Securing the Future of NMR Metabolomics Reproducibility: A Call for Standardized Reporting
- PMID: 40953484
- PMCID: PMC12489887
- DOI: 10.1021/acs.analchem.5c03274
Securing the Future of NMR Metabolomics Reproducibility: A Call for Standardized Reporting
Abstract
Metabolomics is a rapidly growing multidisciplinary field with ever increasing demand and usability, which is attracting a surge of new researchers. While their varied skill sets, scientific questions, and approaches enrich the field with fresh perspectives and innovation, individual investigators also bring wide-ranging levels of metabolomics-specific experience and diverse areas of interest. These factors introduce considerable variability and inconsistency in both the methodology and reporting. A recent comparative literature review of nuclear magnetic resonance (NMR) metabolomics from studies published in 2010 and 2020 revealed significant shortcomings in the reporting of experimental details necessary for evaluating both the scientific rigor and the reproducibility of NMR-based metabolomics experiments. Each stage of metabolomics research contains multiple methodological choices and various optimization parameters, all of which can introduce experimental bias and alter the study results. This emphasizes the need for proper reporting to enhance reproducibility, data reusability, and study comparability. To address these concerns, the NMR Special Interest Group within the Metabolomics Association of North America presents reporting recommendations focused on fundamental aspects of NMR metabolomics research identified from the detailed literature review report. These include specifics with respect to study design, sample preparation, data acquisition, data processing and analysis, data accessibility, and comparability to previous studies. Also presented is a complementary list of seminal papers in the field to guide the study design and implementation of NMR metabolomics experiments. This initiative seeks to enhance the long-term impact of NMR metabolomics by supporting high-quality, reproducible, and impactful data collected from well-executed and thoroughly reported studies.
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