Analysis of highly polar compounds of plant origin: combination of hydrophilic interaction chromatography and electrospray ion trap mass spectrometry

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

A widely targeted metabolite modificomics strategy for modified metabolites identification in tomato

The structural and functional diversity of plant metabolites is largely created via chemical modification of a basic backbone. However, metabolite modifications in plants have still not been thoroughly investigated by metabolomics approaches. In this study, a widely targeted metabolite modificomics (WTMM) strategy was developed based on ultra-high performance liquid chromatography-quadrupole-linear ion trap (UHPLC-Q-Trap) and UHPLC-Q-Exactive-Orbitrap (UHPLC-QE-Orbitrap), which greatly improved the detection sensitivity and the efficiency of identification of modified metabolites. A metabolite modificomics study was carried out using tomato as a model, and over 34,000 signals with MS2 information were obtained from approximately 232 neutral loss transitions. Unbiased metabolite profiling was also performed by utilizing high-resolution mass spectrometry data to annotate a total of 2,118 metabolites with 125 modification types; of these, 165 modified metabolites were identified in this study. Next, the WTMM database was used to assess diseased tomato tissues and 29 biomarkers were analyzed. In summary, the WTMM strategy is not only capable of large-scale detection and quantitative analysis of plant-modified metabolites in plants, but also can be used for plant biomarker development.

Hexosamine biosynthesis and related pathways, protein N-glycosylation and O-GlcNAcylation: their interconnection and role in plants

N-Acetylglucosamine (GlcNAc), a fundamental amino sugar moiety, is essential for protein glycosylation, glycolipid, GPI-anchor protein, and cell wall components. Uridine diphosphate-GlcNAc (UDP-GlcNAc), an active form of GlcNAc, is synthesized through the hexosamine biosynthesis pathway (HBP). Although HBP is highly conserved across organisms, the enzymes involved perform subtly distinct functions among microbes, mammals, and plants. A complete block of HBP normally causes lethality in any life form, reflecting the pivotal role of HBP in the normal growth and development of organisms. Although HBP is mainly composed of four biochemical reactions, HBP is exquisitely regulated to maintain the homeostasis of UDP-GlcNAc content. As HBP utilizes substrates including fructose-6-P, glutamine, acetyl-CoA, and UTP, endogenous nutrient/energy metabolites may be integrated to better suit internal growth and development, and external environmental stimuli. Although the genes encoding HBP enzymes are well characterized in microbes and mammals, they were less understood in higher plants in the past. As the HBP-related genes/enzymes have largely been characterized in higher plants in recent years, in this review we update the latest advances in the functions of the HBP-related genes in higher plants. In addition, HBP's salvage pathway and GlcNAc-mediated two major co- or post-translational modifications, N-glycosylation and O-GlcNAcylation, are also included in this review. Further knowledge on the function of HBP and its product conjugates, and the mechanisms underlying their response to deleterious environments might provide an alternative strategy for agricultural biofortification in the future.

Hydrophilic Peptide and Glycopeptide as Immobilized Sorbents for Glycosylation Analysis

Hydrophilic interaction liquid chromatography (HILIC) is widely used for glycopeptide enrichment in shot-gun glycoproteomics to enhance the glycopeptide signal and minimize the ionization competition of peptides. In this work, we have developed a novel hydrophilic material (glycoHILIC) based on glycopeptides and peptides to provide hydrophilic properties. GlycoHILIC was synthesized by oxidizing cotton and then reacting the resulting aldehyde with the N-terminus of the glycopeptide or peptide by reductive amination. Due to the large amount of hydrophilic carbohydrates and hydrophilic amino acids contained in glycopeptides, glycoHILIC showed significantly better enrichment of glycopeptides than cotton itself. Our results demonstrate that glycoHILIC has high selectivity, a low detection limit, and good stability. Over 257 unique N-linked glycosylation sites in 1477 intact N-glycopeptides from 146 glycoproteins were identified from 1 μL of human serum using glycoHILIC. Serum analysis of pancreatic cancer patients found that 38 N-glycopeptides among 21 glycoproteins changed significantly, of which 7 N-glycopeptides increased and 31 N-glycopeptides decreased. These results demonstrate that glycoHILIC can be used for glycopeptide enrichment and analysis.

Studying Plant Specialized Metabolites Using Computational Metabolomics Strategies

Plant specialized metabolites have diversified vastly over the course of plant evolution, and they are considered key players in complex interactions between plants and their environment. The chemical diversity of these metabolites has been widely explored and utilized in agriculture and crop enhancement, the food industry, and drug development, among other areas. However, the immensity of the plant metabolome can make its exploration challenging. Here we describe a protocol for exploring plant specialized metabolites that combines high-resolution mass spectrometry and computational metabolomics strategies, including molecular networking, identification of structural motifs, as well as prediction of chemical structures and metabolite classes.

Application of metabolomics in diagnostics and differentiation of meningitis: A narrative review with a critical approach to the literature

Due to its high mortality rate associated with various life-threatening sequelae, meningitis poses a vital problem in contemporary medicine. Numerous algorithms, many of which were derived with the aid of artificial intelligence, were brought up in a strive for perfection in predicting the status of sepsis-related survival or exacerbation. This review aims to provide key insights on the contextual utilization of metabolomics. The aim of this the metabolomic approach set of methods can be used to investigate both bacterial and host metabolite sets from both the host and its microbes in several types of specimens - even in one's breath, mainly with use of two methods - Mass Spectrometry (MS) and Nuclear Magnetic Resonance (NMR). Metabolomics, and has been used to elucidate the mechanisms underlying disease development and metabolic identification changes in a wide range of metabolite contents, leading to improved methods of diagnosis, treatment, and prognosis of meningitis. Mass spectrometry (MS) and Nuclear Magnetic Resonance (NMR) are the main analytical platforms used in metabolomics. Its high sensitivity accounts for the usefulness of metabolomics in studies into meningitis, its sequelae, and concomitant comorbidities. Metabolomics approaches are a double-edged sword, due to not only their flexibility, but also - high complexity, as even minor changes in the multi-step methods can have a massive impact on the results. Information on the differential diagnosis of meningitis act as a background in presenting the merits and drawbacks of the use of metabolomics in context of meningeal infections.

Metabolomics of Personalized Body Elements in Thai Traditional Medicine Response to Herbal Medicine for Body Elements Balancing in Healthy Volunteers

Background

In Thai traditional medicine (TTM), the dominant body element called "Dhat Chao Ruean" (DCR) is an integral part in the diagnostic process of Thai traditional medicine. TTM practitioners usually use Thai herbal Benjakul formula (BKF) for adjusting and balancing the body elements. However, the effects of BKF on metabolism and individual response to it have not been studied yet.

Methods

This study proposed to investigate the metabolic profiling in 24 volunteers categorized by their types of birth month DCR (bDCR) after the administration of BKF (450 mg, three tablets three times a day before meals) for seven days. Differences in metabolic profiling between bDCR groups were investigated by using liquid chromatography coupled with mass spectrometry for untargeted analysis, and in addition, the safety was assessed by testing the plasma biochemical level.

Results

This study identified 57 biomarkers in positive ESI and 12 in negative ESI. Piperine was found in varying amount among the participants but it was the highest in the earth group. In addition, this study found that elemicin, phenylpropionic acid, ricinoleic acid, and β-sitosterol are important substances in a single herb of BKF. Regarding biochemical tests, the results indicated that BKF can decrease the lipid profile and it has no toxic effects on liver and kidney functions.

Conclusion

The findings indicated that it is safe to use BKF which can help to improve health in chronic diseases by adjusting abnormality of the elements of the body. In addition, the information gathered from this study is valuable for further study in the field of Thai traditional medicine.

Saliva biomarkers in oral disease

Saliva is a versatile biofluid that contains a wide variety of biomarkers reflecting both physiologic and pathophysiologic states. Saliva collection is noninvasive and highly applicable for tests requiring serial sampling. Furthermore, advances in test accuracy, sensitivity and precision for saliva has improved diagnostic performance as well as the identification of novel markers especially in oral disease processes. These include dental caries, periodontitis, oral squamous cell carcinoma (OSCC) and Sjögren's syndrome (SS). Numerous growth factors, enzymes, interleukins and cytokines have been identified and are the subject of much research investigation. This review highlights current procedures for successful determination of saliva biomarkers including preanalytical factors associated with sampling, storage and pretreatment as well as subsequent analysis. Moreover, it provides an overview of the diagnostic applications of these salivary biomarkers in common oral diseases.

Simultaneous Quantification of Trehalose and Trehalose 6-Phosphate by Hydrophilic Interaction Chromatography/Electrospray Accurate Mass Spectrometry with Application in Non-Targeted Metabolomics

High-resolution mass spectrometry (HRMS) was coupled with ultra-high-performance liquid chromatography (UHPLC) to simultaneously quantify trehalose and trehalose 6-phosphate without derivatization or sample preparation. The use of full scan mode and exact mass analysis also makes it possible to carry out metabolomic analyses as well as semi-quantification. In addition, the use of different clusters in negative mode makes it possible to compensate for deficiencies in linearity and inerrant saturation at time-of-flight detectors. The method has been approved and validated for different matrices, yeasts, and bacteria, and has shown differentiation between bacteria as a function of growth temperatures.

Comparative metabolic profiling of olive leaf extracts from twelve different cultivars collected in both fruiting and flowering seasons

Olea europaea is an economically significant crop native to Mediterranean countries. Its leaves exhibit several biological properties associated to their chemical composition. The aqueous ethanolic extracts of olive leaves from twelve different cultivars were analyzed by high performance liquid chromatography coupled to photodiode array and electrospray ionization mass spectrometry (HPLC/PDA/ESI-MS/MS). A total of 49 phytochemicals were identified in both positive and negative ionization modes. The identified compounds belonged to four classes of secondary metabolites including secoiridoids, flavonoids, pentacyclic triterpenoids and various phenolic compounds. Seasonal variation in chemical composition among the studied cultivars was apparent in autumn and spring. Secologanoside, oleuropein, hydroxy-oleuropein, demethyl oleuropein, gallocatechin, luteolin-O-hexoside, diosmetin, oleanolic acid and maslinic acid were detected in all cultivars in both seasons. Oleuropein-O-deoxyhexoside was tentatively identified for the first time in olive leaf extracts; detected only in the Spanish cultivar Picual (PIC) collected in spring. Also, dihydroxy-oxooleanenoic acid and hydroxy-oxooleanenoic acid, two bioactive pentacyclic triterpenes, were identified. Principle component analysis (PCA) showed good discrimination among the studied cultivars in terms of their botanical origin. This study is considered the first study for non-targeted metabolic profiling of different olive leaf cultivars cultivated in Egypt.

Metabolomics-centered mining of plant metabolic diversity and function: Past decade and future perspectives

Plants are natural experts in organic synthesis, being able to generate large numbers of specific metabolites with widely varying structures that help them adapt to variable survival challenges. Metabolomics is a research discipline that integrates the capabilities of several types of research including analytical chemistry, statistics, and biochemistry. Its ongoing development provides strategies for gaining a systematic understanding of quantitative changes in the levels of metabolites. Metabolomics is usually performed by targeting either a specific cell, a specific tissue, or the entire organism. Considerable advances in science and technology over the last three decades have propelled us into the era of multi-omics, in which metabolomics, despite at an earlier developmental stage than genomics, transcriptomics, and proteomics, offers the distinct advantage of studying the cellular entities that have the greatest influence on end phenotype. Here, we summarize the state of the art of metabolite detection and identification, and illustrate these techniques with four case study applications: (i) comparing metabolite composition within and between species, (ii) assessing spatio-temporal metabolic changes during plant development, (iii) mining characteristic metabolites of plants in different ecological environments and upon exposure to various stresses, and (iv) assessing the performance of metabolomics as a means of functional gene identification , metabolic pathway elucidation, and metabolomics-assisted breeding through analyzing plant populations with diverse genetic variations. In addition, we highlight the prominent contributions of joint analyses of plant metabolomics and other omics datasets, including those from genomics, transcriptomics, proteomics, epigenomics, phenomics, microbiomes, and ion-omics studies. Finally, we discuss future directions and challenges exploiting metabolomics-centered approaches in understanding plant metabolic diversity.

Integration of solid phase extraction with HILIC-MS/MS for analysis of free amino acids in source water

Amino acids (AAs) are prevalent in source water, particularly during spring run-off. Monitoring of amino acids in source water is desirable for water treatment plants (WTP) to indicate changes in source water quality. The objective of this study was to establish analytical procedures for reliable monitoring of amino acids in source water. Therefore, we examined two different methods, large volume inject (LVI) and solid phase extraction (SPE), for sample preparation prior to HILIC-MS/MS. The LVI-HILIC-MS/MS method can provide fast and sensitive detection for clean samples, but suffers from matrix effects, resulting in irreproducible separation and shortening column lifetime. We have demonstrated that SPE was necessary prior to HILIC-MS/MS to achieve reproducible and reliable quantification of AAs in source water. A natural heterocyclic amine 1-methyl-1,2,3,4-tetrahydro-β-carboline-3-carboxylic acid (MTCCA) was also included in the method to indicate changes in other natural nitrogenous compounds in source water. The SPE-HILIC-MS/MS method was able to achieve limits of detection from 2.6-3400 ng/L for the amino acids and MTCCA with RSDs (n=3) of 1.1%-4.8%. As well, retention times (RT) of the analytes were reproducible with variation less than 0.01 min (n=3) through the entire project. We further applied the SPE-HILIC-MS/MS method to determine AAs in authentic source water samples collected from two drinking water treatment plants (WTPs) during the 2021 spring run-off season. The results support that the SPE-HILIC-MS/MS method does not require derivatization and can provide reliable, accurate, and robust analysis of AAs and MTCCA in source water, supporting future monitoring of source water quality.

Rapid and simultaneous determination of histidine metabolism intermediates in human and mouse microbiota and biomatrices

Histidine metabolism is a key pathway physiologically involved in satiety, recognition memory, skin, and neural protection and allergic diseases. Microbiologically-produced imidazole propionate induces type II diabetes and interferes with glucose lowering drugs. Despite their determinant health implications, no single method simultaneously assesses histidine metabolites in urine, feces, and microbiota. The aim of this study was to develop a simple, rapid, and sensitive method for the determination of histidine and its major bioactive metabolites histamine, N-acetylhistamine, imidazole-4-acetate, cis-urocanate, trans-urocanate, glutamate and imidazole propionate, using ultrahigh-performance liquid chromatography with electrospray ionization tandem mass spectrometry. An innovative simple extraction method from small aliquots of human and mice urine, feces and microbial cell extracts was coupled to separation in a 6.5 min chromatographic run. The successful performance allowed accurate and precise quantification of all metabolites in mouse feces, suggesting broad exchange of histidine metabolites between the gut and mice. Higher urine histamine, histamine to histidine ratio, and imidazole-4-acetate pointed to an underlying inflammatory or allergic process in mice compared to human subjects. N-acetylhistamine and imidazole propionate were detected in human and mouse feces, confirming its origin from gut microbial metabolism. Our novel and robust analytical method captured histidine metabolism in a single assay that will facilitate broad and deep histidine metabolic phenotyping assessing the impact of microbiota on host health in large-scale human observational and interventional studies.

Profiling Urinary Sulfate Metabolites With Mass Spectrometry

The study of urinary phase II sulfate metabolites is central to understanding the role and fate of endogenous and exogenous compounds in biological systems. This study describes a new workflow for the untargeted metabolic profiling of sulfated metabolites in a urine matrix. Analysis was performed using ultra-high-performance liquid chromatography-high resolution tandem mass spectrometry (UHPLC-HRMS/MS) with data dependent acquisition (DDA) coupled to an automated script-based data processing pipeline and differential metabolite level analysis. Sulfates were identified through k-means clustering analysis of sulfate ester derived MS/MS fragmentation intensities. The utility of the method was highlighted in two applications. Firstly, the urinary metabolome of a thoroughbred horse was examined before and after administration of the anabolic androgenic steroid (AAS) testosterone propionate. The analysis detected elevated levels of ten sulfated steroid metabolites, three of which were identified and confirmed by comparison with synthesised reference materials. This included 5α-androstane-3β,17α-diol 3-sulfate, a previously unreported equine metabolite of testosterone propionate. Secondly, the hydrolytic activity of four sulfatase enzymes on pooled human urine was examined. This revealed that Pseudomonas aeruginosa arylsulfatases (PaS) enzymes possessed higher selectivity for the hydrolysis of sulfated metabolites than the commercially available Helix pomatia arylsulfatase (HpS). This novel method provides a rapid tool for the systematic, untargeted metabolic profiling of sulfated metabolites in a urinary matrix.

Metabolomics in Parkinson's disease

Parkinson's disease (PD) is a multifactorial neurodegenerative disorder in which environmental (lifestyle, dietary, infectious disease) factors as well as genetic make-up play a role. Metabolomics, an evolving research field combining biomarker discovery and pathogenetics, is particularly useful in studying complex pathophysiology in general and Parkinson's disease (PD) specifically. PD, the second most frequent neurodegenerative disorder, is characterized by the loss of dopaminergic neurons in the substantia nigra and the presence of intraneural inclusions of α-synuclein aggregates. Although considered a predominantly movement disorder, PD is also associated with number of non-motor features. Metabolomics has provided useful information regarding this neurodegenerative process with the aim of identifying a disease-specific fingerprint. Unfortunately, many disease variables such as clinical presentation, motor system involvement, disease stage and duration substantially affect biomarker relevance. As such, metabolomics provides a unique approach to studying this multifactorial neurodegenerative disorder.

Determination of 1-Deoxynojirimycin (1-DNJ) in Leaves of Italian or Italy-Adapted Cultivars of Mulberry (Morus sp.pl.) by HPLC-MS

Recently, 1-DNJ has been widely studied by scientists for its capacity to inhibit α-glucosidase and reduce postprandial blood glucose and fat accumulation. To the best of our knowledge, this is the first analytical determination of 1-DNJ in Morus sp.pl. leaves carried out on Italian crops, and it could be used as a reference to assess the quality of the plant material in comparison to Far Eastern Asia cultivations. The effects of two thermal treatments were compared to test the incidence of the drying process on the 1-DNJ extractability. In addition, two harvesting seasons in the same year (2017) and two subsequent harvesting years (2017-2018) were considered. The amount of 1-DNJ herein found was comparable to that reported in the scientific literature for Asian cultivations. The increase in 1-DNJ along the summer and the higher level of this compound in the apical leaves also complies with previous findings. However, a strong implication for the climatic conditions in the different years and a significant interaction between climate and genotypes suggest exploring very carefully the agronomic practices and selecting cultivars according to different environmental conditions with a view to standardize the 1-DNJ amount in leaves.

Current and future deep learning algorithms for tandem mass spectrometry (MS/MS)-based small molecule structure elucidation

RATIONALE

Structure elucidation of small molecules has been one of the cornerstone applications of mass spectrometry for decades. Despite the increasing availability of software tools, structure elucidation from tandem mass spectrometry (MS/MS) data remains a challenging task, leaving many spectra unidentified. However, as an increasing number of reference MS/MS spectra are being curated at a repository scale and shared on public servers, there is an exciting opportunity to develop powerful new deep learning (DL) models for automated structure elucidation.

ARCHITECTURES

Recent early-stage DL frameworks mostly follow a "two-step approach" that translates MS/MS spectra to database structures after first predicting molecular descriptors. The related architectures could suffer from: (1) computational complexity because of the separate training of descriptor-specific classifiers, (2) the high dimensional nature of mass spectral data and information loss due to data preprocessing, (3) low substructure coverage and class imbalance problem of predefined molecular fingerprints. Inspired by successful DL frameworks employed in drug discovery fields, we have conceptualized and designed hypothetical DL architectures to tackle the above issues. For (1), we recommend multitask learning to achieve better performance with fewer classifiers by grouping structurally related descriptors. For (2) and (3), we introduce feature engineering to extract condensed and higher-order information from spectra and structure data. For instance, encoding spectra with subtrees and pre-calculated spectral patterns add peak interactions to the model input. Encoding structures with graph convolutional networks incorporates connectivity within a molecule. The joint embedding of spectra and structures can enable simultaneous spectral library and molecular database search.

CONCLUSIONS

In principle, given enough training data, adapted DL architectures, optimal hyperparameters and computing power, DL frameworks can predict small molecule structures, completely or at least partially, from MS/MS spectra. However, their performance and general applicability should be fairly evaluated against classical machine learning frameworks.

Microbial dynamics, metabolomic profiles, and the correlation between them during fermentation of serofluid dish

BACKGROUND

Serofluid dish, a traditional Chinese fermented food, possesses unique flavors and health beneficial effects. These properties are likely due to the sophisticated metabolic networks during fermentation, which are mainly driven by microbiota. However, the exact roles of metabolic pathways and the microbial community during this process remain equivocal.

RESULTS

Here, we investigated the microbial dynamics by next-generation sequencing, and outlined a differential non-targeted metabolite profiling in the process of serofluid dish fermentation using the method of hydrophilic interaction liquid chromatography column with ultra-high-performance liquid chromatography-quadruple time-of-flight mass spectrometry. Lactobacillus was the leading genus of bacteria, while Pichia and Issatchenkia were the dominant fungi. They all accumulated during fermentation. In total, 218 differential metabolites were identified, of which organic acids, amino acids, sugar and sugar alcohols, fatty acids, and esters comprised the majority. The constructed metabolic network showed that tricarboxylic acid cycle, urea cycle, sugar metabolism, amino acids metabolism, choline metabolism, and flavonoid metabolism were regulated by the fermentation. Furthermore, correlation analysis revealed that the leading fungi, Pichia and Issatchenkia, were linked to organic acids, amino acid and sugar metabolism, flavonoids, and several other flavor and functional components. Antibacterial tests indicated the antibacterial effect of serofluid soup against Salmonella and Staphylococcus.

CONCLUSION

This work provides new insights into the complex microbial and metabolic networks during serofluid dish fermentation, and a theoretical basis for the optimization of its industrial production. © 2020 Society of Chemical Industry.

Simultaneous analysis of monosaccharides using ultra high performance liquid chromatography-high resolution mass spectrometry without derivatization for validation of certified reference materials

Monosaccharide composition of biological samples can reflect an individual's health status. Monitoring the concentration of individual monosaccharides in human serum requires a technique for the simultaneous analysis of multiple monosaccharide molecules. Furthermore, certified reference materials (CRMs) for overall monosaccharide composition of human serum are required in order to validate the performance of clinical laboratory instruments. In the present study, we present a novel method for the simultaneous analysis of numerous monosaccharide molecules without the need for derivatization or post-column treatment. We utilized ultra-high-performance liquid chromatography (UHPLC)-quadrupole/orbitrap mass spectrometry incorporating a hydrophilic interaction chromatography (HILIC) column. We optimized the precursor ions, product ions, mobile phase composition and gradient program, flow rate, and column temperature. Seven monosaccharides (D-Ribose, L-Arabinose, D-Xylose, D-Fructose, D-Mannose, D-Galactose and D-Glucose) were able to be separated and quantified. We validated the method and the seven molecules showed favorable limits of detection and quantification, recovery rates, carry-over effects, intra- and inter-day accuracy and precision, resolution, and measurement uncertainty. We analyzed human serum samples using the method. To avoid ion suppression and D-d₂-Glucose peak interference, compounds present at concentrations outside of the calibration range were analyzed from diluted samples. Quantification of serum samples corroborated some previous clinical research, in that increased D-Glucose concentration was associated with increased concentrations of D-Mannose and D-Ribose. We also validated the CRMs, and expect these to have utility as standards for serum monosaccharide profiling, thus contributing to public health.

Variation in secondary metabolites in a unique set of tomato accessions collected in Turkey

In this study, 50 tomato landraces grown in Turkey were investigated in terms of their secondary metabolite profiles. Each accession was planted in 2016 and 2017 in 3 replicates in an open field. In this study, color, pH and brix of the fruit samples were measured and an unbiased LCMS-based metabolomics approach was applied. Based on Principal Components Analysis (PCA) and Hierarchical Cluster Analysis (HCA) of the relative abundance levels of >250 metabolites, it could be concluded that fruit size was the most influential to the biochemical composition, rather than the geographical origin of accessions. Results indicated substantial biodiversity in various metabolites generally regarded as key to fruit quality aspects, including sugars; phenolic compounds like phenylpropanoids and flavonoids; alkaloids and glycosides of flavour-related volatile compounds. The phytochemical data provides insight into which Turkish accessions might be most promising as starting materials for the tomato processing and breeding industries.

A click tyrosine zwitterionic stationary phases for hydrophilic interaction liquid chromatography

New zwitterionic (ZIC) stationary phases (SPs) are synthesized with the click and conventional bonding of tyrosine to silica gel. Infrared spectra and elemental analysis demonstrate the successful click and conventional bonding of this ZIC group on silica particles by the surface coverage including 2.36 and 0.75 µm m^-2, respectively. Given the above-mentioned explanation, the present study evaluated the retention mechanism and chromatographic manners of polar compounds on these new materials under hydrophilic interaction liquid chromatography (HILIC) conditions. Based on the results, the Click-Tyrosine Stationary Phase provided good HILIC characteristics when it was applied to separate phenolic compounds, amino acids, alkaloids, and nucleobases compared to bare silica gel SP and even conventional tyrosine SPs. Further, this new Click-Tyrosine-SP represented appropriate HILIC features and column efficiency (the theoretical plate number was up to 50,000 plates m^-1 for thebaine). Furthermore, the study investigated the effect of solute polarity (the number of the hydroxyl group of phenolic compounds) and hydrophobicity (the number of the side chain of aliphatic amino acids) on retention behaviors. Finally, some important factors were studied as the potential variables for guiding the retention behavior of the polar compound in HILIC condition including solvent composition, salt concentration, and the buffer pH of the mobile phase.

Quantitation of amino sugar stereoisomer and muramic acid biomarkers by hydrophilic interaction liquid chromatography-mass spectrometry

A rapid separation and quantitation of the stereoisomer amino sugars glucosamine, galactosamine, and mannosamine, along with muramic acid, is needed. These compounds, when their quantities are accurate, can be used to understand the origin and fate of natural organic matter (NOM) in the environment. These target molecules are biomarkers of fungi and bacteria and allow the deconvolution of microbial transformations and degradation of NOM in a wide variety of environmental matrices. Analytical methods applied to this suite of biomarkers are needed to understand carbon and nitrogen biogeochemistry with a changing global climate. Traditional separations of these analytes by gas chromatography require sample derivatization, as does reverse phase liquid chromatography. In contrast, ion chromatography can separate the analytes directly, but requires a separate analytical method to quantify muramic acid. In this work we present a direct analysis of all these molecules using hydrophilic liquid interaction chromatography. Solvent composition, buffer strength, pH, flow rate, and column temperature were optimized. The method can separate these four compounds and the biopolymeric precursor molecule N-acetylglucosamine in a single run in under 8 min with equivalent resolution to the best previously reported separations that did not require derivatization prior to analysis. Detection of the analytes was performed by both tandem and time-of-flight mass spectrometry. The method was assessed for its quantitative capabilities through i) peak area assignment, ii) check standards with ratios of the target analytes likely to be present in real samples, iii) an injection internal standard, and iv) quantitative analysis of real soil hydrolysates by external calibration and standard addition approaches. Across their expected analytical ranges the response for each analyte was highly linear with good accuracy (<25%) and precision (<15%) over three orders of magnitude. Detection limits of 20 µg L^-1 were found for galactosamine and 5 µg L^-1 for the remainder of the analytes, comparable to the majority of other methods reported in the literature. Overall, this new approach can directly and rapidly quantify amino sugars recovered in environmental hydrolysates.

Impurity analysis of 2-butynoic acid by ion chromatography-mass spectrometry

Small organic acids are widely used within the pharmaceutical industry but can be difficult to analyse. Ion chromatography is a suitable technique for the analysis of these acids but method development can be hindered as mass spectrometry is not often used as a detector; this means that peak tracking and peak purity cannot be performed. The authors report method development for the analysis of 2-butynoic acid, where by using electrospray ionisation mass spectrometry, peak purity was investigated and the presence of co-eluting impurities determined. Optimisation of the additives in the make-up flow to the mass spectrometer was shown to have an impact on the response observed. A standard series of organic acids were analysed spiked in to 2-butynoic acid at levels representative of impurities, the presence of the 2-butynoic acid did not impact the linearity or limit of detection observed for the acids; R² values greater than 0.98 were obtained for all acids with and without the presence of 2-butynoic acid with a limit of detection at 1 ppb for all but one of the acids.

A classification of liquid chromatography mass spectrometry techniques for evaluation of chemical composition and quality control of traditional medicines

Natural products (NPs) and traditional medicines (TMs) are used for treatment of various diseases and also to develop new drugs. However, identification of drug leads within the immense biodiversity of living organisms is a challenging task that requires considerable time, labor, and computational resources as well as the application of modern analytical instruments. LC-MS platforms are widely used for both drug discovery and quality control of TMs and food supplements. Moreover, a large dataset generated during LC-MS analysis contains valuable information that could be extracted and handled by means of various data mining and statistical tools. Novel sophisticated LC-MS based approaches are being introduced every year. Therefore, this review is prepared for the scientists specialized in pharmacognosy and analytical chemistry of NPs as well as working in related areas, in order to navigate them in the world of diverse LC-MS based techniques and strategies currently employed for NP discovery and dereplication, quality control, pattern recognition and sample comparison, and also in targeted and untargeted metabolomic studies. The suggested classification system includes the following LC-MS based procedures: elemental composition determination, isotopic fine structure analysis, mass defect filtering, de novo identification, clustering of the compounds in Molecular Networking (MN), diagnostic fragment ion (or neutral loss) filtering, manual dereplication using MS/MS data, database-assisted peak annotation, annotation of spectral trees, MS fingerprinting, feature extraction, bucketing of LC-MS data, peak profiling, predicted metabolite screening, targeted quantification of biomarkers, quantitative analysis of multi-component system, construction of chemical fingerprints, multi-targeted and untargeted metabolite profiling.

HPTLC-PCA Complementary to HRMS-PCA in the Case Study of Arbutus unedo Antioxidant Phenolic Profiling

A comparison between High-Performance Thin-Layer Chromatography (HPTLC) analysis and Liquid Chromatography High Resolution Mass Spectrometry (LC-HRMS), coupled with Principal Component Analysis (PCA) was carried out by performing a combined metabolomics study to discriminate Arbutus unedo (A. unedo) plants. For a rapid digital record of A. unedo extracts (leaves, yellow fruit, and red fruit collected in La Maddalena and Sassari, Sardinia), HPTLC was used. Data were then analysed by PCA with the results of the ability of this technique to discriminate samples. Similarly, extracts were acquired by non-targeted LC-HRMS followed by unsupervised PCA, and then by LC-HRMS (MS) to identify secondary metabolites involved in the differentiation of the samples. As a result, we demonstrated that HPTLC may be applied as a simple and reliable untargeted approach to rapidly discriminate extracts based on tissues and/or geographical origins, while LC-HRMS could be used to identify which metabolites are able to discriminate samples.

Analytical Platforms and Techniques to Study Stem Cell Metabolism

Over the past decade, advances in systems biology or 'omics techniques have enabled unprecedented insights into the biological processes that occur in cells, tissues, and on the organism level. One of these technologies is the metabolomics, which examines the whole content of the metabolites in a given sample. In a biological system, a stem cell for instance, there are thousands of single components, such as genes, RNA, proteins, and metabolites. These multiple molecular species interact with each other and these interactions may change over the life-time of a cell or in response to specific stimuli, adding to the complexity of the system. Using metabolomics, we can obtain an instantaneous snapshot of the biological status of a cell, tissue, or organism and gain insights on the pattern(s) of numerous analytes, both known and unknown, that result because of a given biological condition. Here, we outline the main methods to study the metabolism of stem cells, including a relatively recent technology of mass spectrometry imaging. Given the abundant and increasing interest in stem cell metabolism in both physiological and pathological conditions, we hope that this chapter will provide incentives for more research in these areas to ultimately reach wide network of applications in biomedical, pharmaceutical, and nutritional research and clinical medicine.

Metabolomics in Plant Stress Physiology

Metabolomics is an essential technology for functional genomics and systems biology. It plays a key role in functional annotation of genes and understanding towards cellular and molecular, biotic and abiotic stress responses. Different analytical techniques are used to extend the coverage of a full metabolome. The commonly used techniques are NMR, CE-MS, LC-MS, and GC-MS. The choice of a suitable technique depends on the speed, sensitivity, and accuracy. This chapter provides insight into plant metabolomic techniques, databases used in the analysis, data mining and processing, compound identification, and limitations in metabolomics. It also describes the workflow of measuring metabolites in plants. Metabolomic studies in plant responses to stress are a key research topic in many laboratories worldwide. We summarize different approaches and provide a generic overview of stress responsive metabolite markers and processes compiled from a broad range of different studies. Graphical Abstract.

Column Characterization and Selection Systems in Reversed-Phase High-Performance Liquid Chromatography

Reversed-phase high-performance liquid chromatography (RP-HPLC) is the most popular chromatographic mode, accounting for more than 90% of all separations. HPLC itself owes its immense popularity to it being relatively simple and inexpensive, with the equipment being reliable and easy to operate. Due to extensive automation, it can be run virtually unattended with multiple samples at various separation conditions, even by relatively low-skilled personnel. Currently, there are >600 RP-HPLC columns available to end users for purchase, some of which exhibit very large differences in selectivity and production quality. Often, two similar RP-HPLC columns are not equally suitable for the requisite separation, and to date, there is no universal RP-HPLC column covering a variety of analytes. This forces analytical laboratories to keep a multitude of diverse columns. Therefore, column selection is a crucial segment of RP-HPLC method development, especially since sample complexity is constantly increasing. Rationally choosing an appropriate column is complicated. In addition to the differences in the primary intermolecular interactions with analytes of the dispersive (London) type, individual columns can also exhibit a unique character owing to specific polar, hydrogen bond, and electron pair donor-acceptor interactions. They can also vary depending on the type of packing, amount and type of residual silanols, "end-capping", bonding density of ligands, and pore size, among others. Consequently, the chromatographic performance of RP-HPLC systems is often considerably altered depending on the selected column. Although a wide spectrum of knowledge is available on this important subject, there is still a lack of a comprehensive review for an objective comparison and/or selection of chromatographic columns. We aim for this review to be a comprehensive, authoritative, critical, and easily readable monograph of the most relevant publications regarding column selection and characterization in RP-HPLC covering the past four decades. Future perspectives, which involve the integration of state-of-the-art molecular simulations (molecular dynamics or Monte Carlo) with minimal experiments, aimed at nearly "experiment-free" column selection methodology, are proposed.

Improved LC/MS Methods for the Analysis of Metal-Sensitive Analytes Using Medronic Acid as a Mobile Phase Additive

Phosphorylated compounds and organic acids with multiple carboxylate groups are commonly observed to have poor peak shapes and signal in LC/MS experiments. The poor peak shape is caused by the presence of trace metals, particularly iron, contributed from a variety of sources within the chromatographic system. To ameliorate this problem, different solvent additives were investigated to reduce the amount of metal in the flow path to achieve better analytical performance for these metal-sensitive compounds. Here, we introduce the use of a solvent additive that can significantly improve the peak shapes and signal of metal-sensitive metabolites for LC/MS analysis. Moreover, the additive is shown to be amenable for other metal-sensitive applications, such as the analysis of phosphopeptides and polar phosphorylated pesticides, where the instruments could be used in either positive or negative analysis mode.

Quantification of nucleotides and their sugar conjugates in biological samples: Purposes, instruments and applications

Nucleotides and their sugar conjugates are fundamental molecules in life, participating in processes of DNA/RNA composition, cell wall build-up, glycosylation reactions, and signal conduction. Therefore, the quantification of these compounds in biological samples significantly benefits the understanding of their functions. However, nucleotides and nucleotide sugars are extremely hydrophilic, causing bad retention and peak symmetry on regular C18 chromatographic columns. To solve this problem, ion-pair (IP) chromatography, ion-exchange (IE) chromatography and hydrophilic interaction chromatography (HILIC) were applied, of which differentiated mechanisms were utilized to increase the retention of the analytes on the stationary phases. IP-HPLC and HILIC were convenient for coupling with many kinds of detectors (ultraviolet, UV or mass spectrometry, MS). Combining these two kinds of techniques, the advantages of better separation and retention were increased, while disadvantages like irreversible adsorption by stationary phases were greatly decreased. Due to the high concentrations of nonvolatile buffer salts used, IE-HPLC was not suitable for MS detectors. Protein precipitation and solid phase extraction were the common methods for sample treatment in the analysis of nucleotides and nucleotide sugars. By carefully optimizing the LCUV or LCMS conditions, high sensitivities could be achieved, and the methods could be applied to the analysis of many kinds of biological samples (cells, tissues, plants, bacteria, etc.). Developing new analyzing techniques may help the utilization of nucleotides and nucleotide sugars in the diagnosis and therapy of diseases.

Method selectivity evaluation using the co-feature ratio in LC/MS metabolomics: Comparison of HILIC stationary phase performance for the analysis of plasma, urine and cell extracts

Evaluation of the chromatographic separation in metabolomics studies has primarily been done using preselected sets of standards or by counting the number of detected features. An alternative approach is to calculate each feature's co-feature ratio, which is a combined selectivity measurement for the separation (i.e. extent of co-elution) and the MS-signal (i.e. adduct formation and in-source fragmentation). The aim of this study was to demonstrate how the selectivity of different HILIC stationary phases can be evaluated using the co-feature ratio approach. The study was based on three sample types; plasma, urine and cell extracts. Samples were analyzed on an UHPLC-ESI-Q-ToF system using an amide, a bare silica and a sulfobetaine stationary phase. For each feature, a co-feature ratio was calculated and used for multivariate analysis of the selectivity differences between the three stationary phases. Unsupervised PCA models indicated that the co-feature ratios were highly dependent on type of stationary phase. For several metabolites a 15-30 fold difference in the co-feature ratio were observed between the stationary phases. Observed selectivity differences related primarily to the retention patterns of unwanted matrix components such as inorganic salts (detected as salt clusters), glycerophospholipids, and polyethylene glycols. These matrix components affected the signal intensity of co-eluting metabolites by interfering with the ionization efficiency and/or their adduct formation. Furthermore, the retention pattern of these matrix components had huge influence on the number of detected features. The co-feature ratio approach has successfully been applied for evaluation of the selectivity performance of three HILIC stationary phases. The co-feature ratio could therefore be used in metabolomics for developing selective methods fit for their purpose, thereby avoiding generic analytical approaches, which are often biased, as type and amount of interfering matrix components are metabolome dependent.

Seasonal variations of iminosugars in mulberry leaves detected by hydrophilic interaction chromatography coupled with tandem mass spectrometry

A rapid, sensitive, and validated method was developed for the simultaneous determination of four iminosugars, 1-deoxynojirimycin (DNJ), d-fagomine (FAG), 2-O-a-d-galactopyranosyl-DNJ (Gal-DNJ), and 4-O-β-d-glucopyranosyl-fagomine (Glu-FAG), in mulberry leaves. The method used hydrophilic interaction chromatography coupled with tandem mass spectrometry. Based on this method, the seasonal variations of iminosugars in the leaves of different mulberry species (Morus alba, Morus multicaulis Perr, Morus atropurpurea Roxb. and Morus wittiorum Hand-Mazz.) collected in Guangzhou, China, during 10 consecutive months in 2013 were investigated. The results indicated that the maximum content of DNJ in the leaves of all four species occurred in summer (June or July). The highest FAG level was found in spring (April or May). The highest levels of Gal-DNJ and Glu-FAG were recorded in autumn (September or October).

Simultaneous untargeted and targeted metabolomics profiling of underivatized primary metabolites in sulfur-deficient barley by ultra-high performance liquid chromatography-quadrupole/time-of-flight mass spectrometry

BACKGROUND

Metabolomics based on mass spectrometry analysis are increasingly applied in diverse scientific domains, notably agronomy and plant biology, in order to understand plants' behaviors under different stress conditions. In fact, these stress conditions are able to disrupt many biosynthetic pathways that include mainly primary metabolites. Profiling and quantifying primary metabolites remain a challenging task because they are poorly retained in reverse phase columns, due to their high polarity and acid-base properties. The aim of this work is to develop a simultaneous untargeted/targeted profiling of amino acids, organic acids, sulfur metabolites, and other several metabolites. This method will be applied on sulfur depleted barley, in order to study this type of stress, which is difficult to detect at early stage. Also, this method aims to explore the impact of this stress on barley's metabolome.

RESULTS

Ultra-high performance liquid chromatography-high resolution mass spectrometry-based method was successfully applied to real samples allowing to discriminate, detect, and quantify primary metabolites in short-runs without any additional sampling step such as derivatization or ion pairing. The retention of polar metabolites was successfully achieved using modified C18 columns with high reproducibility (relative standard deviation below 10%). The quantification method showed a high sensitivity and robustness. Furthermore, high resolution mass spectrometry detection provided reliable quantification based on exact mass, eliminating potential interferences, and allowing the simultaneous untargeted metabolomics analysis. The untargeted data analysis was conducted using Progenesis QI software, performing alignment, peak picking, normalization and multivariate analysis. The simultaneous analysis provided cumulative information allowing to discriminate between two plant batches. Thus, discriminant biomarkers were identified and validated. Simultaneously, quantification confirmed coherently the relative abundance of these biomarkers.

CONCLUSIONS

A fast and innovated simultaneous untargeted/targeted method has successfully been developed and applied to sulfur deficiency on barley. This work opens interesting perspectives in both fundamental and applied research. Biomarker discovery give precious indication to understand plant behavior during a nutritional deficiency. Thus, direct or indirect measurement of these compounds allows a real time fertilization management and encounter the challenges of sustainable agriculture.

Shotgun Glycomics Identifies Tumor-Associated Glycan Ligands Bound by an Ovarian Carcinoma-Specific Monoclonal Antibody

Cancers display distinctive carbohydrate molecules (glycans) on their surface proteins and lipids. mAb A4, an in-house generated monoclonal IgM antibody, is capable of distinguishing malignant ovarian carcinoma cells from benign ovarian epithelia by binding specifically to cancer cell-associated glycans. However, the structural details of the glycan targets of mAb A4 have been elusive. Here we developed a novel approach of isolating and fractionating glycan molecules released from glycoproteins in cancer cell lysates using HILIC-UPLC, and used them as probes on a microarray for affinity-based identification of the binding targets, allowing full-size, difficult to synthesize, cancer-associated glycans to be directly studied. As a result of this "shotgun" glycomics approach, we corroborate the previously assigned specificity of mAb A4 by showing that mAb A4 binds primarily to large (>15 glucose units), sialylated N-glycans containing the H-type 1 antigen (Fuc-α1,2-Gal-β1,3-GlcNAc). Although mAb A4 was also capable of directly binding to type 1 N-acetyl-lactosamine, this epitope was mostly shielded by sialylation and thus relatively inaccessible to binding. Knowledge of the structure of mAb A4 antigen will facilitate its clinical development as well as its use as a diagnostic biomarker.

1-Deoxynojirimycin: Occurrence, Extraction, Chemistry, Oral Pharmacokinetics, Biological Activities and In Silico Target Fishing

1-Deoxynojirimycin (DNJ, C₆H13NO₄, 163.17 g/mol), an alkaloid azasugar or iminosugar, is a biologically active natural compound that exists in mulberry leaves and Commelina communis (dayflower) as well as from several bacterial strains such as Bacillus and Streptomyces species. Deoxynojirimycin possesses antihyperglycemic, anti-obesity, and antiviral features. Therefore, the aim of this detailed review article is to summarize the existing knowledge on occurrence, extraction, purification, determination, chemistry, and bioactivities of DNJ, so that researchers may use it to explore future perspectives of research on DNJ. Moreover, possible molecular targets of DNJ will also be investigated using suitable in silico approach.

Mass spectrometry as a quantitative tool in plant metabolomics

Metabolomics is a research field used to acquire comprehensive information on the composition of a metabolite pool to provide a functional screen of the cellular state. Studies of the plant metabolome include the analysis of a wide range of chemical species with very diverse physico-chemical properties, and therefore powerful analytical tools are required for the separation, characterization and quantification of this vast compound diversity present in plant matrices. In this review, challenges in the use of mass spectrometry (MS) as a quantitative tool in plant metabolomics experiments are discussed, and important criteria for the development and validation of MS-based analytical methods provided.This article is part of the themed issue 'Quantitative mass spectrometry'.

HILIC-MS for metabolomics: An attractive and complementary approach to RPLC-MS

Hydrophilic interaction chromatography (HILIC) is an emerging separation mode of liquid chromatography (LC). Using highly hydrophilic stationary phases capable of retaining polar/ionic metabolites, and accompany with high organic content mobile phase that offer readily compatibility with mass spectrometry (MS) has made HILIC an attractive complementary tool to the widely used reverse-phase (RP) chromatographic separations in metabolomic studies. The combination of HILIC and RPLC coupled with an MS detector expands the number of detected analytes and provides more comprehensive metabolite coverage than use of only RP chromatography. This review describes the recent applications of HILIC-MS/MS in metabolomic studies, ranging from amino acids, lipids, nucleotides, organic acids, pharmaceuticals, and metabolites of specific nature. The biological systems investigated include microbials, cultured cell line, plants, herbal medicine, urine, and serum as well as tissues from animals and humans. Owing to its unique capability to measure more-polar biomolecules, the HILIC separation technique would no doubt enhance the comprehensiveness of metabolite detection, and add significant value for metabolomic investigations. © 2014 Wiley Periodicals, Inc. Mass Spec Rev 35:574-600, 2016.

Mass spectrometry-based plant metabolomics: Metabolite responses to abiotic stress

Metabolomics is one omics approach that can be used to acquire comprehensive information on the composition of a metabolite pool to provide a functional screen of the cellular state. Studies of the plant metabolome include analysis of a wide range of chemical species with diverse physical properties, from ionic inorganic compounds to biochemically derived hydrophilic carbohydrates, organic and amino acids, and a range of hydrophobic lipid-related compounds. This complexitiy brings huge challenges to the analytical technologies employed in current plant metabolomics programs, and powerful analytical tools are required for the separation and characterization of this extremely high compound diversity present in biological sample matrices. The use of mass spectrometry (MS)-based analytical platforms to profile stress-responsive metabolites that allow some plants to adapt to adverse environmental conditions is fundamental in current plant biotechnology research programs for the understanding and development of stress-tolerant plants. In this review, we describe recent applications of metabolomics and emphasize its increasing application to study plant responses to environmental (stress-) factors, including drought, salt, low oxygen caused by waterlogging or flooding of the soil, temperature, light and oxidative stress (or a combination of them). Advances in understanding the global changes occurring in plant metabolism under specific abiotic stress conditions are fundamental to enhance plant fitness and increase stress tolerance. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 35:620-649, 2016.

Metabolic pathway reconstruction strategies for central metabolism and natural product biosynthesis

Metabolic pathway reconstruction presents a challenge for understanding metabolic pathways in organisms of interest. Different strategies, i.e., reference-based vs. de novo, must be used for pathway reconstruction depending on the availability of well-characterized enzymatic reactions. If at least one enzyme is already known to catalyze a reaction, its amino acid sequence can be used as a reference for identifying homologous enzymes in the genome of an organism of interest. Where there is no known enzyme able to catalyze a corresponding reaction, however, the reaction and the corresponding enzyme must be predicted de novo from chemical transformations of the putative substrate-product pair. This review summarizes studies involving reference-based and de novo metabolic pathway reconstruction and discusses the importance of the classification and structure-function relationships of enzymes.