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Metabolite profiling and beyond: approaches for the rapid processing and annotation of human blood serum mass spectrometry data

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Abstract

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.

Workflow for the rapid processing and annotation of untargeted mass spectrometry data

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Acknowledgments

We thank Christoph Böttcher and Stefan Schmidt for the mass spectrometry support in Halle and Carsten Kuhl and Sebastian Wolf for help with the data analysis and the CAMERA and MetFrag tools. Jens Holmer-Jensen and Kjeld Hermansen are thanked for providing the sample set used for isotope pattern analyses. The work by JS was supported by the Nordic Centre of Excellence (NCoE) program (systems biology in controlled dietary interventions and cohort studies (SYSDIET), P No., 070014) and the Danish Obesity Research Centre (DanORC; see www.danorc.dk). DanORC is supported by the Danish Council for Strategic Research (Grant No. 2101-06-0005).

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Authors and Affiliations

  1. Department of Nutrition, Exercise and Sports, University of Copenhagen, Rolighedsvej 30, 1958, Frederiksberg, Denmark

    Jan Stanstrup & Lars Ove Dragsted

  2. Department of Stress and Developmental Biology, IPB Halle, Weinberg 3, 06120, Halle, Germany

    Michael Gerlich & Steffen Neumann

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  1. Jan Stanstrup
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  2. Michael Gerlich
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  3. Lars Ove Dragsted
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  4. Steffen Neumann
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Correspondence to Jan Stanstrup.

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Published in the topical collection Metabolomics and Metabolite Profiling with guest editors Rainer Schuhmacher, Rudolf Krska, Roy Goodacre, and Wolfram Weckwerth.

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Stanstrup, J., Gerlich, M., Dragsted, L.O. et al. Metabolite profiling and beyond: approaches for the rapid processing and annotation of human blood serum mass spectrometry data. Anal Bioanal Chem 405, 5037–5048 (2013). https://doi.org/10.1007/s00216-013-6954-6

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  • DOI: https://doi.org/10.1007/s00216-013-6954-6

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