Signal intensities derived from different NMR probes and parameters contribute to variations in quantification of metabolites.
We discovered that serious issues could arise that may complicate interpretation of metabolomic data when identical samples are analyzed at more than one NMR facility, or using slightly different NMR parameters on the same instrument. This is important because cross-center validation metabolomics studies are essential for the reliable application of metabolomics to clinical biomarker discovery. To test the reproducibility of quantified metabolite data at multiple sites, technical replicates of urine samples were assayed by 1D-(1)H-NMR at the University of Alberta and the University of Michigan. Urine samples were obtained from healthy controls under a standard operating procedure for collection and processing. Subsequent analysis using standard statistical techniques revealed that quantitative data across sites can be achieved, but also that previously unrecognized NMR parameter differences can dramatically and widely perturb results. We present here a confirmed validation of NMR analysis at two sites, and report the range and magnitude that common NMR parameters involved in solvent suppression can have on quantitated metabolomics data. Specifically, saturation power levels greatly influenced peak height intensities in a frequency-dependent manner for a number of metabolites, which markedly impacted the quantification of metabolites. We also investigated other NMR parameters to determine their effects on further quantitative accuracy and precision. Collectively, these findings highlight the importance of and need for consistent use of NMR parameter settings within and across centers in order to generate reliable, reproducible quantified NMR metabolomics data.
Pulmonary Research Group, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.
Paige
P
Lacy
Pulmonary Research Group, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.
Ryan T
RT
McKay
Department of Clinical, Social and Administrative Sciences, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, United States of America.
Michael
M
Finkel
Department of Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, Michigan, United States of America.
Alla
A
Karnovsky
Department of Medicinal Chemistry and the Biochemical Nuclear Magnetic Resonance Core, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, United States of America.
Scott
S
Woehler
Chenomx, Inc., Edmonton, Alberta, Canada.
Michael J
MJ
Lewis
Chenomx, Inc., Edmonton, Alberta, Canada.
David
D
Chang
Department of Clinical, Social and Administrative Sciences, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, United States of America ; Department of Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, Michigan, United States of America.
Kathleen A
KA
Stringer
D015415:Biomarkers / Q000652:urine; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000592:standards*; D055432:Metabolomics / Q000592:standards*; D008875:Middle Aged; D015203:Reproducibility of Results; D059629:Signal-To-Noise Ratio; D016482:Urinalysis
D015415:Biomarkers