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High-Precision Automated Workflow for Urinary Untargeted Metabolomic Epidemiology

preprint
submitted on 25.12.2020, 15:43 and posted on 28.12.2020, 13:23 by Isabel Meister, Pei Zhang, Anirban Sinha, C. Magnus Sköld, Åsa M. Wheelock, Takashi Izumi, Romanas Chaleckis, Craig E. Wheelock
Urine is a non-invasive biofluid that is rich in polar metabolites and well-suited for metabolomic epidemiology. However, due to individual variability in health and hydration status, the physiological concentration of urine can differ >15-fold, which can pose major challenges in untargeted LC-MS metabolomics. Although numerous urine normalization methods have been implemented (e.g., creatinine, specific gravity – SG), most are manual and therefore not practical for population-based studies. To address this issue, we developed a method to measure SG in 96-well-plates using a refractive index detector (RID), which exhibited accuracy within 85-115% and <3.4% precision. Bland-Altman statistics showed a mean deviation of -0.0001 SG units (limits of agreement: -0.0014-0.0011) relative to a hand held refractometer. Using this RID-based SG normalization, we developed an automated LC MS workflow for untargeted urinary metabolomics in 96-well-plate format. The workflow uses positive and negative ionization HILIC chromatography and acquires mass spectra in data independent acquisition (DIA) mode at 3 collision energies. Five technical internal standards (tISs) were used to monitor data quality in each method, all of which demonstrated raw coefficients of variation (CVs) <10% in the quality controls (QCs) and <20% in the samples for a small cohort (n=87 samples, n=22 QCs). Application in a large cohort (n=842 urine samples, n=248 QCs), demonstrated CVQC<5% and CVsamples<16% for 4/5 tISs after signal drift correction by cubic spline regression. The workflow identified >540 urinary metabolites including endogenous and exogenous compounds. This platform is suitable for performing urinary untargeted metabolomic epidemiology and will be useful for applications in population-based molecular phenotyping.

History

Email Address of Submitting Author

isabel.meister@gunma-u.ac.jp

Institution

Gunma University Initiative for Advanced Research (GIAR), Gunma University

Country

Japan

ORCID For Submitting Author

0000-0001-9063-0492

Declaration of Conflict of Interest

no conflict of interest

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