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The maternal serum metabolome by multisegment injection-capillary electrophoresis-mass spectrometry: a high-throughput platform and standardized data workflow for large-scale epidemiological studies

Nature Protocols volume 16, pages 1966–1994 (2021)Cite this article

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Abstract

A standardized data workflow is described for large-scale serum metabolomic studies using multisegment injection-capillary electrophoresis-mass spectrometry. Multiplexed separations increase throughput (<4 min/sample) for quantitative determination of 66 polar/ionic metabolites in serum filtrates consistently detected (coefficient of variance (CV) <30%) with high frequency (>75%) from a multi-ethnic cohort of pregnant women (n = 1,004). We outline a validated protocol implemented in four batches over a 7-month period that includes details on preventive maintenance, sample workup, data preprocessing and metabolite authentication. We achieve stringent quality control (QC) and robust batch correction of long-term signal drift with good mutual agreement for a wide range of metabolites, including serum glucose as compared to a clinical chemistry analyzer (mean bias = 11%, n = 668). Control charts for a recovery standard (mean CV = 12%, n = 2,412) and serum metabolites in QC samples (median CV = 13%, n = 202) demonstrate acceptable intermediate precision with a median intraclass coefficient of 0.87. We also report reference intervals for 53 serum metabolites from a diverse population of women in their second trimester of pregnancy.

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Fig. 1: Data workflow for characterization of the maternal serum metabolome by MSI-CE-MS from a multi-ethnic birth cohort of pregnant women.
Fig. 2: Targeted profiling of known serum metabolites and their unambiguous identification and quantification.
Fig. 3: Nontargeted profiling and authentication of reliably measured ions with putative identification of unknown serum metabolites.
Fig. 4: Schematic overview of data workflow for targeted and nontargeted serum metabolomic analyses using MSI-CE-MS.
Fig. 5: Control charts are used to review data quality of the serum maternal metabolome, including detection of potential batch effects in longitudinal studies.
Fig. 6: 2D scores plot using principal component analysis that compares the technical precision from repeated analysis of pooled serum QC samples relative to the biological variance of the serum metabolome among second trimester pregnant women.
Fig. 7: Interlaboratory method comparison of reported fasting serum glucose concentrations in second trimester pregnant women.
Fig. 8: Intermethod comparison of mean serum metabolite concentrations measured by MSI-CE-MS in pregnant women from FAMILY and START cohorts (n = 600).
Fig. 9: Differences in the serum maternal metabolome as a function of birth cohort ethnicity, fasting status, and regional center from across Canada.

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Data availability

Full datasets generated and/or analyzed in the current study are not publicly available since participants in the FAMILY, CHILD, START and ABC cohorts did not consent to public sharing of their data at the time of recruitment. Datasets can be obatined from the corresponding author on reasonable request.

Software availability

Our protocol used vendor-specific software (Agilent Technologies Inc.) for processing raw data generated by (Q)TOF mass analyzers. We are developing customized open-access software for processing MSI-CE-MS data in future work.

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Acknowledgements

The authors thank the participants in all of the cohorts who generously donated their time, information, and serum samples. The study was funded by the Canadian Institutes of Health Research (CIHR) (Team Grant: DOHaD – Implications for Men, Women, Boys and Girls; 201511). PB-M. also acknowledges support from the Natural Sciences and Engineering Research Council of Canada (NSERC), and Genome Canada. Dr. Anand is supported by a Tier 1 Canada Research Chair in Ethnicity and Cardiovascular Disease and Heart and Stroke Foundation Chair in Population Health. The authors acknowledge the contributions of the cohort investigators who recruited participants from CHILD (A. Becker, P. Mandhane, S. Turvey, T. Moraes, M. Azad, D. Befus), FAMILY (K. Morrison), START (M. Gupta) and ABC (J. Wilson, G. Wahi).

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Authors and Affiliations

  1. Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario, Canada

    Meera Shanmuganathan, Zachary Kroezen, Biban Gill, Sandi Azab & Philip Britz-McKibbin

  2. Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada

    Russell J. de Souza & Sonia S. Anand

  3. Population Health Research Institute, Hamilton Health Sciences, Hamilton, Ontario, Canada

    Russell J. de Souza & Sonia S. Anand

  4. Department of Medicine, McMaster University, Hamilton, Ontario, Canada

    Koon K. Teo, Dipika Desai & Sonia S. Anand

  5. Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada

    Stephanie Atkinson

  6. Department of Pediatrics, University of Toronto, Toronto, Ontario, Canada

    Padmaja Subbarao

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  1. Meera Shanmuganathan
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Contributions

S.S.A. and P.B.-M. designed the research. M.S. conducted research and processed data with support from Z.K., B.G. and S.A. M.S. and P.B.-M. analyzed the data and performed statistical analyses that was reviewed by R.J.d.S. and S.S.A. M.S. and P.B.-M. wrote the paper with critical feedback from R.J.d.S., S.S.A. and S.A. P.B.-M. had primary responsibility for final content. S.A.A., K.K.T., S.A. and P.S. were principal investigators responsible for oversight of each cohort’s data. All listed authors read, edited, and approved the final manuscript.

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Correspondence to Philip Britz-McKibbin.

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Key references using this protocol

de Souza R. J. et al. Curr. Develop. Nutr. 4, nzaa144 (2020): https://doi.org/10.1093/cdn/nzaa144

Yamamoto M. et al. Metabolomics 15, 1–18 (2019): https://doi.org/10.1007/s11306-019-1543-0

Wellington N., et al. Nutrients 11, 2407 (2019): https://www.mdpi.com/2072-6643/11/10/2407

Saoi M., et al. Metabolites 9, 134 (2019): https://www.mdpi.com/2218-1989/9/7/134

DiBattista A. et al. J. Proteome Res. 18, 841–854 (2019): https://pubs.acs.org/doi/10.1021/acs.jproteome.8b00351

Key data used in this protocol

de Souza R. J. et al. Curr. Develop. Nutr. 4, nzaa144 (2020): https://doi.org/10.1093/cdn/nzaa144

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Shanmuganathan, M., Kroezen, Z., Gill, B. et al. The maternal serum metabolome by multisegment injection-capillary electrophoresis-mass spectrometry: a high-throughput platform and standardized data workflow for large-scale epidemiological studies. Nat Protoc 16, 1966–1994 (2021). https://doi.org/10.1038/s41596-020-00475-0

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