High content measurement platforms like mass-spectrometry and quantitative NMR-spectroscopy allow for the simultaneous quantification of a large number of metabolites in a biopsy of a tumor. This is in analogy to genome wide gene expression profiling established about 10 years earlier. While expression profiling is a powerful tool in clinical diagnosis, metabolomics has not reached these standards.

Two-dimensional gas chromatography time-of-flight mass spectrometry (GCxGC-TOF-MS) is one of the most juvenile approaches to metabolomics. After separation through gas chromatography the metabolites are broken down into fragment ions which are recorded as different mass traces by a detector. Spectra over mass traces have to be combined to identify patterns of metabolites. Together with the Oefner Lab (Regensburg) we have developed a peak alignment tool INCA, which allows for a widely automated preprocessing of GCxGC-TOF-MS data (Almstetter, Appel, et al. Anal. Chem. 2009). Both groups keep on working on paving the way for a routine use of metabolic profiling in clinical diagnostics.