Pure Shift Nuclear Magnetic Resonance: a New Tool for Plant Metabolomics

The complete set of metabolites that comprise an organism – substrates, intermediates, and end products of biological processes – was coined in 1998 with the term, metabolome. It is well known that the metabolome is closely related to the phenotype, and it is of particular interest in plants as it reflects the direct interaction between the genotype and the environment^1,2. Hence, the characterization of the metabolomic profile has become of paramount importance in plants. Through the identification and quantification of biomarkers (key metabolites) and metabolic patterns, the discrimination between species, cultivars, development stages, pathogenic diseases, or environmental conditions (daily and seasonal changes, soils, water stress, mechanical stress, harvest and post- harvest treatments), among others, has been possible^3,4,5.

Mass spectrometry (MS) and Nuclear Magnetic Resonance (NMR) spectroscopy are the most widely used analytical platforms for this purpose. Contrary to MS methodologies, NMR stands as a highly reproducible, non-biased, quantitative, accurate, and non-destructive technique that requires minimal sample preparation, making it suitable for metabolomics studies. However, when compared to MS methods, the inherent low sensitivity is a limitation. In recent years and through the use of high-field magnets, cryogenic probes, micro-coil devices, and Dynamic Nuclear Polarization (DNP) methods, the sensitivity of NMR has been greatly improved. In the case of the latter approach, for instance, the sensitivity gain was in the level of two to three orders of magnitude^6,7. To date, almost 20% of the published metabolomics studies are NMR-based and the number is rising⁷.

Even though Proton NMR is the most popular and sensitive experiment for NMR metabolomics fingerprinting, it has some drawbacks. First, all the ¹H NMR signals detected in the sample are distributed in a small window corresponding to the proton chemical shift window, which results in crowded spectra. Second, the homonuclear scalar coupling splits the signals into multiple components (signal multiplicity), spreading the proton signal over a wider frequency range, complicating furthermore the spectra reading by increasing crowding and signal overlapping. In addition, NMR metabolomics is employed in the analysis of mixtures usually containing 50 to 300 molecules at an NMR observable concentration, generating complex spectra comprised of 200 to 2000 peaks.

Homonuclear decoupling proton NMR, also known as Pure Shift, is a method that induces the collapse of a multiplet signal into a single peak. It stands as an excellent tool for increasing signal resolution in crowded spectra^8,9,10 and therefore represents a convenient tool for plants metabolomics¹¹.

In the last decade, new Pure Shift pulse sequences, increasing both sensitivity and decoupling performance, have emerged. Their range of applications have also expanded, from molecular structure elucidation^12,13, to fluxomics¹⁴, mixture assignment^15,16,17, translational diffusion measurements¹⁸, enantiomeric discrimination¹⁹, unit distribution in co-polymers²⁰, among others.

Historically, Broadband Pure Shift experiments suffer from low sensitivity and complicated processing methods, limiting their scope in the assessment of biological extracts⁸. In 2014, Foroozandeh et al. published a new Pure Shift experiment, PSYCHE (Pure Shift Yielded by Chirp Excitation), based on anti-z-COSY pulse sequence which yielded excellent homonuclear decoupling and improved sensitivity values²¹. However, as PSYCHE is a 2D interferogram experiment where chunks of time domain data are acquired, it suffers from periodic sideband artifacts that result from J-coupling modulation distortions at the edges of the chunk. In complex mixtures, these artifacts yield signals larger than those associated with metabolites present at very low concentrations, hindering the analysis¹¹. There are two methods to remove these artifacts – TSE-PHYCHE²² and a more recent modification of the PSYCHE experiment called SAPPHIRE-PSYCHE (Sideband Averaging by Periodic PHase Incrementation of Residual J Evolution)²³.

In 2019, we demonstrated for the first time¹¹ that the SAPPHIRE-PSYCHE Pure Shift method, which removes artifacts with almost no sensitivity penalty²³, could be employed for the analysis of complex biological mixtures, such as extracts of the fruits of Physalis peruviana, commonly known as Cape gooseberries¹¹. We showed that these methods increase the performance of metabolomics data analyses such as metabolic assignment, correlation analysis and multivariate coefficients analysis¹¹. Since then, several Pure Shift metabolomics studies on different biological matrices, such as soft corals²⁴, hypericum plants²⁵, honey^26,27, tea²⁷, peppermint oil²⁶, and walnuts²⁸ have been addressed, demonstrating its importance as a new tool for metabolomics analysis. Paradoxically, the vast majority of these studies employed the standard and easy to implement PSYCHE pulse sequence, available from any spectrometer library, instead of the SAPPHIRE-PSYCHE pulse sequence, which has been shown to perform better. However, it requires better understanding of the pulse sequence for proper setup.

This paper is intended to help new users to apply Pure Shift methods in the study of plants, in particular, leaves of Vanilla sp (V. planifolia and V. pompona)²⁹, potato tubers (S. tuberosum)³⁰, and Cape gooseberries (P. peruviana)³¹. Sample preparation, NMR experimental set up, data acquisition, and data analysis are described in detail. Moreover, the protocol includes key notes to help researchers, new to the field, to properly set up PSYCHE and SAPPHIRE-PSYCHE experiments in the metabolomic profiling of plants.