Mass spectrometry-based metabolic profiling reveals different metabolite patterns in invasive ovarian carcinomas and ovarian borderline tumors.

Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. We have used a metabolite profiling approach to test the hypothesis that quantitative signatures of primary metabolites can be used to characterize molecular changes in ovarian tumor tissues. Sixty-six invasive ovarian carcinomas and nine borderline tumors of the ovary were analyzed by gas chromatography/time-of-flight mass spectrometry (GC-TOF MS) using a novel contamination-free injector system. After automated mass spectral deconvolution, 291 metabolites were detected, of which 114 (39.1%) were annotated as known compounds. By t test statistics with P < 0.01, 51 metabolites were significantly different between borderline tumors and carcinomas, with a false discovery rate of 7.8%, estimated with repeated permutation analysis. Principal component analysis (PCA) revealed four principal components that were significantly different between both groups, with the highest significance found for the second component (P = 0.00000009). PCA as well as additional supervised predictive models allowed a separation of 88% of the borderline tumors from the carcinomas. Our study shows for the first time that large-scale metabolic profiling using GC-TOF MS is suitable for analysis of fresh frozen human tumor samples, and that there is a consistent and significant change in primary metabolism of ovarian tumors, which can be detected using multivariate statistical approaches. We conclude that metabolomics is a promising high-throughput, automated approach in addition to functional genomics and proteomics for analyses of molecular changes in malignant tumors.

Trace-level determination of pesticides in food using difficult matrix introduction-gas chromatography-time-of-flight mass spectrometry.

A procedure for the fully automated analysis of food samples by means of difficult matrix introduction-gas chromatography-time-of-flight mass spectrometry (DMI-GC-ToF MS) is discussed. After extraction, samples require very little clean-up and are injected in a micro- or micro-vial which is held in a liner. Next, the liner is placed in the injector and the contents of the vial are thermally desorbed and led directly to the capillary GC column. After GC-ToF MS analysis, the data are processed automatically using a peak deconvolution algorithm. The practicability of the procedure was demonstrated by analysing spiked grape and pineapple samples down to the 1-10 ng/g concentration level.