# HR-MAS NMR Applications in Plant Metabolomics

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## Abstract

**:**

## 1. Introduction

## 2. Analytical Techniques in Metabolomics

## 3. Theoretical Background of HR-MAS NMR

## 4. HR-MAS NMR-Based Workflow

## 5. Harvesting Plant Material and Sample Preparation

## 6. Pulse Sequences Used in Metabolomics

^{1}H-NOESY (nuclear overhauser effect spectroscopy) with water pre-saturation and the

^{1}H-CPMG (Carr–Purcell–Meiboom–Gill) sequence. NOESY spectra provide a complete and quantitative profile of the observed metabolites with the suppression of the water peak without an effect on the intensity of the other peaks [19,20,21]. CPMG is a pulse sequence which removes the broad signals from macromolecules, like proteins and lipids [19,22].

^{1}H homonuclear correlation experiments are commonly used for identification. COSY (correlation spectroscopy) identifies the spin–spin coupling of protons [19,22] and TOCSY (total correlation spectroscopy) provides information about the correlation between all protons in metabolites [20,22]. Another experiment is the

^{1}H J-resolved where the effect of a chemical shift and J-coupling is separated into two independent directions [24].

^{1}H-

^{13}C heteronuclear correlation experiments. These experiments provide information about the coupling between a proton and a carbon [20,22]. HSQC (heteronuclear single-quantum correlation) provides input about the correlation between a proton and a carbon which are separated by one bond. In addition, HMBC (heteronuclear multiple-bond correlation) gives information about the correlation over multiple bonds [27].

## 7. Pre-Processing of One-Dimensional HR-MAS NMR Spectra

^{1}H-NMR spectrum, pre-processing involves alignment, baseline correction, bucketing, normalisation and scaling.

#### 7.1. Spectral Alignment

^{1}H-NMR spectroscopy [28,29]. In addition, computational approaches to align the spectra have been developed in recent years [23,25]. Most of these approaches use pairwise alignment using a reference spectrum.

#### 7.2. Baseline Correction

#### 7.3. Bucketing

#### 7.4. Normalisation

#### 7.5. Scaling

## 8. Multivariate Analysis

## 9. Applications of HR-MAS NMR in Plant Metabolomics

## 10. Conclusions and Future Perspective

^{1}H-HR-MAS slice localised spectroscopy (SLS) and HR-MAS chemical shift imaging (CSI) to determine the distribution of metabolites along the anteroposterior axis of Drosophila melanogaster [81]. Here, a MAS probe coupled with a three axes gradient system was used, together with pulse sequences for SLS and CSI. HR-MAS CSI is also applied to different food products and also to an intact wasp insect to examine the metabolic profile in specific regions along the sample spinning axis [82]. A slow spinning speed of 500 Hz was used to prevent damage to the insect during HR-MAS CSI measurements [83]. Due to the small sizes of specific structures of plants, high-resolution micro-MAS probe (HR-μMAS) can be considered. A lot smaller sample size (<0.5 mg) can be used in HR-μMAS in comparison to standard HR-MAS sample size (~100–150 mg) [84]. This can be used to study specific parts of plant, as shown for garlic [85].

^{13}C-labelled precursor into the metabolic network or by supplying

^{13}CO

_{2}and follow the redistribution of the label into other metabolites by either NMR or mass spectrometry [88,89]. The redistribution can be followed throughout time during dynamic labelling or after reaching steady-state in a steady-state labelling approach [89]. In the current fluxomics protocols, an extraction step has to be performed, which has the disadvantage of losing components during preparation. It can thus be interesting to develop an HR-MAS NMR-based fluxomics approach which is not available at the moment.

## Author Contributions

## Funding

## Conflicts of Interest

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**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 (B

_{0}).

**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.

NMR Spectroscopy | Mass Spectrometry | |
---|---|---|

Sensitivity | Low sensitivity, but can be improved with higher field strength and cryo- or microprobes | High sensitivity, can reach the detection limit of attomolar (10–18) concentrations |

Sample measurement | In one measurement with a detectable concentration can be detected | Need chromatography techniques for different classes of metabolites |

Sample recovery | Non-destructive technique Several analyses can be performed on the same extracted sample | Destructive technique |

Reproducibility | Very high | Moderate |

Quantification | Absolute quantitation of metabolites possible by adding one standard with known concentration | Quantification is possible with authentic standards, which are not available for newly identified compounds. Ionisation efficiencies, ion suppression and matrix effects have influences on the concentration. |

Targeted or untargeted approach | Untargeted and targeted approach | Untargeted and targeted approach, mainly used for targeted analysis |

Scaling Method | Formula |
---|---|

Autoscaling | ${x}_{ij}^{AS}=\frac{({x}_{ij}^{N}-{\overline{x}}_{j})}{{s}_{j}}$ |

Range scaling | ${x}_{ij}^{RS}=\frac{({x}_{ij}^{N}-{\overline{x}}_{j})}{\left({x}_{{j}_{max}}-{x}_{{j}_{\mathrm{min}}}\right)}$ |

Vast scaling | ${x}_{ij}^{VS}=\frac{\left({x}_{ij}^{N}-{\overline{x}}_{j}\right)}{{s}_{j}}\xb7\frac{{\overline{x}}_{j}}{{s}_{j}}$ |

Pareto scaling | ${x}_{ij}^{PS}=\frac{{x}_{ij}^{N}-{\overline{x}}_{j}}{\sqrt{{s}_{j}}}$ |

**Table 3.**Summary of the publications studying metabolomics using high-resolution magic angle spinning NMR. COSY, Correlation Spectroscopy; CPMG, Carr-Purcell-Meiboom-Gill; CPPR, composite pulses presaturation; HCA, hierarchical cluster analysis; HMBC, heteronuclear multiple bond correlation; HMQC, heteronuclear multiple-quantum correlation; HSQC, heteronuclear single quantum coherence; J-res, J-resolved, KNN, k-nearest neighbors; NOESY, nuclear Overhauser effect spectroscopy, OPLS-DA, orthogonal partial least squares discriminant analysis; PCA, principal component analysis; PLS-DA, partial least-squares discriminant analysis; STOCSY, statistical total correlation spectroscopy; TOCSY, total correlated spectroscopy.

Plant | Research Objective | Magnetic Field Strength (MHz) | Pulse Sequences | Multivariate Models |
---|---|---|---|---|

Influences of Biotic or Abiotic Stress | ||||

Winter wheat (Triticum aestivum) [49] | Evaluate the influences of different drought treatments | 400 | 1D | PCA |

Jatropha curcas [50] | Determine the impacts of pruning procedures and water management | 400 | Zg | - |

Ribes nigrum [51] | Determine the effect of seasonal asymmetric warming | 600 | CPMG, HSQC | PCA |

Soybean [52] | Determine the influences of water deficiency | 600 | CPMG, NOESY | PLS-DA |

Jatropha curcas [41] | Studying the effect of Jatropha mosaic virus on the metabolic profile | 400 | NOESY, CPMG, COSY | - |

Pear (Pyrus communis) and quince (Cydonia oblonga) [42] | Study the effect of humic acid on the morphogenesis of pear and quince | 400 | ^{13}C, CPMG, 1D LED, COSY, TOCSY, HSQC | PCA |

Lettuce (Lactuca sativa) [43] | Influences of the fungicide mancozeb on the leaves at different growth stages | 800 | NOESY, TOCSY, HSQC | PCA, PLS-DA |

Tomato (Solanum lycopersicum) [44] | Study the influences of 6-pentyl-2H-pyran-2-one and harzianic acid on the leaves | 400 | CPMG, COSY, TOCSY, J-res, HSQC, HMBC | PCA |

Maize (Zea mays) [45] | Determine the toxic effects on maize root tips of organo-chlorine pesticides | 600 | CPMG | OPLS-DA |

Maize (Zea mays) [46] | Determine the effect of mineral or compost fertilisation and inoculation with arbuscular mycorrhizal fungi | 400 | CPMG, COSY, TOCSY, J-res, HSQC, HMBC | PCA |

Soybean [47] | Determine the metabolic alternation caused by S. sclerotiorum infection | 500 | CPPR, TOCSY, HSQC | PCA |

Onion (Allium cepa L.) [48] | Evaluate the effect of onion yellow dwarf virus on the metabolites of onions | 400 | Zgpr | PLS-DA |

Study the Ripening and Storage of Fruits | ||||

Mango fruit (Mangifera indica) [53] | Studying the metabolic profile of mango pulp during ripening | 400 | ^{1}H 1D, ^{1}H-^{13}C correlation, TOCSY, J-res | - |

Tomato (Solanum lycopersicum) [54] | Studying different tissues of the tomato during fruit ripening | 500 | NOESY, TOCSY, HMQC | PCA |

Golden delicious apples [55] | Determine the impact of storage time and production systems | 500 | NOESY, COSY, TOCSY | PCA, PLS-DA |

Ginseng [56] | Distinguish the age of ginseng based on metabolomics | 600 | CPMG | PCA, PLS-DA, OPLS-DA |

Studying Different Cell Types of Plants | ||||

Lemon (Citrus limon) and citron (Citrus medica) [57] | The metabolic profile of different parts of the lemon and citron are studied | 400 | ^{1}H, CPMG, COSY, TOCSY, HSQC | - |

Characterising of Plant | ||||

Crocus sativus [58] | Establish the main metabolites present in C. sativus petals | 400 | ^{1}H, COSY, TOCSY, HSQC, HMBC | - |

Berberis laurina (Berberidaceae) [59] | Establish the main metabolites present in Berberis laurina leaves, stems and roots | 400 | Zg, HSQC, HMBC | PCA |

Understanding Transgenic Plants | ||||

Poplar tree (Populus tremula) [39] | Studying the time- and growth-related metabolic profile of PttMYB76 and wild-type poplar tree | 500 | CPMG | PCA, PLS-DA |

Common bean (Phaseolus vulgaris) [60] | Distinction between conventional and transgenic common beans | 500 | CPMG | PCA |

“Swingle” citrumelo [61] | Evaluate the metabolic profile of non-transgenic and transgenic citrumelo | 500 | ^{1}H, HSQC, TOCSY | PCA, PLS-DA |

Geographical Origin of Plants | ||||

Sweet peppers (Capsicum annum) [62] | Discriminate sweet peppers according to their geographical origin | 400 | NOESY, 1D ^{13}C, TOCSY | PLS-DA |

Garlic (Allium sativum) [63] | Characterisation of two varieties garlic cropped in different Italian regions | 400 | NOESY, ^{13}C, TOCSY, HMQC | PLS-DA |

Cocoa beans [64] | Assess the geographical origins of fermented and dried cocoa beans | 400 | ^{1}H | PCA, PLS-DA, OPLS-DA |

Cherry tomatoes of Pachino [66] | Determine the major metabolites present in cherry tomatoes of Pachino | 700 | ^{1}H | PCA |

PGI cherry tomato of Pachino, PGI inter-donato lemon of Messina, red garlic of Nubia [67] | Identify and quantify metabolites from three typical food products of the Mediterranean diet | 700 | ^{1}H | PCA |

PGI inter-donato lemon of Messina [68] | Determine metabolites unique for PGI interdonato lemon of messina | 700 | ^{1}H, COSY, TOCSY, HSQC | - |

Tomato (Lycopersicon esculentum) [69] | Establish the metabolic differences between commercially available varieties | 500 | NOESY, HSQC | PCA |

Distinguish between Different Cultivars | ||||

Trichilia catigua [70] | Classification of commercial samples of Catuaba | 400 | CPMG | PCA, HCA |

Withania somnifera [71] | Evaluate metabolic profile of 4 different chemotypes of W. somnifera | 800 | NOESY, CPMG, COSY, HSQC | PCA |

Apples [72] | Discriminate three different apple cultivars by their metabolic profile | 500 | NOESY, COSY, TOCSY | PCA, PLS-DA |

Melon (Cucumis melo) [73] | Quantification of sugars and compare two varieties | 400 | ^{1}H | - |

Rice (Oryza sativa) [74] | Determine the metabolic variation of diverse rice cultivars | 700 | CPMG, TOCSY, HSQC, STOCSY | PCA, OPLS-DA |

Persimmon (Diospyros kaki) [75] | Follow the metabolic changes during development of different cultivars | 400 | NOESY | PCA |

Seven cultivars of Panax ginseng [76] | Study the primary metabolites of the seven cultivars of ginseng berries | 600 | CPMG | PCA, PLS-DA, OPLS-DA |

Almonds (seeds of Prunus dulcis) [77] | Establish the difference between seven different types of almonds | 500 | Zg, COSY | PCA |

Curtis (Passiflora alata) [78] | Seven herbal medicines containing leaf extract of some Passiflora species | 500 | Zg, COSY | PCA, KNN |

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Augustijn, D.; de Groot, H.J.M.; Alia, A.
HR-MAS NMR Applications in Plant Metabolomics. *Molecules* **2021**, *26*, 931.
https://doi.org/10.3390/molecules26040931

**AMA Style**

Augustijn D, de Groot HJM, Alia A.
HR-MAS NMR Applications in Plant Metabolomics. *Molecules*. 2021; 26(4):931.
https://doi.org/10.3390/molecules26040931

**Chicago/Turabian Style**

Augustijn, Dieuwertje, Huub J. M. de Groot, and A. Alia.
2021. "HR-MAS NMR Applications in Plant Metabolomics" *Molecules* 26, no. 4: 931.
https://doi.org/10.3390/molecules26040931