Long-Term Metabolomics Reference Material

Abstract

The use of quality control samples in metabolomics ensures data quality, reproducibility, and comparability between studies, analytical platforms, and laboratories. Long-term, stable, and sustainable reference materials (RMs) are a critical component of the quality assurance/quality control (QA/QC) system; however, the limited selection of currently available matrix-matched RMs reduces their applicability for widespread use. To produce an RM in any context, for any matrix that is robust to changes over the course of time, we developed iterative batch averaging method (IBAT). To illustrate this method, we generated 11 independently grown Escherichia coli batches and made an RM over the course of 10 IBAT iterations. We measured the variance of these materials by nuclear magnetic resonance (NMR) and showed that IBAT produces a stable and sustainable RM over time. This E. coli RM was then used as a food source to produce a Caenorhabditis elegans RM for a metabolomics experiment. The metabolite extraction of this material, alongside 41 independently grown individual C. elegans samples of the same genotype, allowed us to estimate the proportion of sample variation in preanalytical steps. From the NMR data, we found that 40% of the metabolite variance is due to the metabolite extraction process and analysis and 60% is due to sample-to-sample variance. The availability of RMs in untargeted metabolomics is one of the predominant needs of the metabolomics community that reach beyond quality control practices. IBAT addresses this need by facilitating the production of biologically relevant RMs and increasing their widespread use.

An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics

“…Further, the selection of features present in all biological replicates of PD1074 ensures stable features across a range of environmental conditions ( Figure 1 ). Iterative batch average method (IBAT) controls in the NMR study (from PD1074) combined with the biological replicates of PD1074 and the PD1074 pools enabled us to estimate the relative contribution of extraction (∼40%), growth (∼60%), and instrument variance, as expected in NMR, was negligible 31 . We are also able to directly compare the stable features detected using the BRM approach to PD1074 batch pools.…”

“…In non-model organism experiments, implementation of a BRM 31, 34 also enables stable feature identification. New analytic advances have enabled joint alignment and feature selection across high levels of variability when there is a common QC standard like a BRM included in each batch 77 .…”

“…Spectral features consistently present within groups can be prioritized for future studies so that database matching and, ultimately, compound identification efforts are focused on the most likely biologically important spectral features. This aspect is important as studies 7, 9, 63 increase in size and complexity 31, 36, 64, 65 .…”

“…For each LSCP, worms were collected, population size estimated, and subsequently divided into at least 12 identical aliquots of 200,000 worms in ddH 2 O and flash-frozen in liquid nitrogen to quench metabolism and stored at -80°C 43 . As a QC sample, C. elegans IBAT BRM was generated and saved in 200,000 worm aliquots 31 .…”

“…Identification of shared spectral features using a BRM is a successful strategy 31, 32 that has proven essential in large-scale studies 32-34 . Implementation of BRM controls for instrument variation can estimate and normalize extraction variation 16, 18, 31 . However, variation among samples within the group remains.…”