Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry

Journal name:
Nature Protocols
Volume:
6,
Pages:
1060–1083
Year published:
DOI:
doi:10.1038/nprot.2011.335
Published online

Abstract

Metabolism has an essential role in biological systems. Identification and quantitation of the compounds in the metabolome is defined as metabolic profiling, and it is applied to define metabolic changes related to genetic differences, environmental influences and disease or drug perturbations. Chromatography–mass spectrometry (MS) platforms are frequently used to provide the sensitive and reproducible detection of hundreds to thousands of metabolites in a single biofluid or tissue sample. Here we describe the experimental workflow for long-term and large-scale metabolomic studies involving thousands of human samples with data acquired for multiple analytical batches over many months and years. Protocols for serum- and plasma-based metabolic profiling applying gas chromatography–MS (GC-MS) and ultraperformance liquid chromatography–MS (UPLC-MS) are described. These include biofluid collection, sample preparation, data acquisition, data pre-processing and quality assurance. Methods for quality control–based robust LOESS signal correction to provide signal correction and integration of data from multiple analytical batches are also described.

At a glance

Figures

  1. The generalized workflow for the design of experiments, sample preparation, data acquisition, data preprocessing, integration of multiple analytical experiments and data integration/data analysis for the analysis of serum and plasma in a four-analytical-block biological experiment.
    Figure 1: The generalized workflow for the design of experiments, sample preparation, data acquisition, data preprocessing, integration of multiple analytical experiments and data integration/data analysis for the analysis of serum and plasma in a four-analytical-block biological experiment.
  2. The QC-RLSC protocol for a metabolic feature detected in UPLC-MS (ES+) with signal attenuation across a given analytical batch.
    Figure 2: The QC-RLSC protocol for a metabolic feature detected in UPLC-MS (ES+) with signal attenuation across a given analytical batch.

    A cross-validated LOESS curve (upper plot) is fitted to the QC samples, the correction curve interpolated (triangles), to which the total data set for that peak is corrected (lower plot).

  3. The data preprocessing workflow for UPLC-MS data.
    Figure 3: The data preprocessing workflow for UPLC-MS data.

    The workflow incorporates QC samples for quality assurance, QC-RLSC and block integration.

  4. The experimental workflow followed in the HUSERMET project.
    Figure 4: The experimental workflow followed in the HUSERMET project.

    Four sample aliquots are prepared from a single serum or plasma sample, with three aliquots processed forward for GC-MS, UPLC-MS (ES+) and UPLC-MS (ES−) analysis. Separate workflows for sample preparation, data acquisition, data preprocessing, signal correction and quality assurance are available for data analysis.

  5. Typical chromatograms observed for serum.
    Figure 5: Typical chromatograms observed for serum.

    (ac) Shown are chromatograms for UPLC-TOF (ES−) MS (a), UPLC-TOF (ES+) MS (b) and GC-TOF-MS (c). The base peak intensity chromatogram is depicted for UPLC-TOF-MS data for both ES+ and ES–. The single-ion chromatogram for m/z 73 is depicted for GC-TOF–MS data. m/z 73 is a fragment ion characteristic of the trimethylsilyl-derivatized products of metabolites.

  6. Peak area data for 1-methylnicotinamide before and after QC-RLSC.
    Figure 6: Peak area data for 1-methylnicotinamide before and after QC-RLSC.

    The data represents ten experimental blocks and a total of 1,200-subject sample and 600 QC sample injections. Blue circles represent the subject samples and red circles represent the QC samples.

  7. Gender-specific creatinine distribution.
    Figure 7: Gender-specific creatinine distribution.

    Determined by applying a biochemical quantification assay (left) and GC-TOF-MS in metabolic profiling mode (right). CREA, creatinine.

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Author information

Affiliations

  1. Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester, UK.

    • Warwick B Dunn &
    • Royston Goodacre
  2. School of Chemistry, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester, UK.

    • Warwick B Dunn,
    • David Broadhurst,
    • Paul Begley,
    • Eva Zelena,
    • Sue Francis-McIntyre,
    • Nadine Anderson,
    • Marie Brown,
    • Antony Halsall,
    • Douglas B Kell &
    • Royston Goodacre
  3. Centre for Advanced Discoveries and Experimental Therapeutics, Manchester Biomedical Research Centre and School of Biomedicine, Manchester, UK.

    • Warwick B Dunn
  4. Department of Medicine, Katz Group Centre for Pharmacy & Health, University of Alberta, Edmonton, Alberta, Canada.

    • David Broadhurst
  5. School of Computer Science, The University of Manchester, Manchester, UK.

    • Joshau D Knowles
  6. Department of Investigative Preclinical Toxicology, GlaxoSmithKline, Hertfordshire, UK.

    • John N Haselden &
    • Andrew W Nicholls
  7. Department of Clinical Pharmacology, Drug Metabolism and Pharmacokinetics, AstraZeneca, Cheshire, UK.

    • Ian D Wilson

Contributions

W.B.D. developed the experimental design strategy, the quality control strategy, the analytical methods and co-wrote the paper. D.B. developed the experimental design, sample scheduling and quality control strategies, and developed the QC-RSLC algorithm, performed data analysis and co-wrote the paper. P.B. and E.Z. developed the experimental design strategy and methods and acquired data. S.F.-M. and N.A. acquired data. M.B. developed the XCMS deconvolution strategy and performed data analysis. J.D.K. developed the sample stratification algorithm. A.H., J.N.H. and A.W.N. developed the experimental design strategy. I.D.W., D.B.K. and R.G. developed the experimental design strategy and co-wrote the paper.

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Supplementary information

PDF files

  1. Supplementary Method 1 (5K)

    Typical analysis orders for (A) GC-ToF-MS

  2. Supplementary Method 2 (5K)

    Typical analysis orders for (B) UPLC-ToF-MS

  3. Supplementary Method 3 (6K)

    Typical analysis orders for (C) UPLC-LTQ/Orbitrap-MS

Additional data