Abstract
1H NMR spectra from urine can yield information-rich data sets that offer important insights into many biological and biochemical phenomena. However, the quality and utility of these insights can be profoundly affected by how the NMR spectra are processed and interpreted. For instance, if the NMR spectra are incorrectly referenced or inconsistently aligned, the identification of many compounds will be incorrect. If the NMR spectra are mis-phased or if the baseline correction is flawed, the estimated concentrations of many compounds will be systematically biased. Furthermore, because NMR permits the measurement of concentrations spanning up to five orders of magnitude, several problems can arise with data analysis. For instance, signals originating from the most abundant metabolites may prove to be the least biologically relevant while signals arising from the least abundant metabolites may prove to be the most important but hardest to accurately and precisely measure. As a result, a number of data processing techniques such as scaling, transformation and normalization are often required to address these issues. Therefore, proper processing of NMR data is a critical step to correctly extract useful information in any NMR-based metabolomic study. In this review we highlight the significance, advantages and disadvantages of different NMR spectral processing steps that are common to most NMR-based metabolomic studies of urine. These include: chemical shift referencing, phase and baseline correction, spectral alignment, spectral binning, scaling and normalization. We also provide a set of recommendations for best practices regarding spectral and data processing for NMR-based metabolomic studies of biofluids, with a particular focus on urine.
Highlights
NMR has played an important role in the development and the continuing advances in metabolomics over the past two decades
We recommend that auto-phasing should be used as an initial phasing step
While this is just one example taken for one particular data set, it clearly illustrates how normalization affected the results of this exploratory analysis and the performance of the methods used to discriminate between groups of samples, which is a typical problem in metabolomics studies
Summary
NMR has played an important role in the development and the continuing advances in metabolomics over the past two decades. For the routine analysis of urine 1D 1H NMR spectra, statistical spectroscopy techniques presently appear to be the best option These approaches are robust and they allow useful results to be obtained with relatively little manual effort. We will review and discuss consensus recommendations for spectral processing, namely chemical shift referencing, phasing and baseline correction These steps are critical for generating high quality NMR data. The remainder of this review will focus on providing recommendations for “post processing” of NMR data, including the determination of interesting spectral regions (alignment and binning) as well as spectral normalization, scaling and transformation These are critical steps to statistical spectroscopy and their correct implementation is essential to the successful NMR analysis of urine (and other biofluid) samples
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