HR-MAS NMR Applications in Plant Metabolomics

Abstract

Metabolomics is used to reduce the complexity of plants and to understand the underlying pathways of the plant phenotype. The metabolic profile of plants can be obtained by mass spectrometry or liquid-state NMR. The extraction of metabolites from the sample is necessary for both techniques to obtain the metabolic profile. This extraction step can be eliminated by making use of high-resolution magic angle spinning (HR-MAS) NMR. In this review, an HR-MAS NMR-based workflow is described in more detail, including used pulse sequences in metabolomics. The pre-processing steps of one-dimensional HR-MAS NMR spectra are presented, including spectral alignment, baseline correction, bucketing, normalisation and scaling procedures. We also highlight some of the models which can be used to perform multivariate analysis on the HR-MAS NMR spectra. Finally, applications of HR-MAS NMR in plant metabolomics are described and show that HR-MAS NMR is a powerful tool for plant metabolomics studies.

Keywords: HR-MAS NMR; metabolomics; multivariate analysis; plants.

Figure 1

In systems biology, the information from the genetic program is integrated with information from functional physical structures to provide a comprehensive model of plants.

Figure 2

HR-MAS setup where the sample is rotated with high frequency (>3 kHz) tiled by the magic angle θ_m with respect to the magnetic field (B₀).

Figure 3

A typical high-resolution magic angle spinning (HR-MAS) NMR-based workflow. OPLS-DA, orthogonal partial least squares discriminant analysis; PCA, principal component analysis; SUS plot, Shared and unique (SUS) plot.

Figure 4

Truncated NMR spectrum before and after bucketing into equally spaced buckets of 0.04 ppm width. Bucketing allows for moderate shift averaging at the expense of resolution and provides a matrix for further processing.

Figure 5

Every data point in the hypothetical bucket matrix $X$ ($i\times j$ ) is normalised by the sum of the intensity of each sample. $x_{ij}$ is an element located in the $i$th row and the $j$th column.

Figure 6

(a) Every column of the normalised data matrix $X^N$ is mean centred to obtain the data matrix $X^C$. (b) Every column in the normalised data matrix $X^N$ is scaled using the different methods. The obtained data matrix $X^S$ is used for multivariate analysis. ${\overline{x}}_j$ and $s_j$ are, respectively, the mean and standard deviation of the values of the $j$th column.

Figure 7

PCA score (a) and loading plot (b) of a data set including 50 wild-type samples and 50 mutant samples and 4 buckets for every sample. The score plot shows a clear separation between the wild type and mutant. The PCA loading plot shows that bucket 3 has the most influence on the first principal component and buckets 1 and 2 have the most influence on the second principal component.

Figure 8

Partial least squares discriminant analysis (PLS-DA) score (a) and loading plot (b) and orthogonal partial least squares discriminant analysis (OPLS-DA) score (c) and loading plot (d) for the same data set as described in Figure 6. In both models, there is also a clear separation between the wild type and mutant in the score plots.