TY - JOUR
T1 - Long-Term Metabolomics Reference Material
AU - Gouveia, Goncalo J.
AU - Shaver, Amanda O.
AU - Garcia, Brianna M.
AU - Morse, Alison M.
AU - Andersen, Erik C.
AU - Edison, Arthur S.
AU - McIntyre, Lauren M.
N1 - Funding Information:
Research reported in this publication was supported by the National Institutes of Health under Award Number 1U2CES030167-01. The authors would like to thank Dr. David Blum and Dr. Ron Garrison from the Bio-expression and Fermentation Facility at the University of Georgia for training and advice using the Bioreactors and Pamela Kirby at the Edison Lab for assistance with material handling and storage logistics.
Funding Information:
All raw and processed data, along with detailed experimental, NMR acquisition, and data analysis methods, are available under project identifier PR001106 at the Metabolomics Workbench (www.metabolomicsworkbench.org). The data can be accessed directly via its project doi: 10.21228/M8R395. Metabolomics Workbench is supported by NIH grant U2C-DK119886. Acknowledgments
Publisher Copyright:
© 2021 The Authors. Published by American Chemical Society
PY - 2021/7/6
Y1 - 2021/7/6
N2 - 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 grownEscherichia colibatches 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. ThisE. coliRM was then used as a food source to produce aCaenorhabditis elegansRM for a metabolomics experiment. The metabolite extraction of this material, alongside 41 independently grown individualC. eleganssamples 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.
AB - 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 grownEscherichia colibatches 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. ThisE. coliRM was then used as a food source to produce aCaenorhabditis elegansRM for a metabolomics experiment. The metabolite extraction of this material, alongside 41 independently grown individualC. eleganssamples 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.
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U2 - 10.1021/acs.analchem.1c01294
DO - 10.1021/acs.analchem.1c01294
M3 - Article
C2 - 34156835
AN - SCOPUS:85110142986
SN - 0003-2700
VL - 93
SP - 9193
EP - 9199
JO - Analytical Chemistry
JF - Analytical Chemistry
IS - 26
ER -