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Evaluation of normalization strategies for GC-based metabolomics

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Abstract

Introduction

For many samples studied by GC-based metabolomics applications, extensive sample preparation involving extraction followed by a two-step derivatization procedure of methoximation and trimethylsilylation (TMS) is typically required to expand the metabolome coverage. Performing normalization is critical to correct for variations present in samples and any biases added during the sample preparation steps and analytical runs. Addressing the totality of variations with an adequate normalization method increases the reliability of the downstream data analysis and interpretation of the results.

Objectives

Normalizing to sample mass is one of the most commonly employed strategies, while the total peak area (TPA) as a normalization factor is also frequently used as a post-acquisition technique. Here, we present a new normalization approach, total derivatized peak area (TDPA), where data are normalized to the intensity of all derivatized compounds. TDPA relies on the benefits of silylation as a universal derivatization method for GC-based metabolomics studies.

Methods

Two sample classes consisting of systematically incremented sample mass were simulated, with the only difference between the groups being the added amino acid concentrations. The samples were TMS derivatized and analyzed using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS). The performance of five normalization strategies (no normalization, normalized to sample mass, TPA, total useful peak area (TUPA), and TDPA) were evaluated on the acquired data.

Results

Of the five normalization techniques compared, TUPA and TDPA were the most effective. On PCA score space, they offered a clear separation between the two classes.

Conclusion

TUPA and TDPA carry different strengths: TUPA requires peak alignment across all samples, which depends upon the completion of the study, while TDPA is free from the requirement of alignment. The findings of the study would enhance the convenient and effective use of data normalization strategies and contribute to overcoming the data normalization challenges that currently exist in the metabolomics community.

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Data availability

MATLAB code produced for this paper is available at https://github.com/ryland-chem/totalDerivPeakNorm. All other data is available upon request.

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Funding

Authors would like to thank MITACS, DNA Genotek, Inc., The Natural Sciences and Engineering Research Council of Canada (NSERC) for support. The support of The Canada Foundation for Innovation (CFI), Genome Canada, and Genome Alberta to The Metabolomics Innovation Center (TMIC) is also acknowledged. RTG acknowledges support from NSERC and the Canadian Institutes of Health Research (CIHR) in the form of scholarships.

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Authors and Affiliations

  1. Department of Chemistry, University of Alberta, Edmonton, AB, T6G 2G2, Canada

    Seo Lin Nam, Ryland T. Giebelhaus, Kieran S. Tarazona Carrillo, A. Paulina de la Mata & James J. Harynuk

  2. The Metabolomics Innovation Centre, Edmonton, AB, Canada

    Seo Lin Nam, Ryland T. Giebelhaus, Kieran S. Tarazona Carrillo, A. Paulina de la Mata & James J. Harynuk

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  1. Seo Lin Nam
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  2. Ryland T. Giebelhaus
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  3. Kieran S. Tarazona Carrillo
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  4. A. Paulina de la Mata
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  5. James J. Harynuk
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Contributions

All authors contributed to the study conception and design. Sample preparation and data collection were performed by KT; data processing and analysis were performed by SLN and RTG. The first draft of the manuscript was written by SLN and all authors commented on previous versions of the manuscript. All authors have read and agreed to the final manuscript.

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Correspondence to James J. Harynuk.

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Nam, S.L., Giebelhaus, R.T., Tarazona Carrillo, K.S. et al. Evaluation of normalization strategies for GC-based metabolomics. Metabolomics 20, 22 (2024). https://doi.org/10.1007/s11306-023-02086-8

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