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.

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.

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.

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.

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.

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.

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.

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.

Understanding and classifying metabolite space and metabolite-likeness.

While the entirety of 'Chemical Space' is huge (and assumed to contain between 10(63) and 10(200) 'small molecules'), distinct subsets of this space can nonetheless be defined according to certain structural parameters. An example of such a subspace is the chemical space spanned by endogenous metabolites, defined as 'naturally occurring' products of an organisms' metabolism. In order to understand this part of chemical space in more detail, we analyzed the chemical space populated by human metabolites in two ways. Firstly, in order to understand metabolite space better, we performed Principal Component Analysis (PCA), hierarchical clustering and scaffold analysis of metabolites and non-metabolites in order to analyze which chemical features are characteristic for both classes of compounds. Here we found that heteroatom (both oxygen and nitrogen) content, as well as the presence of particular ring systems was able to distinguish both groups of compounds. Secondly, we established which molecular descriptors and classifiers are capable of distinguishing metabolites from non-metabolites, by assigning a 'metabolite-likeness' score. It was found that the combination of MDL Public Keys and Random Forest exhibited best overall classification performance with an AUC value of 99.13%, a specificity of 99.84% and a selectivity of 88.79%. This performance is slightly better than previous classifiers; and interestingly we found that drugs occupy two distinct areas of metabolite-likeness, the one being more 'synthetic' and the other being more 'metabolite-like'. Also, on a truly prospective dataset of 457 compounds, 95.84% correct classification was achieved. Overall, we are confident that we contributed to the tasks of classifying metabolites, as well as to understanding metabolite chemical space better. This knowledge can now be used in the development of new drugs that need to resemble metabolites, and in our work particularly for assessing the metabolite-likeness of candidate molecules during metabolite identification in the metabolomics field.

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.

Comprehensive analysis of the intracellular metabolism of antiretroviral nucleosides and nucleotides using liquid chromatography-tandem mass spectrometry and method improvement by using ultra performance liquid chromatography.

Nucleoside reverse transcriptase inhibitors (NRTIs) are a key class of drugs for the treatment of HIV infection. NRTIs are intracellularly phosphorylated to their active triphosphate metabolites and compete with endogenous deoxynucleotides (dNTP) for substrate binding. It is therefore important to analyze the intracellular concentrations of these compounds to understand drug efficacy and toxicity. To that purpose an analytical platform was developed that is capable of analyzing 8 NRTIs, 12 phosphorylated NRTIs and 4 dNTPs in small numbers of peripheral blood mononuclear cells, i.e. 1 × 10(6) cells. The platform consists of two liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods: a reversed-phase method for NRTIs using positive electrospray ionization (ESI) and an ion-pair LC-MS/MS method for the phosphorylated compounds using negative ESI. The methods use the same LC-MS system and column and changing from one method to the other only includes changing the mobile phase. The methods were partially validated, focussing on sensitivity, accuracy and precision. Successful transfer of the methods to ultra performance liquid chromatography (UPLC) led to a significant improvement of speed for the analysis of NRTIs and sensitivity for both NRTIs and phosphorylated NRTIs. The latter was demonstrated by the improved separation by UHPLC of dGTP vs. AZT-TP and ATP which made direct analysis of dGTP possible using the optimal MS/MS transition thereby significantly improving the detection limit of dGTP. Typically LLOQs observed for both the NRTIs and phosphorylated NRTIs were 1 nM, while the mean accuracy varied between 82 and 120% and inter- and intra-assay precision was generally <20%.

Comprehensive analysis of umami compounds by ion-pair liquid chromatography coupled to mass spectrometry.

UNLABELLED: An ion-pair LC-ESI-MS method was developed capable of analyzing various reported umami or umami-enhancing compounds, including glutamic acid and 5'-ribonucleotides. The method was validated using tomato and potato samples and showed overall good analytical performance with respect to selectivity, detection limit, linearity, and repeatability. The method was applied to various tomato samples resulting in concentrations of glutamic acid and 5'-ribonucleotides that were in good comparison with literature. The methodology might also be used for the discovery of new umami (enhancing) compounds in an untargeted mode. This was to a certain extent demonstrated for tomato samples by correlating all peaks observed with the ion-pair liquid chromatography-mass spectrometry (LC-MS) method to sensory properties using multivariate statistics. PRACTICAL APPLICATION: This study describes the development and application of a LC-MS method, which can be used to quantify several known umami (enhancing) compounds in various foods. Furthermore, the method might be useful for the discovery of new umami (enhancing) compounds.

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).

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.

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.

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.

Analysis of reaction products of food contaminants and ingredients: bisphenol A diglycidyl ether (BADGE) in canned foods.

Bisphenol A diglycidyl ether (BADGE) is an epoxide that is used as a starting substance in the manufacture of can coatings for food-contact applications. Following migration from the can coating into food, BADGE levels decay and new reaction products are formed by reaction with food ingredients. The significant decay of BADGE was demonstrated by liquid chromatographic (LC) analysis of foodstuffs, that is, tuna, apple puree, and beer, spiked with BADGE before processing and storage. Life-science inspired analytical approaches were successfully applied to study the reactions of BADGE with food ingredients, for example, amino acids and sugars. An improved mass balance of BADGE was achieved by selective detection of reaction products of BADGE with low molecular weight food components, using a successful combination of stable isotopes of BADGE and analysis by LC coupled to fluorescence detection (FLD) and high-resolution mass spectrometric (MS) detection. Furthermore, proteomics approaches showed that BADGE also reacts with peptides (from protein digests in model systems) and with proteins in foods. The predominant reaction center for amino acids, peptides, and proteins was cysteine.