The use of metabolite, lipid, and protein profiles for systems biology, biomedical research, and clinical applications has risen dramatically over recent years. Global profiling allows for the detection, identification and relative quantification of candidate biomarkers, or more specifically molecular markers, which discriminate disease processes, oxidative stress, injury, histopathological changes, pathophysiological mechanisms, drug effects, glycosylation, etc. This technique has matured so that it is now possible to observe 1000s – 10,000s of unknown chemical compounds and different protein groups from cell lysates, bio-fluids and tissue samples, with the ability to gather simultaneous data of features much like RNA sequencing. This non-targeted data analysis approach can highlight gross differences between sample groups and can be further queried by targeted quantitative analysis for specific proteins and/or biomarkers of interest. However, due to the nature of detection, it is difficult if not impossible to relate data between instrument runs or amongst similar studies without a common internal standard or QC check.
There is a significant need for tools to increase harmonization, comparability, and reproducibility between laboratories and/or studies, and to provide quality control of sample preparation and data analysis protocols utilized in non-targeted analysis. The development of reference materials will serve as normalization and harmonization tools for quality assurance/quality control (QA/QC), materials for methods development or new technology characterization. In addition, these reference materials will generate publicly available reference datasets, which can be used to evaluate data processing and analysis.
There are currently no RM-based series available for use as study harmonization materials for workflow and data validation in these fields. We are generating both bio-fluid and tissue based materials to develop workflow best practices and serve as harmonization QA/QC checks for NMR- and MS-based metabolomic, lipidomic and proteomic measurements. Reference datasets including highly confident identifications of chemical compounds, peptides and protein groups are being generated in order to validate analytical data processing and results for non-targeted studies.