posted on 2021-03-19, 19:36authored byIsabel Meister, Pei Zhang, Anirban Sinha, C. Magnus Sköld, Åsa M. Wheelock, Takashi Izumi, Romanas Chaleckis, Craig E. Wheelock
Urine is a noninvasive
biofluid that is rich in polar metabolites
and well suited for metabolomic epidemiology. However, because of
individual variability in health and hydration status, the physiological
concentration of urine can differ >15-fold, which can pose major
challenges
in untargeted liquid chromatography–mass spectrometry (LC–MS)
metabolomics. Although numerous urine normalization methods have been
implemented (e.g., creatinine, specific gravitySG), 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 to 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 a 96-well-plate format.
The workflow uses positive and negative ionization HILIC chromatography
and acquires mass spectra in data-independent acquisition (DIA) mode
at three 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 urine 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.