Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives.

Metabolomics involves the unbiased quantitative and qualitative analysis of the complete set of metabolites present in cells, body fluids and tissues (the metabolome). By analyzing differences between metabolomes using biostatistics (multivariate data analysis; pattern recognition), metabolites relevant to a specific phenotypic characteristic can be identified. However, the reliability of the analytical data is a prerequisite for correct biological interpretation in metabolomics analysis. In this review the challenges in quantitative metabolomics analysis with regards to analytical as well as data preprocessing steps are discussed. Recommendations are given on how to optimize and validate comprehensive silylation-based methods from sample extraction and derivatization up to data preprocessing and how to perform quality control during metabolomics studies. The current state of method validation and data preprocessing methods used in published literature are discussed and a perspective on the future research necessary to obtain accurate quantitative data from comprehensive GC-MS data is provided.

Substitution patterns in methylated potato starch as revealed from the structure and composition of fragments in enzymatic digests.

The effect of the granule structure on the methylation of starch was investigated by comparing the substitution patterns of potato starch methylated in granular suspension and in solution to DS 0.3. Substitution patterns were analyzed by successive digestion with alpha-amylase and amyloglucosidase, fractionation of the resulting malto-oligosaccharide mixture by GPC on a preparative scale, and characterization of the fractions by GLC and MALDI-MS. The mass composition of fractions with intermediate and higher degree of polymerization was indicative of enhanced clustering of substituents in granular methyl starch. On the contrary, the composition of the smaller saccharides was governed by enzyme specificity, which was also reflected in strong deviations in their monomer composition. A sequencing study on selected 'pure' small saccharides confirmed and complemented previous conclusions on enzyme specificity.

Uniform procedure of (1)H NMR analysis of rat urine and toxicometabonomics Part II: comparison of NMR profiles for classification of hepatotoxicity.

A procedure of nuclear magnetic resonance (NMR) urinalysis using pattern recognition is proposed for early detection of toxicity of investigational compounds in rats. The method is applied to detect toxicity upon administration of 13 toxic reference compounds and one nontoxic control compound (mianserine) in rats. The toxic compounds are expected to induce necrosis (bromobenzene, paracetamol, carbon tetrachloride, iproniazid, isoniazid, thioacetamide), cholestasis (alpha-naphthylisothiocyanate (ANIT), chlorpromazine, ethinylestradiol, methyltestosterone, ibuprofen), or steatosis (phenobarbital, tetracycline). Animals were treated daily for 2 or 4 days except for paracetamol and bromobenzene (1 and 2 days) and carbon tetrachloride (1 day only). Urine was collected 24 h after the first and second treatment. The animals were sacrificed 24 h after the last treatment, and NMR data were compared with liver histopathology as well as blood and urine biochemistry. Pathology and biochemistry showed marked toxicity in the liver at high doses of bromobenzene, paracetamol, carbon tetrachloride, ANIT, and ibuprofen. Thioacetamide and chlorpromazine showed less extensive changes, while the influences of iproniazid, isoniazid, phenobarbital, ethinylestradiol, and tetracycline on the toxic parameters were marginal or for methyltestosterone and mianserine negligible. NMR spectroscopy revealed significant changes upon dosing in 88 NMR biomarker signals preselected with the Procrustus Rotation method on principal component discriminant analysis (PCDA) plots. Further evaluation of the specific changes led to the identification of biomarker patterns for the specific types of liver toxicity. Comparison of our rat NMR PCDA data with histopathological changes reported in humans and/or rats suggests that rat NMR urinalysis can be used to predict hepatotoxicity.

Sensitivity of (1)H NMR analysis of rat urine in relation to toxicometabonomics. Part I: dose-dependent toxic effects of bromobenzene and paracetamol.

(1)H nuclear magnetic resonance (NMR) spectroscopy of rat urine in combination with pattern recognition analysis was evaluated for early noninvasive detection of toxicity of investigational chemical entities. Bromobenzene (B) and paracetamol (P) were administered at five single oral dosages between 2 and 500 mg/kg and between 6 and 1800 mg/kg, respectively. The sensitivity of the proposed method to detect changes in the NMR spectra 24 and 48 h after single dosing was compared with histopathology and biochemical parameters in plasma and urine. Both B and P applied at the highest dosages induced liver necrosis and markedly increased aspartate aminotransferase (AST) and alanine aminotransferase (ALT) plasma levels. At dosages of 125 mg/kg B and 450 mg/kg P, liver necrosis and changes in AST and ALT were less pronounced, while at lower dose levels these effects could not be detected. Changes in kidney pathology or standard urine biochemistry were not observed at any of these dosages. Evaluation of the total NMR dataset showed 80 signals to be sensitive for B and P dosing. Principal component analysis on the reduced dataset revealed that NMR spectra were significantly different at dosages above 8 mg/kg (B) and 110 mg/kg (P) at both sampling times. This implies a 4- to 16-fold increased sensitivity of NMR versus histopathology and clinical chemistry in recognizing early events of liver toxicity.

Novel system for classifying chromatographic applications, exemplified by comprehensive two-dimensional gas chromatography and multivariate analysis.

For practical chromatographers it is extremely difficult to judge the merits and limitations of new technological developments. On the other hand, it is nearly impossible for those at the forefront of technology to judge the implications of their efforts for all specific applications of chromatography. Both chromatographers and researchers can be aided by a classification of the numerous specific applications into a few well-defined categories. In this paper, we propose such a classification of all chemical analysis by chromatography into three generic types of applications, viz. target-compound analysis, group-type separation, and fingerprinting. The requirements for each type are discussed in general terms. The classification scheme is applied to assess the benefits and limitations of comprehensive two-dimensional gas chromatography (GC x GC) and the possible additional benefits of using multivariate-analysis (MVA) techniques for each type of application. The conclusions pertaining to the generic types of applications are indicative for the implications of new developments for specific chemical analysis by chromatography.

Quantitative analysis of target components by comprehensive two-dimensional gas chromatography.

Quantitative analysis using comprehensive two-dimensional (2D) gas chromatography (GC) is still rarely reported. This is largely due to a lack of suitable software. The objective of the present study is to generate quantitative results from a large GC x GC data set, consisting of 32 chromatograms. In this data set, six target components need to be quantified. We compare the results of conventional integration with those obtained using so-called "multiway analysis methods". With regard to accuracy and precision, integration performs slightly better than Parallel Factor (PARAFAC) analysis. In terms of speed and possibilities for automation, multiway methods in general are far superior to traditional integration.