Cross-Platform Evaluation of Commercially Targeted and Untargeted Metabolomics Approaches to Optimize the Investigation of Psychiatric Disease

Publications: Cross-Platform Evaluation of Commercially Targeted and Untargeted Metabolomics Approaches to Optimize the Investigation of Psychiatric Disease

Metabolites 2021, 11, 609; DOI link: https://doi.org/10.3390/metabo11090609

Authors: Lauren E. Chaby, Heather C. Lasseter, Kévin Contrepois , Reza M. Salek, Christoph W. Turck, Andrew Thompson, Timothy Vaughan, Magali Haas and Andreas Jeromin

Abstract:

Metabolomics methods often encounter trade-offs between quantification accuracy and coverage, with truly comprehensive coverage only attainable through a multitude of complementary assays. Due to the lack of standardization and the variety of metabolomics assays, it is difficult to integrate datasets across studies or assays. To inform metabolomics platform selection, with a focus on posttraumatic stress disorder (PTSD), we review platform use and sample sizes in psychiatric metabolomics studies and then evaluate five prominent metabolomics platforms for coverage and performance, including intra-/inter-assay precision, accuracy, and linearity. We found performance was variable between metabolite classes, but comparable across targeted and untargeted approaches. Within all platforms, precision and accuracy were highly variable across classes, ranging from 0.9–63.2% (coefficient of variation) and 0.6–99.1% for accuracy to reference plasma. Several classes had high inter-assay variance, potentially impeding dissociation of a biological signal, including glycerophospholipids, organooxygen compounds, and fatty acids. Coverage was platform-specific and ranged from 16–70% of PTSD-associated metabolites. Non-overlapping coverage is challenging; however, benefits of applying multiple metabolomics technologies must be weighed against cost, biospecimen availability, platform-specific normative levels, and challenges in merging datasets. Our findings and open-access cross-platform dataset can inform platform selection and dataset integration based on platform-specific coverage breadth/overlap and metabolite-specific performance.

Data in the BRAINCommons

The dataset for this project is hosted in the BRAINCommons™, the cloud-based research and discovery platform for the brain health community.

For more information about the BRAINCommons™, visit https://www.braincommons.org.

If you are interested in accessing the data associated with this publication, please reach out to Maryan Zirkle at executive.director@braincommons.org.

Acknowledgements and Funding

This work was supported by Cohen Veterans Bioscience and generous grants COH-0013 and COH-0003 from Steven A. Cohen for the RAPID-Dx program.

We are immensely grateful to Amit Etkin and colleagues for their invaluable sample collection, as well as the Indiana University Genetic Biobank for their expertise in the processing and preparation of all samples. We are also greatly appreciative of the metabolomics vendors that participated in this cross-platform comparison, for providing their technical expertise and highly beneficial discussions. We are also deeply thankful to Eugene Rakhmatulin and the WaveAccess team for their invaluable contributions and creativity in generating the Metabolomics Platform Exploration Tool.

H.C.L., L.E.C., T.V., A.T., M.H. and A.J. are employed by Cohen Veterans Bioscience, a nonprofit 501 (c) (3) research organization.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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