable was sufficient. Finally, visualization of variables importance in kernel space was achieved.

Metabolomics of cerebrospinal fluid reveals changes in the central nervous system metabolism in a rat model of multiple sclerosis.

Experimental Autoimmune Encephalomyelitis (EAE) is the most commonly used animal model for Multiple Sclerosis (MScl). CSF metabolomics in an acute EAE rat model was investigated using targetted LC-MS and GC-MS. Acute EAE in Lewis rats was induced by co-injection of Myelin Basic Protein with Complete Freund's Adjuvant. CSF samples were collected at two time points: 10 days after inoculation, which was during the onset of the disease, and 14 days after inoculation, which was during the peak of the disease. The obtained metabolite profiles from the two time points of EAE development show profound differences between onset and the peak of the disease, suggesting significant changes in CNS metabolism over the course of MBP-induced neuroinflammation. Around the onset of EAE the metabolome profile shows significant decreases in arginine, alanine and branched amino acid levels, relative to controls. At the peak of the disease, significant increases in concentrations of multiple metabolites are observed, including glutamine, O-phosphoethanolamine, branched-chain amino acids and putrescine. Observed changes in metabolite levels suggest profound changes in CNS metabolism over the course of EAE. Affected pathways include nitric oxide synthesis, altered energy metabolism, polyamine synthesis and levels of endogenous antioxidants. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0306-3) contains supplementary material, which is available to authorized users.

Identification of unknown migrants from food contact materials.

Materials that come into contact with foodstuffs can transfer components that may cause odour or taint problems or in the worse case cause the foodstuff to be unsafe to eat. The identities of some of these are easily predicted from the chemistry of known components but others are not. In this respect, it is important to be able to identify and quantify these chemicals. This chapter describes the need for methods of identification of unknown chemicals that may migrate. Mass spectrometric analytical methods are described, including headspace-gas chromatography with mass spectrometry (HS-GC-MS), liquid injection gas chromatography with MS, and liquid chromatography with time-of-flight MS (LC-TOF-MS).