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

Comparative metabolites profiling of different solvent extracts of Asparagus species cladodes using liquid chromatography-mass spectrometry-based metabolomics and molecular networking

INTRODUCTION

Asparagus species are naturally distributed worldwide and are known for their pharmacological properties that offer cures for various ailments. However, the metabolic choreography of these Asparagus species is not well characterized, and the compounds contributing to their bioactivities remain unknown.

OBJECTIVE

This study aimed to profile and compare the metabolomes of three Asparagus species cladodes using different solvent extractions.

METHODS

An ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry-based metabolomics and molecular networking approach was used to study the effects of different solvents (ethyl acetate, methanol, and chloroform) with varying polarity on metabolites extraction and identification of bioactive compounds from three Asparagus species cladodes (Asparagus falcatus, Asparagus plumosus, and Asparagus densiflorus 'Meyersii').

RESULTS

Multivariate statistical analyses (mainly principal component analysis) revealed a significant separation between the three solvents and the three species, indicating notable metabolic differences. A total of 118 metabolites were identified in the three species extracted with the different solvents, with methanolic and chloroform extracts containing more metabolites compared with ethyl acetate extracts. These metabolites were identified as belonging to the flavonoids, cinnamic acids, organooxygen compounds, steroids, fatty acids, benzenes, and glycerophospholipids compound classes. Furthermore, these compounds classes were differentially distributed among the three species, indicating chemical/chemotaxis differences between the compared species. Chloroform and methanol are recommended as the optimal solvents to obtain a high content of phytochemical compounds from Asparagus species cladodes.

Reducing Mass Confusion over the Microbiome

As genetic tools continue to emerge and mature, more information is revealed about the identity and diversity of microbial community members. Genetic tools can also be used to make predictions about the chemistry that bacteria and fungi produce to function and communicate with one another and the host. Ongoing efforts to identify these products and link genetic information to microbiome chemistry rely on analytical tools. This tutorial highlights recent advancements in microbiome studies driven by techniques in mass spectrometry.

Liquid Chromatography-Mass Spectrometry for Clinical Metabolomics: An Overview

Metabolomics is a discipline that offers a comprehensive analysis of metabolites in biological samples. In the last decades, the notable evolution in liquid chromatography and mass spectrometry technologies has driven an exponential progress in LC-MS-based metabolomics. Targeted and untargeted metabolomics strategies are important tools in health and medical science, especially in the study of disease-related biomarkers, drug discovery and development, toxicology, diet, physical exercise, and precision medicine. Clinical and biological problems can now be understood in terms of metabolic phenotyping. This overview highlights the current approaches to LC-MS-based metabolomics analysis and its applications in the clinical research.

Expanding Urinary Metabolite Annotation through Integrated Mass Spectral Similarity Networking

The urine metabolome constitutes a rich source of functional information reflecting physiological states that are influenced by distinct conditions and biological stresses, such as responses to drug treatments or disease manifestations. Although global liquid chromatography-mass spectrometry (MS) profiling provides the most comprehensive measurement of metabolites in complex biological samples, annotation remains a challenge, and computational approaches are necessary to translate the molecular composition into biological knowledge. Here, we investigated the use of tandem MS-based enhanced molecular networks (MolNetEnhancer) to improve the metabolite annotation of urine extracts. The samples (n = 10) were analyzed by hydrophilic interaction chromatography-quadrupole time-of-flight mass spectrometry in both electrospray ionization (ESI) modes. Consistent with other common data preprocessing software, the use of Progenesis QI led to the annotation of up to 20 metabolites based on MS2 library searches, showing a high fragmentation score (cosine similarity ≥ 0.7), that is, ∼2% of mass features containing MS2 spectra. Molecular networking based on library matching resulted in the annotation of up to 62 urinary compounds. Using a combination of unsupervised substructure discovery (MS2LDA), the in silico tool network annotation propagation (NAP), and ClassyFire chemical ontology, embedded in a multilayered molecular network by MolNetEnhancer, we were able to expand the chemical characterization to ∼50% of the data set. The integrative approach led to the annotation of 275 compounds at the metabolomics standards initiative (MSI) confidence level 2, as well as 459 and 578 urinary metabolites (MSI level 3) in both negative and positive ESI modes, respectively. The exhaustive MS2-based annotation outperformed similar studies applied to larger cohorts while offering the discovery of metabolites not identified by the MS2 library search. This is the first work that effectively integrates orthogonal annotation methods and MS2-based fragmentation studies to improve metabolite annotation in urine samples.

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.

Globoidnan A, rabdosiin and globoidnan B as new phenolic markers in European-sourced comfrey (Symphytum officinale L.) root samples

INTRODUCTION

Symphytum officinale L. (comfrey, Boraginaceae) is a cultivated or spontaneously growing medicinal plant that is traditionally used for the treatment of bone fractures, hematomas, muscle pains and joint pains. A wide range of topical preparations and dried roots for ex tempore applications are marketed in European drug stores or pharmacies.

OBJECTIVE

The aim of this study was to perform the qualitative and quantitative analysis of pyrrolizidine alkaloids (PAs) and phenolic compounds in the hydroethanolic extracts of 16 commercial comfrey root batches purchased from 12 different European countries.

METHODS

Liquid chromatography hyphenated with high-resolution tandem mass spectrometry (LC-HRMS/MS) was used for the profiling of PAs and phenolic compounds, whereas LC-MS/MS and liquid chromatography with diode array detection (LC-DAD) were used for their quantification.

RESULTS

20 PAs (i.e. intermedine, lycopsamine, acetylintermedine, acetyllycopsamine, symphytine, symphytine-N-oxide), 17 phenolic compounds (i.e. caffeic and rosmarinic acids, rabdosiin, globoidnan A, globoidnan B) and 9 nonphenolic compounds (sugars, organic and fatty acids) were fully or partly annotated in the analysed samples. In addition, the quantitative analyses revealed that globoidnan B, rabdosiin and globoidnan A are new phenolic markers that can be used together with rosmarinic acid and PAs for the quality control of commercial comfrey root batches.

CONCLUSIONS

This study brings new insights into the phytochemical complexity of S. officinale, revealing not only numerous toxic PAs, but also a significant number of valuable phenolic compounds that could contribute to the bioactivities of comfrey-based preparations.

Untargeted metabolite profiling and phytochemical analysis based on RP-HPLC-DAD-QTOF-MS and MS/MS for discovering new bioactive compounds in Rumex algeriensis flowers and stems

INTRODUCTION

Natural products with distinctive pharmaceutical activities are considered as the main source of new herbal drugs, functional foods and cosmetic additives. Rumex algeriensis is an endemic medicinal plant with no accessible information about its chemical profile and biological activities.

OBJECTIVE

In the quest for new sources of biologically-active compounds, we intended in the present work to undertake a comprehensive characterisation of phytochemical compounds from Rumex algeriensis flowers and stems hydro-methanolic extract.

METHODOLOGY

Chemical profiles were evaluated by a rapid analytical method reversed-phase high-performance liquid chromatography (RP-HPLC) coupled to electrospray ionisation-quadrupole-time-of-flight mass spectrometry (ESI-QTOF-MS) and tandem mass spectrometry (MS/MS) using negative and positive ions modes.

RESULTS

In this work, 44 bioactive compounds were tentatively identified using high mass accuracy data and confirmed by MS/MS experiments, among which five compounds were reported for the first time in the Polygonaceae family. These compounds were classified as sugars, hydroxybenzoic acids, hydroxycinnamic acids, flavonols, flavanones, flavone, flavanols, condensed tannins, hydrolysable tannins and their conjugated derivatives.

CONCLUSIONS

The obtained results highlighted that Rumex algeriensis, even though undervalued and unexplored, contributes to the preclinical knowledge that could be considered as a renewable source of nutraceutical compounds that may be exploited in novel herbal medicinal products, in the food, pharmaceutical and nutraceutical industries.

Adaptive defence-related changes in the metabolome of Sorghum bicolor cells in response to lipopolysaccharides of the pathogen Burkholderia andropogonis

Plant cell suspension culture systems are valuable for the study of complex biological systems such as inducible defence responses and aspects of plant innate immunity. Perturbations to the cellular metabolome can be investigated using metabolomic approaches in order to reveal the underlying metabolic mechanism of cellular responses. Lipopolysaccharides from the sorghum pathogen, Burkholderia andropogonis (LPSB.a.), were purified, chemically characterised and structurally elucidated. The lipid A moiety consists of tetra- and penta-acylated 1,4'-bis-phosphorylated disaccharide backbone decorated by aminoarabinose residues, while the O-polysaccharide chain consists of linear trisaccharide repeating units of [→2)-α-Rha3CMe-(1 → 3)-α-Rha-(1 → 3)-α-Rha-(1 → ]. The effect of LPSB.a. in triggering metabolic reprogramming in Sorghum bicolor cells were investigated using untargeted metabolomics with liquid chromatography coupled to mass spectrometry detection. Cells were treated with LPSB.a. and the metabolic changes monitored over a 30 h time period. Alterations in the levels of phytohormones (jasmonates, zeatins, traumatic-, azelaic- and abscisic acid), which marked the onset of defence responses and accumulation of defence-related metabolites, were observed. Phenylpropanoids and indole alkaloids as well as oxylipins that included di- and trihydroxyoctadecedienoic acids were identified as signatory biomarkers, with marked secretion into the extracellular milieu. The study demonstrated that sorghum cells recognise LPSB.a. as a 'microbe-associated molecular pattern', perturbing normal cellular homeostasis. The molecular features of the altered metabolome were associated with phytohormone-responsive metabolomic reconfiguration of primary and secondary metabolites originating from various metabolic pathways, in support of defence and immunity.

Impact of matrix effects and ionization efficiency in non-quantitative untargeted metabolomics

INTRODUCTION

LC-MS-based untargeted metabolomics has become increasingly popular due to the vast amount of information gained in a single analysis. Many studies utilize metabolomics to profile metabolic changes in various representative biofluids, tissues, or other sample types. Most analyses are performed measuring changes in the metabolic pool of a single biological matrix due to an altered phenotype, such as disease versus normal. Measurements in such experiments are typically highly reproducible with little variation due to analytical and technological advancements in mass spectrometry. With the expanded application of metabolomics into various non-analytical scientific disciplines, the emergence of studies comparing the signal intensities of specific analytes across different biological matrices (e.g. plasma vs. urine) is becoming more common, but the matrix effect between sample types is often neglected. Additionally, the practice of comparing the signal intensities of different analytes and correlating to relative abundance is also increasingly prevalent, but the response ratio between analytes due to differences in ionization efficiency is not always accounted for in data analysis. This report serves to communicate and raise awareness of these two well-recognized issues to prevent improper data interpretation in the field of metabolomics.

OBJECTIVES

We demonstrate the impact of matrix effects and ionization efficiency with labeled analytical standards in human plasma, serum, and urine and describe how the direct comparison of non-quantitative signal intensities between biofluids, as well as between different analytes in the same biofluid, in untargeted metabolomics is inaccurate without proper response corrections.

METHODS

Human plasma, serum, and urine (n = 4 technical replicates per biofluid) were spiked with a panel of labeled internal standards all at identical concentrations, simultaneously extracted, and analyzed by UHPLC-HRMS. Signal intensities were compared for demonstration of the impact of matrix effects in untargeted metabolomics. A neat mixture of two co-eluting, structurally-similar labeled standards at the same concentration was also analyzed to demonstrate the effect of ionization efficiency on signal intensity.

RESULTS

Despite being spiked at identical concentrations, labeled standards we examined in this study showed significant differences in their signal intensities between biofluids, as well as from each other in the same biofluid, due to matrix effects. Co-eluting standards were also found to yield significantly different signal intensities at identical concentrations due to differences in ionization efficiency.

CONCLUSIONS

Due to the presence of matrix effects in untargeted, non-quantitative metabolomics, the signal intensity of any single analyte cannot be directly compared to the signal intensity of that same analyte (or any other analyte) between any two different matrices. Due to differences in ionization efficiency, the signal intensity of any single analyte cannot be directly compared to the signal intensity of any other analyte, even in the same matrix.

A split-range acquisition method for the non-targeted metabolomic profiling of human plasma with hydrophilic interaction chromatography - high-resolution mass spectrometry

Untargeted metabolomics of human plasma with mass spectrometry is of particular interest in medical research to explore pathophysiology, find disease biomarkers or for the understanding of the response to pharmacotherapy. Since analytical performances may be impacted by the laboratory environment and the acquisition method settings, the objectives of this study were to assess the role of interfering compounds and to propose an acquisition method to maximize the metabolome coverage for human plasma metabolomic analysis. Human plasma samples were processed with liquid/liquid extraction then analysed with HILIC-high resolution mass spectrometry. A method with a single m/z range was compared to four methods with different split acquisition ranges and four sets of ionization source parameters were compared. The data were analysed with the R software and on the Worklow4Metabolomics online platform. The major interfering compounds were identified in blank samples where they accounted for up to 86% of the signal intensity. Splitting the acquisition range into 3 m/z ranges improved the number of detected features, the number of features with proposed annotation in the Human Metabolome Database, as well as signal intensity throughout the whole m/z range. The method performing best was the one using three m/z ranges of approximatively the same extent. Ionization source parameters also strongly affected the number of detected features. Splitting the acquisition range into 3 m/z ranges with optimized ionization source parameters allows a comprehensive analysis of the human plasma metabolome with perspectives for applications to pathophysiological studies.

Role of Endogenous Metabolite Alterations in Neuropsychiatric Disease

The potential role in neuropsychiatric disease risk arising from alterations and derangements of endogenous small-molecule metabolites remains understudied. Alterations of endogenous metabolite concentrations can arise in multiple ways. Marked derangements of single endogenous small-molecule metabolites are found in a large group of rare genetic human diseases termed "inborn errors of metabolism", many of which are associated with prominent neuropsychiatric symptomology. Whether such metabolites act neuroactively to directly lead to distinct neural dysfunction has been frequently hypothesized but rarely demonstrated unequivocally. Here we discuss this disease concept in the context of our recent findings demonstrating that neural dysfunction arising from accumulation of the schizophrenia-associated metabolite l-proline is due to its structural mimicry of the neurotransmitter GABA that leads to alterations in GABA-ergic short-term synaptic plasticity. For cases in which a similar direct action upon neurotransmitter binding sites is suspected, we lay out a systematic approach that can be extended to assessing the potential disruptive action of such candidate disease metabolites. To address the potentially important and broader role in neuropsychiatric disease, we also consider whether the more subtle yet more ubiquitous variations in endogenous metabolites arising from natural allelic variation may likewise contribute to disease risk but in a more complex and nuanced manner.

Improving metabolome coverage and data quality: advancing metabolomics and lipidomics for biomarker discovery

This Feature Article highlights some of the key challenges within the field of metabolomics and examines what role separation and analytical sciences can play to improve the use of metabolomics in biomarker discovery and personalized medicine. Recent progress in four key areas is highlighted: (i) improving metabolite coverage, (ii) developing accurate methods for unstable metabolites including in vivo global metabolomics methods, (iii) advancing inter-laboratory studies and reference materials and (iv) improving data quality, standardization and quality control of metabolomics studies.

Metabolomics in Plant Priming Research: The Way Forward?

A new era of plant biochemistry at the systems level is emerging, providing detailed descriptions of biochemical phenomena at the cellular and organismal level. This new era is marked by the advent of metabolomics—the qualitative and quantitative investigation of the entire metabolome (in a dynamic equilibrium) of a biological system. This field has developed as an indispensable methodological approach to study cellular biochemistry at a global level. For protection and survival in a constantly-changing environment, plants rely on a complex and multi-layered innate immune system. This involves surveillance of ‘self’ and ‘non-self,’ molecule-based systemic signalling and metabolic adaptations involving primary and secondary metabolites as well as epigenetic modulation mechanisms. Establishment of a pre-conditioned or primed state can sensitise or enhance aspects of innate immunity for faster and stronger responses. Comprehensive elucidation of the molecular and biochemical processes associated with the phenotypic defence state is vital for a better understanding of the molecular mechanisms that define the metabolism of plant–pathogen interactions. Such insights are essential for translational research and applications. Thus, this review highlights the prospects of metabolomics and addresses current challenges that hinder the realisation of the full potential of the field. Such limitations include partial coverage of the metabolome and maximising the value of metabolomics data (extraction of information and interpretation). Furthermore, the review points out key features that characterise both the plant innate immune system and enhancement of the latter, thus underlining insights from metabolomic studies in plant priming. Future perspectives in this inspiring area are included, with the aim of stimulating further studies leading to a better understanding of plant immunity at the metabolome level.

Panomics for Precision Medicine

Medicine is poised to undergo a digital transformation. High-throughput platforms are creating terabytes of genomic, transcriptomic, proteomic, and metabolomic data. The challenge is to interpret these data in a meaningful manner - to uncover relationships that are not readily apparent between molecular profiles and states of health or disease. This will require the development of novel data pipelines and computational tools. The combined analysis of multi-dimensional data is referred to as 'panomics'. The ultimate hope of integrative panomics is that it will lead to the discovery and application of novel markers and targeted therapeutics that drive forward a new era of 'precision medicine' where inter-individual variation is accounted for in the treatment of patients.