title: (1)H NMR-based metabolite profiling workflow to reduce inter-sample chemical shift variations in urine samples for improved biomarker discovery.
abstract: Metabolite profiling of urine has seen much advancement in recent years, and its analysis by nuclear magnetic resonance (NMR) spectroscopy has become well established. However, the highly variable nature of human urine still requires improved protocols despite some standardization. In particular, diseases such as kidney disease can have a profound effect on the composition of urine and generate a highly diverse sample set for clinical studies. Large variations in pH and the cationic concentration of urine play an important role in creating positional noise within datasets generated from NMR. We demonstrate positional noise to be a confounding variable for multivariate statistical tools such as statistical total correlation spectroscopy (STOCSY), thereby hindering the process of biomarker discovery. We present a two-dimensional buffering system using potassium fluoride (KF) and phosphate buffer to reduce positional noise in metabolomic data generated from urine samples with various levels of proteinuria. KF reduces positional noise in citrate peaks, by decreasing the mean relative standard deviation (RSD) from 0.17 to 0.09. By reducing positional noise with KF, STOCSY analysis of citrate peaks saw significant improvement. We further aligned spectral data using a recursive segment-wise peak alignment (RSPA) method, which leads to further improvement of the positional noise (RSD = 0.06). These results were validated using diverse selection of metabolites which lead to an overall improvement in positional noise using the suggested protocol. In summary, we provide an improved workflow for urine metabolite biomarker discovery to achieve higher data quality for better pathophysiological understanding of human diseases. Graphical abstract Citrate peaks in the range 2.75-2.5 ppm from datasets with different sample preparation protocols and with/without in silico alignment. A Citrate peaks with standard phosphate buffering and without in silico alignment. B citrate peaks with standard phosphate buffering and with in silico alignment. C citrate peak with additional potassium fluoride and standard phosphate buffering without in silico alignment. D citrate peaks with additional potassium fluoride and standard phosphate buffering with in silico alignment. Below the respective spectrum are displayed the percent relative standard deviation (RSD) of the respective citrate peaks. This is a measure of the positional noise of peaks within a (1)H NMR analysis. It can be seen that D performs the best in reducing positional noise of citrate peaks. E-H STOCSY analysis of correlating spectral features with the driver peak at 2.675 ppm (see red arrow) to identify structural correlations. As a, b, c, and d are known to be structurally correlated, STOCSY analysis should reveal r (2) = 1 if data is perfectly aligned and can therefore be used as a measure of peak alignment. E Strong positional noise does not allow identifying the c and d peaks of the AB system to be correlated. F, G Neither in silico alignment or KF addition alone can completely improve the alignment and therefore increase the correlations. H Highly improved alignment by combining both KF addition and in silico alignment reduces positional noise and elucidates all four citrate peaks to be strongly correlated.
authors.affiliation: Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, German Research Center for Environment Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany.
authors.forename: Ryan B
authors.initials: RB
authors.lastname: Gil
authors.affiliation: Division of Clinical Chemistry and Pathobiochemistry (Central Laboratory), University Hospital, Otfried-Mueller-Str. 10, 72076, Tübingen, Germany.|German Center for Diabetes Research (DZD), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany.|Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich at the University of Tübingen (IDM), 72076, Tübingen, Germany.
authors.forename: Rainer
authors.initials: R
authors.lastname: Lehmann
authors.affiliation: Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, German Research Center for Environment Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany. schmitt-kopplin@helmholtz-muenchen.de.|German Center for Diabetes Research (DZD), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany. schmitt-kopplin@helmholtz-muenchen.de.|Chair of Analytical Food Chemistry, Technische Universität München, Alte Akademie 10, 85354, Freising-Weihenstephan, Germany. schmitt-kopplin@helmholtz-muenchen.de.
authors.forename: Philippe
authors.initials: P
authors.lastname: Schmitt-Kopplin
authors.affiliation: Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, German Research Center for Environment Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany. silke.heinzmann@helmholtz-muenchen.de.
authors.forename: Silke S
authors.initials: SS
authors.lastname: Heinzmann
mesh_terms: D015415:Biomarkers / Q000652:urine*; D006863:Hydrogen-Ion Concentration; D055432:Metabolomics; D066244:Proton Magnetic Resonance Spectroscopy / Q000379:methods*
chemical_list: D015415:Biomarkers
keywords: Biomarker discovery; Metabolomics; Multivariate data analysis; Non-targeted; Nuclear magnetic resonance spectroscopy; Urine