Introduction

1_{H nmr based metabolic profiling suffers from the considerable peak overlap in biofluid spectra,} especially when chemical shift variation occurs or the data are binned into discrete chemical shift regions for intensity calculations (bucketing), and this often inhibits complete interpretation of the spectra and accompanying pattern recognition models.90, 116, 117

One specific problem for nmr spectroscopy is that many metabolites give rise to a considerable number of spin-coupled multiplets over a range of chemical shifts, see also figure 2.3. This has two consequences: impaired peak dispersion due to spectral overlap and over-representation of the metabolite in any statistical classification exercise, as illustrated in figure 3.1. One method for reducing peak overlap is to disperse signals into a second dimension. J -resolved (jres) nmr spectroscopy71

is one of the simplest two-dimensional nmr experiments with which2Dspectra can be rapidly obtained. This can be more efficient than physical separation of the compounds (for example using a chromatography step before nmr analysis)118 _{or the use of lower sensitivity 2D} nmr experiments (such as homo- and heteronuclear correlation spectroscopy).119

The jres experiment is formed of an array of spin–echo pulse elements, in which an incremented delay period is used to define a second frequency dimension. After suitable data processing,40 _the chemical shift and J -coupling information are resolved on two orthogonal axes, thereby increasing signal dispersion. The 2D _{jres spectrum can be projected onto the chemical shift axis,}120, 121 effectively yielding a1H broadband decoupled proton spectrum consisting exclusively of ‘singlets’, as will be discussed in figure 3.3, which can then be subjected to pattern recognition analysis. Potentially, this approach can reduce the overlap and over-representation problems encountered in biological samples.

PEAK OVERLAP

OVER-REPRESENTATION

Figure 3.1: The presence of multiplets in nmr spectra has a major effect on the complexity as well as the information content of the spectra. Although the multiplicity of peaks contains vital structural information, it also increases the level of peak overlap and gives a metabolite peak a wider base in a spectroscopic data table, causing highly-split multiplets to be ‘over-represented’.

The value of jres nmr spectra in metabolic profiling studies of biofluids was demonstrated two decades ago122–125

and jres spectra have since then been routinely acquired40, 126–128 _including, inter alia, studies of human urine,122 _plasma48, 123 _{and cerebrospinal fluid.}124

jres nmr spec- troscopy has also been utilised in studies of the components of plants,129 _fish130 _{and beer,}131 _in kinetic drug metabolism studies,88

and combined with magic-angle spinning nmr methods on tis- sues132 and even in vivo.133 _{The most readily apparent advantages of jres nmr spectroscopy} stem from improved dispersion, through reduced peak congestion. The refocusing character of the pulse sequence also results in T2-editing, which attenuates broad macromolecular signals as well as resonances from motionally constrained compounds and species with chemical exchange processes at intermediate rates on the nmr time-scale. The spin–spin coupling information is re- tained in the 2D _{spectrum and can aid peak identification. jres has proven to be particularly} useful in metabolic profiling studies122, 128, 134 _{because many of the nuclear spin systems of such} small molecules exhibit first-order nmr spectra; hence, artefactual peaks associated with second- order effects are rare. However, there are limitations caused by the non-standard line-shape and T2-editing in jres nmr spectra and the effects on metabolite quantitation will be discussed in §3.4.6.135

Other studies have investigated the effect of jres nmr data acquisition parameters ap- plied to metabonomics.40, 136

Improved spectral clustering of data in pca models built from jres data versus conventional one-dimensional (1D _{and Carr–Purcell–Meiboom–Gill (cpmg)) spectra} has been reported.127

To date, most studies evaluating jres spectra have used binned (or bucketed) data based on a low-resolution representation of the peak intensities rather than the full-resolution spectral data.127–130, 132 _{The limitations of binning spectral data on data interpretation and information} recovery are well-documented80, 137

and are illustrated in figure 3.2: an nmr peak can be divided across different bins, or a bin may contain a number of peaks. Therefore, statistical effects become

1.9 1.95 2 2.05 2.1

ppm

Figure 3.2: Full datapoint resolution spectra (red, 44001 points: δ 0.1–4.5, 6.0–10) will allow for better biomarker recovery than the binned equivalent, shown as grey bars. For clarity, only a part of the full spectral region is shown.

less pronounced and small peaks may become completely obscured. The main reason for binning data is to minimise the effects of chemical shift variability, which is necessary when statistical techniques are applied. The development of alignment algorithms for nmr data have lessened the need for this compromise, because they resolve all peak positional variation whilst retaining the high resolution of the data.116, 117, 138, 139

Here, the jres nmr experiment will be comprehensively re-evaluated with respect to its use in mathematical modelling of biological spectra. Full-resolution one-dimensional projections are used, and it is anticipated that the increased level of apparent resolution will aid biomarker identification, especially for complicated, highly overlapped spectral regions. The processing steps required for routine and robust use of jres nmr spectra in metabonomics will be considered, including peak alignment (following a recommendation from Dr. M. Coen, Imperial College London, UK). The qualitative and quantitative aspects in conjunction with correlation spectroscopy approaches will be assessed, and a method based on regression for peak assignment based on jres projections will be evaluated. Finally, the degree of information recovery compared to conventionally acquired one- dimensional1_{H nmr spectra (}1D _{and cpmg) will be considered. As an example where nmr peak} overlap and over-representation are acute, spectra acquired from plasma and urine samples from a galactosamine toxicity study in the rat were used.140 _{These contain exogenous and endogenous} metabolites that are spectrally high-represented, caused by the presence of many multiplets, thus obscuring a considerable portion of the spectral range.