Informatics for tandem mass spectrometry-based metabolomics

Metabolomics is a rapidly expanding field with applications in areas such as medicine, agriculture, or food safety. Tandem mass spectrometry (MS) is one of the main technologies that drives the field forward. Optionally coupled to a chromatographic element, MS can capture detailed snapshots of an organism’s metabolome. The resulting data sets are complex and difficult to analyse due to the multitude of external, biologically irrelevant influences. In particular metabolite identification – the ultimate goal of MS metabolomics – is a highly challenging exercise with inherently uncertain results. We have developed the data processing tool MassCascade to rapidly analyse and visualise chromatography MS data. MassCascade features methods for data (pre-)processing from initial file input to the compilation of the final result matrix. To simplify use and break down the complex analysis process, the tool has been made available in the form of a plug-in for the workflow platform KNIME: MassCascade-KNIME offers a visual representation of each processing function that can be utilized following the concept of visual programming. To further support metabolomics data analysis, cheminformatics methods have been added separately to the workflow platform from the Chemistry Development Kit to enable digital small molecule handling, essential for semi-automated metabolite identification. To demonstrate the MS analysis process and test MassCascade and its plugin, two scenarios typical in metabolomics were chosen: spectral fingerprinting and metabolite identification. A set of metabolomics tomato samples from a long-term study about chromatography MS system stability was processed and interpreted. Distinct trends and clustering could be extracted and explained verifying correct processing by the tool. Metabolite identification of spectral features was applied on a study about tomato ripening. Features differentiating ripening of four different tomato genotypes were singled out to that end. The implemented information-driven identification methodology enabled the selection of putative metabolite identifications from large lists of chemical compounds.