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Metabolomics

Nowadays, gene discovery has been made very efficient with the combination of deep sequencing and the exploitation of natural variation. Just in Arabidopsis, hundreds of genetic loci have been identified as influencing a wide variety of processes, and we aim to go from gene-of-interest to characterized protein product using approaches to “take a picture” of the comprehensive metabolome of the plant.

The IPB is currently operating a wide range of NMR and mass spectrometry instruments for metabolomics across all four departments, which are integrated into our Metabolomics Platform.

The experimental work is complemented by extensive Cheminformatics and Bioinformatics research to process and interpret the huge amounts of data. The IPB is operating the first European MassBank server, and hosts several online tools for metabolite identification.

Contact partner for all interests concerning the metabolomics platform is Dr. Steffen Neumann.

Publications by Tag: Metabolomics

Displaying results 1 to 3 of 3.

Publications

Stanstrup, J., Gerlich, M., Dragsted, L.O. & Neumann, S. Metabolite profiling and beyond: approaches for the rapid processing and annotiation of human blood serum mass spectrometry data. Anal Bioanal Chem 405, 5037-5048, (2013) DOI: 10.1007/s00216-013-6954-6

In this paper, we describe data processing and metabolite identification approaches which lead to a rapid and semi-automated interpretation of metabolomics experiments. Data from metabolite fingerprinting using LC-ESI-Q-TOF/MS were processed with several open-source software packages, including XCMS and CAMERA to detect features and group features into compound spectra. Next, we describe the automatic scheduling of tandem mass spectrometry (MS) acquisitions to acquire a large number of MS/MS spectra, and the subsequent processing and computer-assisted annotation towards identification using the R packages MetShot, Rdisop, and the MetFusion application. We also implement a simple retention time prediction model using predicted lipophilicity logD, which predicts retention times within 42 s (6 min gradient) for most compounds in our setup. We putatively identified 44 common metabolites including several amino acids and phospholipids at metabolomics standards initiative (MSI) levels two and three and confirmed the majority of them by comparison with authentic standards at MSI level one. To aid both data integration within and data sharing between laboratories, we integrated data from two labs and mapped retention times between the chromatographic systems. Despite the different MS instrumentation and different chromatographic gradient programs, the mapped retention times agree within 26 s (20 min gradient) for 90 % of the mapped features.
Books and chapters

Neumann, S., Rasche, F., Wolf, S. & Böcker, S. Metabolite Identification and Computational Mass Spectrometry.. In: The Handbook of Plant Metabolomics, Metabolite Profiling and Networking. (Weckwerth, W. & Kahl, G.). Wiley-VCH 289-303, (2013) ISBN: 978-3-527-32777-5 DOI: 10.1002/9783527669882.ch16

Previous chapters have introduced protocols and examples for high-throughput metabolomics experiments. Metabolite identification is an important step in these experiments, bridging the metabolomics experiment, metabolite profiling, and the biological interpretation of the results. The elemental composition of the individual metabolites is the most basic information that can be calculated already from the mass spectrometry (MS) profiling data. For a more thorough identification, the “interesting” peaks are subjected to MS2, or even higher-order MSn measurements. Such spectra carry rich structural hints, revealing building blocks of the unknown compound, or allowing comparison with databases of reference spectra. This chapter describes a general strategy to identify metabolites, and proceeds through the steps of the identification for two example compounds, first calculating elemental compositions, performing in silico identification without reference spectra, and finally spectral library lookup.

Publications

Schymanski E.L. & Neumann, S. The Critical Assessment of Small Molecule Identification (CASMI): Challenges and Solutions. Metabilotes 3(3), 517-538, (2013) DOI: 10.3390/metabo3030517

The Critical Assessment of Small Molecule Identification, or CASMI, contest was founded in 2012 to provide scientists with a common open dataset to evaluate their identification methods. In this article, the challenges and solutions for the inaugural CASMI 2012 are presented. The contest was split into four categories corresponding with tasks to determine molecular formula and molecular structure, each from two measurement types, liquid chromatography-high resolution mass spectrometry (LC-HRMS), where preference was given to high mass accuracy data, and gas chromatography-electron impact-mass spectrometry (GC-MS), i.e., unit accuracy data. These challenges were obtained from plant material, environmental samples and reference standards. It was surprisingly difficult to obtain data suitable for a contest, especially for GC-MS data where existing databases are very large. The level of difficulty of the challenges is thus quite varied. In this article, the challenges and the answers are discussed, and recommendations for challenge selection in subsequent CASMI contests are given.

This page was last modified on 10.03.2014.

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