Ultra-high pressure liquid chromatography–mass spectrometry for plant metabolomics: A systematic comparison of high-resolution quadrupole-time-of-flight and single stage Orbitrap mass spectrometers

A comprehensive metabolome profiling of Terminalia chebula, Terminalia bellerica, and Phyllanthus emblica to explore the medicinal potential of Triphala

Triphala is a traditional Ayurvedic herbal formulation composed of three fruits: amla (Phyllanthus emblica), bibhitaki (Terminalia bellerica), and haritaki (Terminalia chebula). Triphala is a potent Ayurvedic remedy that promotes digestion, detoxification, and overall wellness, while also providing antioxidant benefits through its trio of nutrient-rich fruits. In order to elucidate the individual contributions of the three ingredients of Triphala from molecular perspective, the individual ingredients were used for the untargeted LCMS/MS analysis. Fresh fruits (PE, TC, and TB) were collected, processed into coarse powders, and sequentially extracted {hexane, chloroform, and ethyl acetate}. LCMS/MS data analysis was performed on the resultant metabolites, with bioinformatics tools employed for pathway enrichment, target prediction, and classification of identified compounds. Additionally, polyphenols were identified as key compounds with potential health benefits. LCMS analysis of the individual extracts identified a total of 10227 features, resulting in 2515 annotated metabolites, with PE contributing the highest number at 1286. Comparative analysis revealed 408 non-redundant metabolites, with 74.2% being unique to individual fruits, underscoring the complementary phytochemical profiles. Pathway enrichment analysis highlighted dominant phenylpropanoid biosynthesis pathways across all extracts, while a comprehensive polyphenol classification identified 71 polyphenols, with significant interactions predicted between polyphenols and gut microbiota. Additionally, five common polyphenols showed potential human targets related to antioxidant activity. These findings provide a deeper understanding of the phytochemical diversity and potential health benefits of Triphala, supporting its traditional use in promoting health.

Climate-Affected Australian Tropical Montane Cloud Forest Plants: Metabolomic Profiles, Isolated Phytochemicals, and Bioactivities

The Australian Wet Tropics World Heritage Area (WTWHA) in northeast Queensland is home to approximately 18 percent of the nation's total vascular plant species. Over the past century, human activity and industrial development have caused global climate changes, posing a severe and irreversible danger to the entire land-based ecosystem, and the WTWHA is no exception. The current average annual temperature of WTWHA in northeast Queensland is 24 °C. However, in the coming years (by 2030), the average annual temperature increase is estimated to be between 0.5 and 1.4 °C compared to the climate observed between 1986 and 2005. Looking further ahead to 2070, the anticipated temperature rise is projected to be between 1.0 and 3.2 °C, with the exact range depending on future emissions. We identified 84 plant species, endemic to tropical montane cloud forests (TMCF) within the WTWHA, which are already experiencing climate change threats. Some of these plants are used in herbal medicines. This study comprehensively reviewed the metabolomics studies conducted on these 84 plant species until now toward understanding their physiological and metabolomics responses to global climate change. This review also discusses the following: (i) recent developments in plant metabolomics studies that can be applied to study and better understand the interactions of wet tropics plants with climatic stress, (ii) medicinal plants and isolated phytochemicals with structural diversity, and (iii) reported biological activities of crude extracts and isolated compounds.

A comparison of high- and low-resolution gas chromatography-mass spectrometry for herbal product classification: A case study with Ocimum essential oils

INTRODUCTION

Selection of marker compounds for targeted chemical analysis is complicated when considering varying instrumentation and closely related plant species. High-resolution gas chromatography-mass spectrometry (GC-MS), via orbitrap detection, has yet to be evaluated for improved marker compound selection.

OBJECTIVE

This study directly compares high- and low-resolution GC-MS for botanical maker compound selection using Ocimum tenuiflorum L. (OT) and Ocimum gratissimum L. (OG) for botanical ingredient authentication.

METHODS

The essential oils of OT and OG were collected via hydrodistillation before untargeted chemical analysis with gas chromatography coupled to single-quadrupole (GC-SQ) and orbitrap (GC-Orbitrap) detectors. The Global Natural Products Social Molecular Networking (GNPS) software was used for compound annotation, and a manual search was used to find the 41 most common Ocimum essential oil metabolites.

RESULTS

The GC-Orbitrap resulted in 1.7-fold more metabolite detection and increased dynamic range compared to the GC-SQ. Spectral matching and manual searching were improved with GC-Orbitrap data. Each instrument had differing known compound concentrations; however, there was an overlap of six compounds with higher abundance in OG than OT and three compounds with a higher abundance in OT than OG, suggesting consistent detection of the most variable compounds. An unsupervised principal component analysis (PCA) could not discern the two species with either dataset.

CONCLUSION

GC-Orbitrap instrumentation improves compound detection, dynamic range, and feature annotation in essential oil analysis. However, considering both high- and low-resolution data may improve reliable marker compound selection, as GC-Orbitrap analysis alone did not improve unsupervised separation of two Ocimum species compared to GC-SQ data.

Development of trisiloxane surfactant vesicles ultrasonic extraction method combined with ultrahigh-performance liquid chromatography tandem mass spectrometry for the rapid differentiation of Bupleuri Radix based on metabolomics

INTRODUCTION

Due to the variety, chemical composition and complex structure, the quality control of Bupleuri Radix (BR) is a challenging task. There are still many trace compounds in BR that are difficult to extract and detect.

OBJECTIVE

To develop an innovative method of trisiloxane surfactant vesicles ultrasonic extraction (TSVUE) combined with ultrahigh-performance liquid chromatography tandem mass spectrometry for the identification from Bupleurum chinense DC. (BC) to Bupleurum scorzonerifolium Willd (BS) based on metabolomics.

METHODS

Based on extraction effect for BR, five different types of surfactants vesicles were prepared and compared. Then, a single-factor test and a response surface methodology study were adopted to obtain the optimal conditions for the surfactant vesicles ultrasonic extraction method. Finally, a non-targeted metabolomics method with information dependent acquisition mode was performed to analyse differential metabolites in BC and BS.

RESULTS

Sugar-based surfactant containing trisiloxane [N-3-propyl-methyltrisiloxane-N-glucoheptonamne (Si(3)N-GHA)] displayed higher extraction efficiency compared to other types of surfactants when it comes to being used in pretreatment methods. And a TSVUE method was established and optimised. In total, 131 constituents were identified in two BR herbs, of which 35 were unreported, and 11 were characterised as chemical markers.

CONCLUSIONS

This method provides promising perspectives for rapidly identifying trace compounds in complex systems of traditional Chinese medicine (TCM), as well as for laying the foundation in the identification of similar herbs from the same species. Meanwhile, these findings serve as a promising application of trisiloxane surfactant vesicles in the extraction field of TCM.

Experimental Growth Conditions affect Direct and Indirect Defences in two Cotton Species

Cotton has been used as a model plant to study direct and indirect plant defence against herbivorous insects. However, the plant growing conditions could have an important effect on the outcome of such plant defence studies. We examined how common experimental growth conditions influence constitutive and inducible defences in two species of cotton, Gossypium hirsutum and G. herbaceum. We induced plants by applying caterpillar regurgitant to mechanical wounds to compare the induction levels between plants of both species grown in greenhouse or phytotron conditions. For this we measured defence metabolites (gossypol and heliocides) and performance of Spodoptera frugiperda caterpillars on different leaves, the emission of plant volatiles, and their attractiveness to parasitic wasps. Induction increased the levels of defence metabolites, which in turn decreased the performance of S. frugiperda larvae. Constitutive and induced defence levels were the highest in plants grown in the phytotron (compared to greenhouse plants), G. hirsutum and young leaves. Defence induction was more pronounced in plants grown in the phytotron and in young leaves. Also, the differences between growing conditions were more evident for metabolites in the youngest leaves, indicating an interaction with plant ontogeny. The composition of emitted volatiles was different between plants from the two growth conditions, with greenhouse-grown plants showing more variation than phytotron-grown plants. Also, G. hirsutum released higher amounts of volatiles and attracted more parasitic wasps than G. herbaceum. Overall, these results highlight the importance of experimental abiotic factors in plant defence induction and ontogeny of defences. We therefore suggest careful consideration in selecting the appropriate experimental growing conditions for studies on plant defences.

The effect of root hairs on exudate composition: a comparative non-targeted metabolomics approach

Root exudation is a major pathway of organic carbon input into soils. It affects soil physical properties, element solubility as well as speciation, and impacts the microbial community in the rhizosphere. Root exudates contain a large number of primary and secondary plant metabolites, and the amount and composition are highly variable depending on plant species and developmental stage. Detailed information about exudate composition will allow for a better understanding of exudate-driven rhizosphere processes and their feedback loops. Although non-targeted metabolomics by high-resolution mass spectrometry is an established tool to characterize root exudate composition, the extent and depth of the information obtained depends strongly on the analytical approach applied. Here, two genotypes of Zea mays L., differing in root hair development, were used to compare six mass spectrometric approaches for the analysis of root exudates. Reversed-phase liquid chromatography and hydrophilic interaction liquid chromatography combined with time-of-flight mass spectrometry (LC-TOF-MS), as well as direct infusion Fourier-transform ion cyclotron resonance mass spectrometry (DI-FT-ICR-MS), were applied with positive and negative ionization mode. By using the same statistical workflow, the six approaches resulted in different numbers of detected molecular features, ranging from 176 to 889, with a fraction of 48 to 69% of significant features (fold change between the two genotypes of > 2 and p-value < 0.05). All approaches revealed the same trend between genotypes, namely up-regulation of most metabolites in the root hair defective mutant (rth3). These results were in agreement with the higher total carbon and nitrogen exudation rate of the rth3-mutant as compared to the corresponding wild-type maize (WT). However, only a small fraction of features were commonly found across the different analytical approaches (20-79 features, 13-31% of the rth3-mutant up-regulated molecular formulas), highlighting the need for different mass spectrometric approaches to obtain a more comprehensive view into the composition of root exudates. In summary, 111 rth3-mutant up-regulated compounds (92 different molecular formulas) were detected with at least two different analytical approaches, while no WT up-regulated compound was found by both, LC-TOF-MS and DI-FT-ICR-MS. Zea mays L. exudate features obtained with multiple analytical approaches in our study were matched against the metabolome database of Zea mays L. (KEGG) and revealed 49 putative metabolites based on their molecular formula.

Eight key rules for successful data-dependent acquisition in mass spectrometry-based metabolomics

In recent years, metabolomics has emerged as a pivotal approach for the holistic analysis of metabolites in biological systems. The rapid progress in analytical equipment, coupled to the rise of powerful data processing tools, now provides unprecedented opportunities to deepen our understanding of the relationships between biochemical processes and physiological or phenotypic conditions in living organisms. However, to obtain unbiased data coverage of hundreds or thousands of metabolites remains a challenging task. Among the panel of available analytical methods, targeted and untargeted mass spectrometry approaches are among the most commonly used. While targeted metabolomics usually relies on multiple-reaction monitoring acquisition, untargeted metabolomics use either data-independent acquisition (DIA) or data-dependent acquisition (DDA) methods. Unlike DIA, DDA offers the possibility to get real, selective MS/MS spectra and thus to improve metabolite assignment when performing untargeted metabolomics. Yet, DDA settings are more complex to establish than DIA settings, and as a result, DDA is more prone to errors in method development and application. Here, we present a tutorial which provides guidelines on how to optimize the technical parameters essential for proper DDA experiments in metabolomics applications. This tutorial is organized as a series of rules describing the impact of the different parameters on data acquisition and data quality. It is primarily intended to metabolomics users and mass spectrometrists that wish to acquire both theoretical background and practical tips for developing effective DDA methods.

Alignment and Analysis of a Disparately Acquired Multibatch Metabolomics Study of Maternal Pregnancy Samples

Untargeted liquid chromatography-mass spectrometry metabolomics studies are typically performed under roughly identical experimental settings. Measurements acquired with different LC-MS protocols or following extended time intervals harbor significant variation in retention times and spectral abundances due to altered chromatographic, spectrometric, and other factors, raising many data analysis challenges. We developed a computational workflow for merging and harmonizing metabolomics data acquired under disparate LC-MS conditions. Plasma metabolite profiles were collected from two sets of maternal subjects three years apart using distinct instruments and LC-MS procedures. Metabolomics features were aligned using metabCombiner to generate lists of compounds detected across all experimental batches. We applied data set-specific normalization methods to remove interbatch and interexperimental variation in spectral intensities, enabling statistical analysis on the assembled data matrix. Bioinformatics analyses revealed large-scale metabolic changes in maternal plasma between the first and third trimesters of pregnancy and between maternal plasma and umbilical cord blood. We observed increases in steroid hormones and free fatty acids from the first trimester to term of gestation, along with decreases in amino acids coupled to increased levels in cord blood. This work demonstrates the viability of integrating nonidentically acquired LC-MS metabolomics data and its utility in unconventional metabolomics study designs.

Rapid discovery of potentially vasodilative compounds from Uncaria by UHPLC/Q-Orbitrap-MS based metabolomics and correlation analysis

The genus Uncaria belongs to the family of Rubiaceae, which contains approximately 34 species. It has been widely used as a traditional Chinese medicine (TCM) in China to treat hypertension, fevers, headaches, gastrointestinal illness, epilepsy, wounds, and ulcers. Uncaria rhynchophylla. (Miq.) Miq. ex Hvail.(URM) and Uncaria hirsuta Havil.(UHH) are mainly used as remedies for hypertension, which both belong to the resource of "Gou-teng" in the Chinese Pharmacopoeia. However, the authentic antihypertensive components of Uncaria still have not been fully elucidated until now. In this work, we firstly explored and compared the vasorelaxation effect of URM and UHH on the isolated rat mesenteric artery ring. Then, the variations of metabolite profiles between URM and UHH samples were investigated by UHPLC/Q-Orbitrap-MS, and 16 different metabolites have been found through multivariate statistical analysis. Further, the potential vasodilative compounds which include corynoxeine, isocorynoxeine, isorhynchophylline, rhynchophylline, hirsuteine and hirsutine were screened through the correlation analysis between metabolites and anti-hypertension activities. And the relaxation effects of the six compounds on the mesenteric artery have verified. The results indicated that metabolomics combined with correlation analysis could be effective strategies to rapid explore the active compounds from TCM.

Metabolomics of Chlorophylls and Carotenoids: Analytical Methods and Metabolome-Based Studies

Chlorophylls and carotenoids are two families of antioxidants present in daily ingested foods, whose recognition as added-value ingredients runs in parallel with the increasing number of demonstrated functional properties. Both groups include a complex and vast number of compounds, and extraction and analysis methods evolved recently to a modern protocol. New methodologies are more potent, precise, and accurate, but their application requires a better understanding of the technical and biological context. Therefore, the present review compiles the basic knowledge and recent advances of the metabolomics of chlorophylls and carotenoids, including the interrelation with the primary metabolism. The study includes material preparation and extraction protocols, the instrumental techniques for the acquisition of spectroscopic and spectrometric properties, the workflows and software tools for data pre-processing and analysis, and the application of mass spectrometry to pigment metabolomics. In addition, the review encompasses a critical description of studies where metabolomics analyses of chlorophylls and carotenoids were developed as an approach to analyzing the effects of biotic and abiotic stressors on living organisms.

FT-ICR-MS-based metabolomics: A deep dive into plant metabolism

Metabolomics involves the identification and quantification of metabolites to unravel the chemical footprints behind cellular regulatory processes and to decipher metabolic networks, opening new insights to understand the correlation between genes and metabolites. In plants, it is estimated the existence of hundreds of thousands of metabolites and the majority is still unknown. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a powerful analytical technique to tackle such challenges. The resolving power and sensitivity of this ultrahigh mass accuracy mass analyzer is such that a complex mixture, such as plant extracts, can be analyzed and thousands of metabolite signals can be detected simultaneously and distinguished based on the naturally abundant elemental isotopes. In this review, FT-ICR-MS-based plant metabolomics studies are described, emphasizing FT-ICR-MS increasing applications in plant science through targeted and untargeted approaches, allowing for a better understanding of plant development, responses to biotic and abiotic stresses, and the discovery of new natural nutraceutical compounds. Improved metabolite extraction protocols compatible with FT-ICR-MS, metabolite analysis methods and metabolite identification platforms are also explored as well as new in silico approaches. Most recent advances in MS imaging are also discussed.

Metabolic effect of drought stress on the leaves of young oil palm (Elaeis guineensis) plants using UHPLC-MS and multivariate analysis

The expansion of the oil palm in marginal areas can face challenges, such as water deficit, leading to an impact on palm oil production. A better understanding of the biological consequences of abiotic stresses on this crop can result from joint metabolic profiling and multivariate analysis. Metabolic profiling of leaves was performed from control and stressed plants (7 and 14 days of stress). Samples were extracted and analyzed on a UHPLC-ESI-Q-TOF-HRMS system. Acquired data were processed using XCMS Online and MetaboAnalyst for multivariate and pathway activity analysis. Metabolism was affected by drought stress through clear segregation between control and stressed groups. More importantly, metabolism changed through time, gradually from 7 to 14 days. The pathways most affected by drought stress were: starch and sucrose metabolism, glyoxylate and dicarboxylate metabolism, alanine, aspartate and glutamate metabolism, arginine and proline metabolism, and glycine, serine and threonine metabolism. The analysis of the metabolic profile were efficient to correlate and differentiate groups of oil palm plants submitted to different levels of drought stress. Putative compounds and their affected pathways can be used in future multiomics analysis.

Ultra-high-performance liquid chromatography high-resolution mass spectrometry variants for metabolomics research

Ultra-high-performance liquid chromatography high-resolution mass spectrometry (UHPLC-HRMS) variants currently represent the best tools to tackle the challenges of complexity and lack of comprehensive coverage of the metabolome. UHPLC offers flexible and efficient separation coupled with high-sensitivity detection via HRMS, allowing for the detection and identification of a broad range of metabolites. Here we discuss current common strategies for UHPLC-HRMS-based metabolomics, with a focus on expanding metabolome coverage.

Analysis of Nucleosides and Nucleotides in Plants: An Update on Sample Preparation and LC-MS Techniques

Nucleotides fulfill many essential functions in plants. Compared to non-plant systems, these hydrophilic metabolites have not been adequately investigated in plants, especially the less abundant nucleotide species such as deoxyribonucleotides and modified or damaged nucleotides. Until recently, this was mainly due to a lack of adequate methods for in-depth analysis of nucleotides and nucleosides in plants. In this review, we focus on the current state-of-the-art of nucleotide analysis in plants with liquid chromatography coupled to mass spectrometry and describe recent major advances. Tissue disruption, quenching, liquid-liquid and solid-phase extraction, chromatographic strategies, and peculiarities of nucleotides and nucleosides in mass spectrometry are covered. We describe how the different steps of the analytical workflow influence each other, highlight the specific challenges of nucleotide analysis, and outline promising future developments. The metabolite matrix of plants is particularly complex. Therefore, it is likely that nucleotide analysis methods that work for plants can be applied to other organisms as well. Although this review focuses on plants, we also discuss advances in nucleotide analysis from non-plant systems to provide an overview of the analytical techniques available for this challenging class of metabolites.

Untargeted Metabolomics Analysis by UHPLC-MS/MS of Soybean Plant in a Compatible Response to Phakopsora pachyrhizi Infection

Phakopsora pachyrhizi is a biotrophic fungus, causer of the disease Asian Soybean Rust, a severe crop disease of soybean and one that demands greater investment from producers. Thus, research efforts to control this disease are still needed. We investigated the expression of metabolites in soybean plants presenting a resistant genotype inoculated with P. pachyrhizi through the untargeted metabolomics approach. The analysis was performed in control and inoculated plants with P. pachyrhizi using UHPLC-MS/MS. Principal component analysis (PCA) and the partial least squares discriminant analysis (PLS-DA), was applied to the data analysis. PCA and PLS-DA resulted in a clear separation and classification of groups between control and inoculated plants. The metabolites were putative classified and identified using the Global Natural Products Social Molecular Networking platform in flavonoids, isoflavonoids, lipids, fatty acyls, terpenes, and carboxylic acids. Flavonoids and isoflavonoids were up-regulation, while terpenes were down-regulated in response to the soybean–P. pachyrhizi interaction. Our data provide insights into the potential role of some metabolites as flavonoids and isoflavonoids in the plant resistance to ASR. This information could result in the development of resistant genotypes of soybean to P. pachyrhizi, and effective and specific products against the pathogen.

LC-MS Data set on the Malayan Deer (Cervus timorensis) Antler Velvet and its antibiofilm activity against Candida species: LC-MS Data set on the Malayan Deer (Cervus timorensis) Antler Velvet and its antibiofilm properties against Candida species

Deer antler velvet (DAV) has been traditionally used in Chinese medicine, including treatment on toothache [1]. Due to its rapid and regenerative capacity, deer antlers were proposed to be the good model for bone remodelling in mammals [2]. The data presented in this work is on the liquid chromatography and mass spectrometry (LC-MS) profile and bioactive potential of Malayan deer antler velvet (DAV) on different Candida species that has clinical importance. Aqueous extraction of DAV samples was subjected to Liquid chromatography quadrupole time of flight mass spectrometry (LC-QTOF-MS) profiling. Reverse phase (RP) separation was used due to the process extraction using water as a solvent to separate polar compound. The data was interpreted using Profile Analysis 2.1V. The DAV samples were also tested for the effect on the biofilm formation of seven Candida species in a 96 well plate [3]. The biofilms were developed for 72 h in aerobic environment. Following that, the biofilms biomass was determined using crystal violet assay.

Collision energies on QTof and Orbitrap instruments: How to make proteomics measurements comparable?

Quadrupole time-of-flight (QTof) collision-induced dissociation (CID) and Orbitrap higher-energy collisional dissociation (HCD) are the most commonly used fragmentation techniques in mass spectrometry-based proteomics workflows. The information content of the MS/MS spectra is first and foremost determined by the applied collision energy. How can we set up the two instrument types to achieve maximum transferability? To answer this question, we compared MS/MS spectra obtained on a Bruker QTof CID and a Thermo Q-Exactive Focus Orbitrap HCD instrument as a function of collision energy using the similarity index. Results show that with a few eV lower collision energy setting on HCD (Orbitrap-specific CID) than on QTof CID, nearly identical MS/MS spectra can be obtained for leucine enkephalin pentapeptide standard, for selected +2 and +3 enolase tryptic peptides and for a large number of peptides in a HeLa protein digest. The Bruker QTof was able to produce colder ions, which may be significant to study inherently labile compounds. Further, we examined energy dependence of peptide identification confidence, as characterized by Mascot scores, on the HeLa peptides. In line with earlier QTof results, this dependence shows one or two maxima (unimodal or bimodal behavior) on Orbitrap. The fraction of bimodal peptides is lower on Orbitrap. Optimal energies as a function of m/z show a similar linear trend on both instruments, which suggests that with appropriate collision energy adjustment, matching conditions for proteomics can be achieved. Data have been deposited in the MassIVE repository (MSV000086434).

High-resolution mass spectrometry for bioanalytical applications: Is this the new gold standard?

Liquid chromatography coupled to quadrupole-based tandem mass spectrometry (QqQ) is termed the "gold standard" for bioanalytical applications because of its unpreceded selectivity, sensitivity, and the ruggedness of the technology. More recently, however, high-resolution mass spectrometry (HRMS) has become increasingly popular for bioanalytical applications. Nonetheless, this technique is still viewed, either as a screening technology or as a research tool. Although HRMS is actively discussed during scientific conferences, it is yet to be widely utilised in routine laboratory settings and there remains a reluctance to use HRMS for quantitative measurements in regulated environments. This paper does not aim to comprehensively describe the potential of the latest HRMS technology, but rather, it focuses on what results can be obtained and outlines the author's experiences over a period of many years of the routine application of various forms of HRMS instrumentation. Fifteen years ago, some nine different QqQ methods were used in the author's laboratory to analyse a variety of different veterinary drug resides. Today, many more analytes are quantified by seven HRMS methods and just three QqQ methods remain in use for the analysis of a small set of compounds yet to be upgraded to HRMS analysis. This continual upgrading and migration of analytical methods were accompanied by regularly participating in laboratory proficiency tests (PTs). The PT reports (covering a range of analytes and analytical methods) were used to compare the accuracy of HRMS- versus QqQ-based measurements. In the second part of this paper, the particular strengths and limitations of HRMS for both method development and routine measurements are critically discussed. This also includes some anecdotal experiences encountered when replacing QqQ assays with HRMS methods.

R, Shaari K, Ismail IS, Nagao N, Abas F. Mass Spectrometry-Based Metabolomics Combined with Quantitative Analysis of the Microalgal Diatom (Chaetoceros calcitrans)

Although many metabolomics studies of higher land plant species have been conducted, similar studies of lower nonland plant species, which include microalgae, are still developing. The present study represents an attempt to characterize the metabolic profile of a microalgal diatom Chaetoceros calcitrans, by applying high-resolution mass spectrometry detection, via Q-ExactiveTM Plus Orbitrap mass spectrometry. The results showed that 54 metabolites of various classes were tentatively identified. Experimentally, the chloroform and acetone extracts were clearly distinguished from other solvent extracts in chemometric regression analysis using PLS, showing the differences in the C. calcitrans metabolome between the groups. In addition, specific metabolites were evaluated, which supported the finding of antioxidant and anti-inflammatory activities. This study also provides data on the quantitative analysis of four carotenoids based on the identification results. Therefore, these findings could serve as a reliable tool for identifying and quantifying the metabolome that could reflect the metabolic activities of C. calcitrans.

Metabolomics Benefits from Orbitrap GC-MS-Comparison of Low- and High-Resolution GC-MS

The development of improved mass spectrometers and supporting computational tools is expected to enable the rapid annotation of whole metabolomes. Essential for the progress is the identification of strengths and weaknesses of novel instrumentation in direct comparison to previous instruments. Orbitrap liquid chromatography (LC)–mass spectrometry (MS) technology is now widely in use, while Orbitrap gas chromatography (GC)–MS introduced in 2015 has remained fairly unexplored in its potential for metabolomics research. This study aims to evaluate the additional knowledge gained in a metabolomics experiment when using the high-resolution Orbitrap GC–MS in comparison to a commonly used unit-mass resolution single-quadrupole GC–MS. Samples from an osmotic stress treatment of a non-model organism, the microalga Skeletonema costatum, were investigated using comparative metabolomics with low- and high-resolution methods. Resulting datasets were compared on a statistical level and on the level of individual compound annotation. Both MS approaches resulted in successful classification of stressed vs. non-stressed microalgae but did so using different sets of significantly dysregulated metabolites. High-resolution data only slightly improved conventional library matching but enabled the correct annotation of an unknown. While computational support that utilizes high-resolution GC–MS data is still underdeveloped, clear benefits in terms of sensitivity, metabolic coverage, and support in structure elucidation of the Orbitrap GC–MS technology for metabolomics studies are shown here.

Mass Spectrometry-Based Untargeted Plant Metabolomics

Metabolomics has grown into one of the major approaches for systems biology studies, in part driven by developments in mass spectrometry (MS), providing high sensitivity and coverage of the metabolome at high throughput. Untargeted metabolomics allows for the investigation of metabolic phenotypes involving several hundreds to thousands of metabolites. In this approach, all signals in a mass chromatogram are processed in an unbiased way, allowing for a deeper investigation of metabolic phenotypes, but also resulting in significantly more complex data processing and post-processing steps. In this article, we discuss all the intricacies involved in extracting and analyzing metabolites by chromatography coupled to MS, as well as the processing and analysis of such datasets. © 2019 The Authors. Basic Protocol 1: Metabolite extraction for LC-MS Alternate Protocol: Methyl tert-butyl ether (MTBE) extraction for multiple mass spectrometry platforms (GC-polar, LC-polar, LC-lipid) Basic Protocol 2: LC-MS analysis Support Protocol 1: GC-MS derivatization and analysis Support Protocol 2: Lipid analysis Basic Protocol 3: LC-MS data processing Basic Protocol 4: Data analysis Basic Protocol 5: Metabolite annotation Support Protocol 3: Molecular networking using MetNet Support Protocol 4: Co-injection of authentic standards.

Hormonal and metabolic responses of Mexican lime plants to CTV infection

Plant viral infections alter gene expression and metabolism in infected host. To study the molecular responses of Mexican lime to CTV infection, an analysis of plant metabolome in response to infection with severe (T318) or mild (T385) isolates of CTV was performed. Healthy plants and those infected with any of the two virus strains showed different metabolite profiles, at different stages of new sprout development. Proline content increased in plants infected with CTV, proportionally to the virulence of the virus strain. Abscisic acid content decreased after virus infection whereas jasmonic and salicylic acid levels increased. CTV infection had an impact on plant secondary metabolism, by stimulating the synthesis of different metabolites such as l-methylhistidine, phenylpropanoid derivatives. These metabolites are common responses of different organisms, including higher mammals, to viral diseases, and its presence in this system points to the existence of universal responses to virus infection among different kingdoms.

Rapid Discovery of the Potential Toxic Compounds in Polygonum multiflorum by UHPLC/Q-Orbitrap-MS-Based Metabolomics and Correlation Analysis

The dry roots of Polygonum multiflorum (PM), involving both the raw and processed materials, are widely used as the traditional Chinese medicine for treating various diseases in China. Hepatotoxicity has been occasionally reported in patients who consume PM. Unfortunately, no definite criteria are currently available regarding the processing technology of PM for reduction the toxicity. In this work, we aimed to investigate the variations of PM metabolite profiles induced by different processing technologies by UHPLC/Q-Orbitrap-MS and multivariate statistical analysis, and to discover the potential toxic compounds by correlating the cytotoxicity of L02 cell with the contents of metabolites in raw and processed PM samples. We could identify two potential toxic compounds, emodin-8-O-glucoside and torachrysone-O-hexose, which could be selected as the toxic markers to evaluate different processing methods. The results indicated all processed PM samples could decrease the cytotoxicity on L02 cell. The best processing technology for PM process was to steam PM in black soybean decoction (BD-PM) for 24 h.

Investigation of Drug-Packaging Interactions with Mass Spectroscopy Detectors: A Meta-Synthesis of the Literature

Background

The production of hospital-compounded medicines with a longer shelf life raises questions about drug-packaging interactions, especially desorption events involving extractables and leachables (E/L). A meta-synthesis of the literature was performed to describe which mass spectrometer is suitable for identifying and quantifying E/L.

Methods

A meta-synthesis of studies focused on the identification or quantification of E/L published between January 1997 and December 2017 was performed. Inclusion criteria were E/L studies dealing with pharmaceutical products, in which mass spectrometry (MS) coupled to liquid chromatography (LC) or gas chromatography (GC) was used. The full-text articles had to be available and written in English. Articles about food packaging, environmental contamination, counterfeit compounds, pharmacokinetics, or process-related impurity studies were excluded. Two researchers independently assessed the papers according to a score based on a seven-item questionnaire.

Results

In total, 32 papers matched our criteria and were included in the meta-synthesis. For qualitative analysis with LC, quadrupole time-of-flight (QTOF; n=4) and ion trap (n=4) mass detectors were used the most; and with GC, single quadrupole (n=8). For quantification studies with LC, QTOF (n=3) and triple quadrupole (n=2) were used the most; and with GC, single quadrupole (n=7).

Conclusions

For simultaneous qualitative and quantitative analysis of E/L with LC, QTOF or Orbitrap is a suitable detector. For quantitative analysis with LC only, triple quadrupole is suitable. For qualitative and quantitative analysis with GC, single quadrupole can be used.

LC-MS data for metabolomics analysis of Garcinia mangostana L. seed germination

Metabolic regulation is important during seed germination for the establishment of seedling. The germination strategy of mangosteen (Garcinia mangostana L.) seed is thought to be unique due to its recalcitrant characteristic (sensitive to coldness and drying). To investigate the metabolic changes during seed germination, we performed metabolomics analysis on germinating mangosteen seed sown after zero, one, three, five, seven and nine days. Sampled mangosteen seeds were subjected to methanol extraction prior analysis using Liquid Chromatography-Time of Flight-Mass Spectrometry (LC-TOF-MS). MS data were further analyzed using ProfileAnalysis (version 2.1). This is one of the earliest reports in metabolite identification and profiling of mangosteen seed at different germination stages. This data article refers to the article entitled “Metabolite profiling of mangosteen seed germination highlights metabolic changes related to carbon utilization and seed protection” (Mazlan et al., 2019) [1].

Metabolic fingerprinting analysis of oil palm reveals a set of differentially expressed metabolites in fatal yellowing symptomatic and non-symptomatic plants

INTRODUCTION

Oil palm (E. guineensis), the most consumed vegetable oil in the world, is affected by fatal yellowing (FY), a condition that can lead to the plant's death. Although studies have been performed since the 1980s, including investigations of biotic and abiotic factors, FY's cause remains unknown and efforts in researches are still necessary.

OBJECTIVES

This work aims to investigate the metabolic expression in plants affected by FY using an untargeted metabolomics approach.

METHOD

Metabolic fingerprinting analysis of oil palm leaves was performed using ultra high liquid chromatography-electrospray ionization-mass spectrometry (UHPLC-ESI-MS). Chemometric analysis, using principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA), was applied to data analysis. Metabolites identification was performed by high resolution mass spectrometry (HRMS), MS/MS experiments and comparison with databases and literature.

RESULTS

Metabolomics analysis based on MS detected more than 50 metabolites in oil palm leaf samples. PCA and PLS-DS analysis provided group segregation and classification of symptomatic and non-symptomatic FY samples, with a great external validation of the results. Nine differentially expressed metabolites were identified as glycerophosphorylcholine, arginine, asparagine, apigenin 6,8-di-C-hexose, tyramine, chlorophyllide, 1,2-dihexanoyl-sn-glycero-3-phosphoethanolamine, proline and malvidin 3-glucoside-5-(6″-malonylglucoside). Metabolic pathways and biological importance of those metabolites were assigned.

CONCLUSION

Nine metabolites were detected in a higher concentration in non-symptomatic FY plants. Seven are related to stress factors i.e. plant defense and nutrient absorption, which can be affected by the metabolic depression of these compounds. Two of those metabolites (glycerophosphorylcholine and 1,2-dihexanoyl-sn-glycero-3-phosphoethanolamine) are presented as potential biomarkers, since they have no known direct relation to plant stress.

Integration of non-targeted metabolomics and automated determination of elemental compositions for comprehensive alkaloid profiling in plants

Plants produce a large array of specialized metabolites to protect themselves. Among these allelochemicals, alkaloids display highly diverse and complex structures that are directly related to their biological activities. Plant alkaloid profiling traditionally requires extensive and time-consuming sample preparation and analysis. Herein, we developed a rapid and efficient approach for the comprehensive profiling of alkaloids in plants using ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS)-based metabolomics. Using automated compound extraction and elemental composition assignment, our method achieved >83% correct alkaloid identification and even >90% for medium to high intensity peaks. This represented a significant improvement in identification rate compared to generic methods used for EC determination with no a priori, such as in untargeted metabolomics studies. The developed approach was then applied to identify specific alkaloids of Aconitum lycoctonum L. and A. napellus L. (Ranunculaceae) using different parts of the plant (leaf, perianth and pollen). Significant differences in alkaloid profiles between the two species were highlighted and discussed under taxonomic and evolutionary perspectives. Taken together, the presented approach constitutes a valuable chemotaxonomic tool in the search for known and unknown alkaloids from plants.

Analytical performance of the various acquisition modes in Orbitrap MS and MS/MS

Quadrupole Orbitrap instruments (Q Orbitrap) permit high-resolution mass spectrometry-based full scan acquisitions and have a number of acquisition modes where the quadrupole isolates a particular mass range prior to a possible fragmentation and high-resolution mass spectrometry-based acquisition. Selecting the proper acquisition mode(s) is essential if trace analytes are to be quantified in complex matrix extracts. Depending on the particular requirements, such as sensitivity, selectivity of detection, linear dynamic range, and speed of analysis, different acquisition modes may have to be chosen. This is particularly important in the field of multi-residue analysis (eg, pesticides or veterinary drugs in food samples) where a large number of analytes within a complex matrix have to be detected and reliably quantified. Meeting the specific detection and quantification performance criteria for every targeted compound may be challenging. It is the aim of this paper to describe the strengths and the limitations of the currently available Q Orbitrap acquisition modes. In addition, the incorporation of targeted acquisitions between full scan experiments is discussed. This approach is intended to integrate compounds that require an additional degree of sensitivity or selectivity into multi-residue methods.

Mass spectrometry in untargeted liquid chromatography/mass spectrometry metabolomics: Electrospray ionisation parameters and global coverage of the metabolome

RATIONALE

Liquid chromatography coupled to mass spectrometry (LC/MS) is a dominant analytical platform in metabolomics, because of the high sensitivity and resolution, thus enabling large-scale coverage of metabolomes. Correspondingly, electrospray ionisation (ESI) is the favoured ionisation method in untargeted LC/MS metabolomics given the ability to produce large numbers of ions. In the workflow of LC/ESI-MS metabolomics, maximising the ionisation efficiency over a wide mass range is inevitably an essential and determining step, subsequently defining the extent of coverage of the metabolome under investigation. Thus in this study, electronic factors related to the functioning of the ESI source, namely the capillary and sample cone voltages, were explored to investigate the influence on the data acquired in metabolomic investigations.

METHODS

Hydromethanolic samples from an untargeted study (sorghum plants responding dynamically to fungal infection) were analysed on a high-resolution/definition LC/ESI-MS system. Here the capillary and sample cone voltages of the ZSpray™ ESI source were varied between 1.5-3.0 kV and 10.0-40.0 V, respectively. The acquired data were processed with MarkerLynx™ software and analysed using central composite design response surface methodology and chemometric approaches (principal component analysis and orthogonal projection latent structures-discriminant analysis).

RESULTS

The results evidently demonstrate that both capillary and sampling cone voltages not only significantly influence the recorded MS signals with regard to the number and abundance of features, but also the overall structure of the collected data. This consequently impacts on the information extracted from the data and thus affects coverage of the metabolome.

CONCLUSIONS

The observations postulate in that, untargeted LC/MS metabolomics, 'what you see is what you ionise'. Although there is convergence of collected data under different ESI conditions, the nuances observed indicate that the exploration of different ion source settings could be the best trade-off in expanding and maximising the metabolome coverage in untargeted metabolomic experiments.

Validating Quantitative Untargeted Lipidomics Across Nine Liquid Chromatography-High-Resolution Mass Spectrometry Platforms

Liquid chromatography-mass spectrometry (LC-MS) methods are most often used for untargeted metabolomics and lipidomics. However, methods have not been standardized as accepted "best practice" documents, and reports lack harmonization with respect to quantitative data that enable interstudy comparisons. Researchers use a wide variety of high-resolution mass spectrometers under different operating conditions, and it is unclear if results would yield different biological conclusions depending on the instrument performance. To this end, we used 126 identical human plasma samples and 29 quality control samples from a nutritional intervention study. We investigated lipidomic data acquisitions across nine different MS instruments (1 single TOF, 1 Q/orbital ion trap, and 7 QTOF instruments). Sample preparations, chromatography conditions, and data processing methods were kept identical. Single-point internal standard calibrations were used to estimate absolute concentrations for 307 unique lipids identified by accurate mass, MS/MS spectral match, and retention times. Quantitative results were highly comparable between the LC-MS platforms tested. Using partial least-squares discriminant analysis (PLS-DA) to compare results between platforms, a 92% overlap for the most discriminating lipids based on variable importance in projection (VIP) scores was achieved for all lipids that were detected by at least two instrument platforms. Importantly, even the relative positions of individual samples on the PLS-DA projections were identical. The key for success in harmonizing results was to avoid ion saturation by carefully evaluating linear dynamic ranges using serial dilutions and adjusting the resuspension volume and/or injection volume before running actual study samples.

Metabolite profiling of non-sterile rhizosphere soil

Rhizosphere chemistry is the sum of root exudation chemicals, their breakdown products and the microbial products of soil-derived chemicals. To date, most studies about root exudation chemistry are based on sterile cultivation systems, which limits the discovery of microbial breakdown products that act as semiochemicals and shape microbial rhizosphere communities. Here, we present a method for untargeted metabolic profiling of non-sterile rhizosphere soil. We have developed an experimental growth system that enables the collection and analysis of rhizosphere chemicals from different plant species. High-throughput sequencing of 16SrRNA genes demonstrated that plants in the growth system support a microbial rhizosphere effect. To collect a range of (a)polar chemicals from the system, we developed extraction methods that do not cause detectable damage to root cells or soil-inhabiting microbes, thus preventing contamination with cellular metabolites. Untargeted metabolite profiling by UPLC-Q-TOF mass spectrometry, followed by uni- and multivariate statistical analyses, identified a wide range of secondary metabolites that are enriched in plant-containing soil, compared with control soil without roots. We show that the method is suitable for profiling the rhizosphere chemistry of Zea mays (maize) in agricultural soil, thereby demonstrating the applicability to different plant-soil combinations. Our study provides a robust method for the comprehensive metabolite profiling of non-sterile rhizosphere soil, which represents a technical advance towards the establishment of causal relationships between the chemistry and microbial composition of the rhizosphere.

Liquid chromatography-high resolution mass spectrometry for the analysis of phytochemicals in vegetal-derived food and beverages

The recent years witnessed a change in the perception of nutrition. Diet does not only provide nutrients to meet the metabolic requirements of the body, but it also constitutes an active way for the consumption of compounds beneficial for human health. Fruit and vegetables are an excellent source of such compounds, thus the growing interest in characterizing phytochemical sources, structures and activities. Given the interest for phytochemicals in food, the development of advanced and suitable analytical techniques for their identification is fundamental for the advancement of food research. In this review, the state of the art of phytochemical research in food plants is described, starting from sample preparation, throughout extract clean-up and compound separation techniques, to the final analysis, considering both qualitative and quantitative investigations. In this regard, from an analytical point of view, fruit and vegetable extracts are complex matrices, which greatly benefit from the use of modern hyphenated techniques, in particular from the combination of high performance liquid chromatography separation and high resolution mass spectrometry, powerful tools which are being increasingly used in the recent years. Therefore, selected applications to real samples are presented and discussed, in particular for the analysis of phenols, polyphenols and phenolic acids. Finally, some hot points are discussed, such as waste characterization for high value-compounds recovery and the untargeted metabolomics approach.

Evaluation of the Nutritional Quality of Chinese Kale (Brassica alboglabra Bailey) Using UHPLC-Quadrupole-Orbitrap MS/MS-Based Metabolomics

Chinese kale (Brassica alboglabra Bailey) is a widely consumed vegetable which is rich in antioxidants and anticarcinogenic compounds. Herein, we used an untargeted ultra-high-performance liquid chromatography (UHPLC)-Quadrupole-Orbitrap MS/MS-based metabolomics strategy to study the nutrient profiles of Chinese kale. Seven Chinese kale cultivars and three different edible parts were evaluated, and amino acids, sugars, organic acids, glucosinolates and phenolic compounds were analysed simultaneously. We found that two cultivars, a purple-stem cultivar W1 and a yellow-flower cultivar Y1, had more health-promoting compounds than others. The multivariate statistical analysis results showed that gluconapin was the most important contributor for discriminating both cultivars and edible parts. The purple-stem cultivar W1 had higher levels of some phenolic acids and flavonoids than the green stem cultivars. Compared to stems and leaves, the inflorescences contained more amino acids, glucosinolates and most of the phenolic acids. Meanwhile, the stems had the least amounts of phenolic compounds among the organs tested. Metabolomics is a powerful approach for the comprehensive understanding of vegetable nutritional quality. The results provide the basis for future metabolomics-guided breeding and nutritional quality improvement.

A discovery-driven approach to elucidate urinary metabolome changes after a regular and moderate consumption of beer and nonalcoholic beer in subjects at high cardiovascular risk

SCOPE

The aim of this work was to study the urinary metabolomics changes of participants that consumed beer, nonalcoholic beer (na-beer), and gin.

METHODS AND RESULTS

Thirty-three males at high cardiovascular risk between 55 and 75 years old participated in an open, randomized, crossover, controlled trial with three nutritional interventions consisting of beer, na-beer, and gin for 4 wk. Diet and physical activity was monitored throughout the study and compliance was assessed by measurement of urinary isoxanthohumol. Metabolomic analysis was performed in urine samples by LC coupled to an LTQ-Orbitrap mass spectrometer combined with univariate and multivariate statistical analysis. Ten metabolites were identified. Eight were exogenous metabolites related to beer, na-beer, or gin consumption, but two of them were related to endogenic changes: hydroxyadipic acid linked to fatty acid oxidation, and 4-guanidinobutanoic acid, which correlated with a decrease in urinary creatinine. Plasmatic acylcarnitines were quantified by targeted MS. A regular and moderate consumption of beer and na-beer decreased stearoylcarnitine concentrations.

CONCLUSION

Humulinone and 2,3-dihydroxy-3-methylvaleric acid showed to be potential biomarkers of beer and na-beer consumption. Moreover, the results of this trial provide new evidence that the nonalcoholic fraction of beer may increase fatty oxidation.

Strategy for comparative untargeted metabolomics reveals honey markers of different floral and geographic origins using ultrahigh-performance liquid chromatography-hybrid quadrupole-orbitrap mass spectrometry

Honey discrimination based on floral and geographic origins is limited by the ability to determine reliable markers because developing hypothetical substances in advance considerably limits the throughput of metabolomics studies. Here, we present a novel approach to screen and elucidate honey markers based on comparative untargeted metabolomics using ultrahigh-performance liquid chromatography-hybrid quadrupole-orbitrap mass spectrometry (UHPLC-Q-Orbitrap). To reduce metabolite information losses during sample preparation, the honey samples were dissolved in water and centrifuged to remove insoluble particles prior to UHPLC-Q-Orbitrap analysis in positive and negative electrospray ionization modes. The data were pretreated using background subtraction, chromatographic peak extraction, normalization, transformation and scaling to remove interferences from unwanted biases and variance in the experimental data. The pretreated data were further processed using principal component analysis (PCA) and a three-stage approach (t-test, volcano plot and variable importance in projection (VIP) plot) to ensure marker authenticity. A correlation between the molecular and fragment ions with a mass accuracy of less than 1.0ppm was used to annotate and elucidate the marker structures, and the marker responses in real samples were used to confirm the effectiveness of the honey discrimination. Moreover, we evaluated the data quality using blank and quality control (QC) samples based on PCA clustering, retention times, normalized levels and peak areas. This strategy will help guide standardized, comparative untargeted metabolomics studies of honey and other agro-products from different floral and geographic origins.

Control of sexuality by the sk1-encoded UDP-glycosyltransferase of maize

Sex determination in maize involves the production of staminate and pistillate florets from an initially bisexual floral meristem. Pistil elimination in staminate florets requires jasmonic acid signaling, and functional pistils are protected by the action of the silkless 1 (sk1) gene. The sk1 gene was identified and found to encode a previously uncharacterized family 1 uridine diphosphate glycosyltransferase that localized to the plant peroxisomes. Constitutive expression of an sk1 transgene protected all pistils in the plant, causing complete feminization, a gain-of-function phenotype that operates by blocking the accumulation of jasmonates. The segregation of an sk1 transgene was used to effectively control the production of pistillate and staminate inflorescences in maize plants.

Modern mass spectrometry for synthetic biology and structure-based discovery of natural products

Covering: up to 2016In this highlight, we describe the current landscape for dereplication and discovery of natural products based on the measurement of the intact mass by LC-MS. Often it is assumed that because better mass accuracy (provided by higher resolution mass spectrometers) is necessary for absolute chemical formula determination (≤1 part-per-million), that it is also necessary for dereplication of natural products. However, the average ability to dereplicate tapers off at ∼10 ppm, with modest improvement gained from better mass accuracy when querying focused databases of natural products. We also highlight some recent examples of how these platforms are applied to synthetic biology, and recent methods for dereplication and correlation of substructures using tandem MS data. We also offer this highlight to serve as a brief primer for those entering the field of mass spectrometry-based natural products discovery.

Extending the Dynamic Range in Metabolomics Experiments by Automatic Correction of Peaks Exceeding the Detection Limit

Metabolomics, the analysis of potentially all small molecules within a biological system, has become a valuable tool for biomarker identification and the elucidation of biological processes. While metabolites are often present in complex mixtures at extremely different concentrations, the dynamic range of available analytical methods to capture this variance is generally limited. Here, we show that gas chromatography coupled to atmospheric pressure chemical ionization mass spectrometry (GC-APCI-MS), a state of the art analytical technology applied in metabolomics analyses, shows an average linear range (LR) of 2.39 orders of magnitude for a set of 62 metabolites from a representative compound mixture. We further developed a computational tool to extend this dynamic range on average by more than 1 order of magnitude, demonstrated with a dilution series of the compound mixture, using robust and automatic reconstruction of intensity values exceeding the detection limit. The tool is freely available as an R package (CorrectOverloadedPeaks) from CRAN ( https://cran.r-project.org/ ) and can be incorporated in a metabolomics data processing pipeline facilitating large screening assays.

Stable Isotope-Assisted Evaluation of Different Extraction Solvents for Untargeted Metabolomics of Plants

The evaluation of extraction protocols for untargeted metabolomics approaches is still difficult. We have applied a novel stable isotope-assisted workflow for untargeted LC-HRMS-based plant metabolomics , which allows for the first time every detected feature to be considered for method evaluation. The efficiency and complementarity of commonly used extraction solvents, namely 1 + 3 (v/v) mixtures of water and selected organic solvents (methanol, acetonitrile or methanol/acetonitrile 1 + 1 (v/v)), with and without the addition of 0.1% (v/v) formic acid were compared. Four different wheat organs were sampled, extracted and analysed by LC-HRMS. Data evaluation was performed with the in-house-developed MetExtract II software and R. With all tested solvents a total of 871 metabolites were extracted in ear, 785 in stem, 733 in leaf and 517 in root samples, respectively. Between 48% (stem) and 57% (ear) of the metabolites detected in a particular organ were found with all extraction mixtures, and 127 of 996 metabolites were consistently shared between all extraction agent/organ combinations. In aqueous methanol, acidification with formic acid led to pronounced pH dependency regarding the precision of metabolite abundance and the number of detectable metabolites, whereas extracts of acetonitrile-containing mixtures were less affected. Moreover, methanol and acetonitrile have been found to be complementary with respect to extraction efficiency. Interestingly, the beneficial properties of both solvents can be combined by the use of a water-methanol-acetonitrile mixture for global metabolite extraction instead of aqueous methanol or aqueous acetonitrile alone.

Liquid chromatography-quadrupole time of flight tandem mass spectrometry-based targeted metabolomic study for varietal discrimination of grapes according to plant sterols content

Grapevine and derived products are rich in a wide range of compounds and its quality mainly depends on its metabolites, as a result of viticulture practices. Plant sterols, also called phytosterols (PS), are secondary metabolites regarded as bioactive substance present in grape berries and other plant-based food. The present study deals with a metabolomic approach focusing on phytosterols family in six varieties of Rioja grapes (Cabernet Sauvignon, Tempranillo, Graciano, Garnacha, White Garnacha and Viura), in order to find significant differences among them. Liquid chromatography- mass spectrometry with a quadrupole-time of flight mass analyzer (LC-QTOF) was used to find as many metabolites as possible in the different grape berry fractions, and using statistics to help finding significant clustering of the metabolic profile of pulp, peel and seeds in relation to the variety. The best chromatographic and detection conditions were achieved by gas phase ionization via atmospheric pressure chemical ionization (APCI) in positive mode. Furthermore, analysis with electrospray (ESI) is also needed for phytosterol derivatives confirmation. Putative compounds of interest in the analyzed samples were found by an automated compound extraction algorithm (Molecular Feature Extraction, MFE) and an initial differential expression from the data was created with the aid of commercial software. Once the data were collected, the results were filtered, aligned and normalized, and evaluating applying one-way analysis of variance (ANOVA) with a 95% significance level. For sample class prediction, partial least square-discriminant analysis (PLS-DA) is used as a supervised pattern recognition method and excellent separation among the grape varieties is shown. An overall accuracy of 93.3% (pulp samples), 100.0% (peel) or 96.7% (seeds) in discriminating between grape varieties was achieved when comparing the different fractions. In general, 7 PS derivatives were identified with ID scores higher than 84%.

Metabolomics for the Authentication of Natural Extracts Used in Flavors and Fragrances: the Case Study of Violet Leaf Absolutes from Viola odorata

Natural extracts used in fine fragrances (alcoholic perfumes) are rare and precious. As such, they represent an interesting target for fraudulent practices called adulterations. Absolutes, important materials used in the creation of perfumes, are obtained by organic solvent extraction of raw plant materials. Because the nonvolatile part of these natural extracts is not normalized and scarcely reported, highlighting potential adulterations present in this fraction appears highly challenging. For the first time, we investigated the use of nontargeted UHPLC-ToFMS metabolomics for this purpose, considering Viola odorata l., a plant largely used in the perfume industry, as a model. Significant differences in the metabolic fingerprints of the violet leaf absolutes were evidenced according to geographical locations, and/or adulterations. Additionally, markers of the geographical origin were detected through their molecular weight/most probable molecular formula and retention time, while adulterations were statistically validated. In this study, we thus clearly demonstrated the efficiency of UHPLC-ToFMS-based metabolomics in accelerating both the identification of the origin of raw materials as well as the search for potential adulterations in absolutes, natural products of high added value.

Metabolite Damage and Metabolite Damage Control in Plants

It is increasingly clear that (a) many metabolites undergo spontaneous or enzyme-catalyzed side reactions in vivo, (b) the damaged metabolites formed by these reactions can be harmful, and (c) organisms have biochemical systems that limit the buildup of damaged metabolites. These damage-control systems either return a damaged molecule to its pristine state (metabolite repair) or convert harmful molecules to harmless ones (damage preemption). Because all organisms share a core set of metabolites that suffer the same chemical and enzymatic damage reactions, certain damage-control systems are widely conserved across the kingdoms of life. Relatively few damage reactions and damage-control systems are well known. Uncovering new damage reactions and identifying the corresponding damaged metabolites, damage-control genes, and enzymes demands a coordinated mix of chemistry, metabolomics, cheminformatics, biochemistry, and comparative genomics. This review illustrates the above points using examples from plants, which are at least as prone to metabolite damage as other organisms.

Told through the wine: A liquid chromatography-mass spectrometry interplatform comparison reveals the influence of the global approach on the final annotated metabolites in non-targeted metabolomics

This work focuses on the influence of the selected LC-HRMS platform on the final annotated compounds in non-targeted metabolomics. Two platforms that differed in columns, mobile phases, gradients, chromatographs, mass spectrometers (Orbitrap [Platform#1] and Q-TOF [Platform#2]), data processing and marker selection protocols were compared. A total of 42 wines samples from three different protected denomination of origin (PDO) were analyzed. At the feature level, good (O)PLS-DA models were obtained for both platforms (Q(2)[Platform#1]=0.89, 0.83 and 0.72; Q(2)[Platform#2]=0.86, 0.86 and 0.77 for Penedes, Ribera del Duero and Rioja wines respectively) with 100% correctly classified samples in all cases. At the annotated metabolite level, platforms proposed 9 and 8 annotated metabolites respectively which were identified by matching standards or the MS/MS spectra of the compounds. At this stage, there was no coincidence among platforms regarding the suggested metabolites. When screened on the raw data, 6 and 5 of these compounds were detected on the other platform with a similar trend. Some of the detected metabolites showed complimentary information when integrated on biological pathways. Through the use of some examples at the annotated metabolite level, possible explanations of this initial divergence on the results are presented. This work shows the complications that may arise on the comparison of non-targeted metabolomics platforms even when metabolite focused approaches are used in the identification.