Abstract
Nuclear magnetic resonance (NMR) spectroscopy is well-established to address questions in large-scale untargeted metabolomics. Although several approaches in data processing and analysis are available, significant issues remain. NMR spectroscopy of urine generates information-rich but complex spectra in which signals often overlap. Furthermore, slight changes in pH and salt concentrations cause peak shifting, which introduces, in combination with baseline irregularities, un-informative noise in statistical analysis. Within this work, a straight-forward data processing tool addresses these problems by applying a non-linear curve fitting model based on Voigt function line shape and integration of the underlying peak areas. This method allows a rapid untargeted analysis of urine metabolomics datasets without relying on time-consuming 2D-spectra based deconvolution or information from spectral libraries. The approach is validated with spiking experiments and tested on a human urine 1H dataset compared to conventionally used methods and aims to facilitate metabolomics data analysis.
Highlights
The field of metabolomics aims to study the complex mixture of metabolites in any tissue or organism and is widely used in several research fields for biomarker discovery, in nutritional studies or to personalized medicine-related scientific questions [1,2,3,4]
In nuclear magnetic resonance spectroscopy (NMR) metabolomics analysis, such trends are often covered by background
The field of untargeted NMR metabolomics became increasingly important over the past few years
Summary
The field of metabolomics aims to study the complex mixture of metabolites in any tissue or organism and is widely used in several research fields for biomarker discovery, in nutritional studies or to personalized medicine-related scientific questions [1,2,3,4]. Proton-NMR spectroscopy has the advantage of directly producing quantitative measures and offers structural information, as well as high reproducibility [6,7,8]. Proton signals underlie the sensitivity against minor changes in pH or matrix composition, which results in drifts along the chemical shift axis of some metabolites whereby the extend differs between resonances [9,10,11]. This positional noise adds variation to the dataset and affects subsequent analysis. As metabolomics often aim to identify biomarkers from datasets, which tend to have high variances in metabolite presence and concentration by nature, additional variance should be kept as low as possible
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