By Fabio Casu
The field of untargeted metabolomics presents measurement quality challenges stemming from instrument variability, impacting data reproducibility/harmonization, and contributing to limitations associated with metabolite identification using existing metabolomic databases. Quality assurance (QA) and quality control (QC) practices are therefore an essential component of a metabolomics workflow, especially for LC/MS-based analyses, but also for NMR studies.
Reference materials (RMs) are well-defined, stable and homogeneous QC materials, which allow the evaluation and control of intra- and inter-laboratory systematic variability associated with instrument performance and data processing protocols to enable measurement harmonization and comparability.
The National Institute of Standards and Technology (NIST) is working toward the development of a new generation of RMs for multi-platform metabolomic analyses which aim to provide the metabolomics community (researchers, commercial service providers, instrument vendors) with more economical reference materials with qualitatively characterized metabolite profiles for comparison, benchmarking, and harmonization. Metabolomics RMs currently under development include human biofluid-based materials (urine, plasma) commonly used in clinical diagnostics, tissues (liver), as well as alternative biological matrices such as complex microbial systems (feces) to support relatively non-invasive metabolomics/lipidomics screenings. Each material will be developed as a suite to include multiple phenotypes with distinct metabolic profiles to facilitate differential analysis and will include reference datasets providing highly confident identifications of molecular components to validate data processing workflows and software.
The development of unified multi-platform QA/QC tools that include associated reference data was prompted by an urgent need in the community to increase measurement reproducibility while also improving transparency in scientific data reporting. The future goal is to reduce uncertainty within experimental workflows and enhance confidence in the results obtained from untargeted metabolomic studies. These efforts will ultimately benefit the metabolomics field by improving laboratory performance and standardization of metabolomic studies across different analytical platforms and laboratories.