Untargeted Studies Research Articles

An untargeted lipidomic strategy combining comprehensive two-dimensional liquid chromatography and chemometric analysis

Untargeted lipidomic samples are extremely complex and often exceed the limits of peak capacity achievable by one-dimensional liquid chromatography (LC). Comprehensive two-dimensional liquid chromatography (LC × LC) appears as a promising alternative to overcome this drawback. Unfortunately, this approach generates highly complex datasets which untargeted analysis is challenging. In this work, a global methodological strategy combining LC × LC–MS/MS with chemometric data analysis is proposed for untargeted lipidomic studies. The feasibility of the proposed methodology is demonstrated by its application to assess the effects of arsenic exposure on the lipidome of growing rice samples. A two-dimensional chromatographic setup coupling reversed phase (RP) and hydrophilic interaction liquid chromatography (HILIC) modes together with a triple quadrupole mass detector (TQD) is proposed to analyze lipid extracts from rice samples at different experimental conditions. Chemometric tools were used for data compression, spectral and elution profiles resolution, feature detection and statistical analysis of the multidimensional LC × LC–MS/MS data. The obtained results revealed that the proposed methodology was useful to gather relevant information from untargeted lipidomic studies and detect potential biomarkers.

Recent Advances in Targeted and Untargeted Metabolomics by NMR and MS/NMR Methods.

Metabolomics has made significant progress in multiple fronts in the last 18 months. This minireview aimed to give an overview of these advancements in the light of their contribution to targeted and untargeted metabolomics. New computational approaches have emerged to overcome the manual absolute quantitation step of metabolites in one-dimensional (1D) 1H nuclear magnetic resonance (NMR) spectra. This provides more consistency between inter-laboratory comparisons. Integration of two-dimensional (2D) NMR metabolomics databases under a unified web server allowed for very accurate identification of the metabolites that have been catalogued in these databases. For the remaining uncatalogued and unknown metabolites, new cheminformatics approaches have been developed by combining NMR and mass spectrometry (MS). These hybrid MS/NMR approaches accelerated the identification of unknowns in untargeted studies, and now they are allowing for profiling ever larger number of metabolites in application studies.

Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction.

We introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS2), and NMR into a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter out the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture, and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana. The NMR/MS2 approach is well suited to the discovery of new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases.

Characterization of the Spectral Accuracy of an Orbitrap Mass Analyzer Using Isotope Ratio Mass Spectrometry.

Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) source coupled to the Q Exactive Plus has been extensively used in untargeted mass spectrometry imaging (MSI) analyses of biological tissue sections. Although the Orbitrap is a high-resolution and accurate-mass (HRAM) mass analyzer, these attributes alone cannot be used for the reliable identification of unknown analytes observed in complex biological matrices. Spectral accuracy (SA) is the ability of the mass spectrometer to accurately measure the isotopic distributions which, when used with high mass measurement accuracy (MMA), can facilitate the elucidation of a single elemental composition. To investigate the effects of different ion populations on an Orbitrap's SA and MMA, a solution of caffeine, the tetrapeptide MRFA, and ultramark was analyzed using a Q Exactive Plus across eight distinct automatic gain control (AGC) targets. The same compounds from the same lot numbers were also individually analyzed using isotope ratio mass spectrometry (IRMS) to accurately determine the isotopic abundance of 13C, 15N, and 34S. We demonstrated that at optimum absolute ion abundances the Orbitrap can be used to accurately count carbons, nitrogens, and sulfurs in samples with varying masses. Additionally, absolute monoisotopic ion abundances required for high SA were empirically determined by using the expected (IRMS) and experimental (Orbitrap) isotopic distributions to calculate the Pearson chi-square test. These thresholds for absolute ion abundances can be used in untargeted MSI studies to shorten an identification list by rapidly screening for isotopic distributions whose absolute ion abundances are high enough to accurately estimate the number of atoms.

Untargeted GC-MS Metabolomics.

Untargeted metabolomics refers to the high-throughput analysis of the metabolic state of a biological system (e.g., tissue, biological fluid, cell culture) based on the concentration profile of all measurable free low molecular weight metabolites. Gas chromatography-mass spectrometry (GC-MS), being a highly sensitive and high-throughput analytical platform, has been proven a useful tool for untargeted studies of primary metabolism in a variety of applications. As an omic analysis, GC-MS metabolomics is a multistep procedure; thus, standardization of an untargeted GC-MS metabolomics protocol requires the integrated optimization of pre-analytical, analytical, and computational steps. The main difference of GC-MS metabolomics compared to other metabolomics analytical platforms, including liquid chromatography-MS, is the need for the derivatization of the metabolite extracts into volatile and thermally stable derivatives, the latter being quantified in the metabolic profiles. This analytical step requires special care in the optimization of the untargeted GC-MS metabolomics experimental protocol. Moreover, both the derivatization of the original sample and the compound fragmentation that takes place in GC-MS impose specialized GC-MS metabolomic data identification, quantification, normalization and filtering methods. In this chapter, we describe the integrated protocol of untargeted GC-MS metabolomics with both the analytical and computational steps, focusing on the GC-MS specific parts, and provide details on any sample depending differences.

A review of blood sample handling and pre-processing for metabolomics studies.

Metabolomics has been found to be applicable to a wide range of clinical studies, bringing a new era for improving clinical diagnostics, early disease detection, therapy prediction and treatment efficiency monitoring. A major challenge in metabolomics, particularly untargeted studies, is the extremely diverse and complex nature of biological specimens. Despite great advances in the field there still exist fundamental needs for considering pre-analytical variability that can introduce bias to the subsequent analytical process and decrease the reliability of the results and moreover confound final research outcomes. Many researchers are mainly focused on the instrumental aspects of the biomarker discovery process, and sample related variables sometimes seem to be overlooked. To bridge the gap, critical information and standardized protocols regarding experimental design and sample handling and pre-processing are highly desired. Characterization of a range variation among sample collection methods is necessary to prevent results misinterpretation and to ensure that observed differences are not due to an experimental bias caused by inconsistencies in sample processing. Herein, a systematic discussion of pre-analytical variables affecting metabolomics studies based on blood derived samples is performed. Furthermore, we provide a set of recommendations concerning experimental design, collection, pre-processing procedures and storage conditions as a practical review that can guide and serve for the standardization of protocols and reduction of undesirable variation.

Experimental and Study Design Considerations for Uncovering Oncometabolites.

Metabolomics as a field has gained attention due to its potential for biomarker discovery, namely because it directly reflects disease phenotype and is the downstream effect of posttranslational modifications. The field provides a "top-down," integrated view of biochemistry in complex organisms, as opposed to the traditional "bottom-up" approach that aims to analyze networks of interactions between genes, proteins and metabolites. It also allows for the detection of thousands of endogenous metabolites in various clinical biospecimens in a high-throughput manner, including tissue and biofluids such as blood and urine. Of note, because biological fluid samples can be collected relatively easily, the time-dependent fluctuations of metabolites can be readily studied in detail.In this chapter, we aim to provide an overview of (1) analytical methods that are currently employed in the field, and (2) study design concepts that should be considered prior to conducting high-throughput metabolomics studies. While widely applicable, the concepts presented here are namely applicable to high-throughput untargeted studies that aim to search for metabolite biomarkers that are associated with a particular human disease.

Improving global feature detectabilities through scan range splitting for untargeted metabolomics by high-performance liquid chromatography-Orbitrap mass spectrometry

Untargeted metabolomics aims at obtaining quantitative information on the highest possible number of low-molecular biomolecules present in a biological sample. Rather small changes in mass spectrometric spectrum acquisition parameters may have a significant influence on the detectabilities of metabolites in untargeted global-scale studies by means of high-performance liquid chromatography-mass spectrometry (HPLC-MS). Employing whole cell lysates of human renal proximal tubule cells, we present a systematic global-scale study of the influence of mass spectrometric scan parameters and post-acquisition data treatment on the number and intensity of metabolites detectable in whole cell lysates.Ion transmission and ion collection efficiencies in an Orbitrap-based mass spectrometer basically depend on the m/z range scanned, which, ideally, requires different instrument settings for the respective mass ranges investigated. Therefore, we split a full scan range of m/z 50–1000 relevant for metabolites into two separate segments (m/z 50–200 and m/z 200–1,000), allowing an independent tuning of the ion transmission parameters for both mass ranges. Three different implementations, involving either scanning from m/z 50–1000 in a single scan, or scanning from m/z 50–200 and from m/z 200–1000 in two alternating scans, or performing two separate HPLC-MS runs with m/z 50–200 and m/z 200–1000 scan ranges were critically assessed. The detected features were subjected to rigorous background filtering and quality control in order to obtain reliable metabolite features for subsequent differential quantification.The most efficient approach in terms of feature number, which forms the basis for statistical analysis, identification, and for generating biological hypotheses, was the separate analysis of two different mass ranges. This lead to an increase in the number of detectable metabolite features, especially in the higher mass range (m/z greater than 400), by 2.5 (negative mode) to 6-fold (positive mode) as compared to analysis involving a single scan range. The total number of features confidently detectable was 560 in positive ion mode, and 436 in negative ion mode.

Profiling cancer metabolism at the 'omic' level: a last resort or the next frontier?

When profiling metabolites, there are two general experimental paradigms: untargeted studies at the omic scale and targeted studies which usually focus on a much smaller subset of compounds. Although untargeted studies have become increasingly fashionable and are often perceived to be the cutting edge, it is important to recognize that they have limitations and are not always the better experiment. In theory, an untargeted analysis profiles all of the same metabolites as a targeted analysis, plus more. So why is more not always better? We will frame our consideration in the words of pioneering computer scientist Alan Perlis: “Fools ignore complexity. Pragmatists suffer it. Some can avoid it. Geniuses remove it.” [1].

When cells divide: Label-free multimodal spectral imaging for exploratory molecular investigation of living cells during cytokinesis.

In vivo, molecular-level investigation of cytokinesis, the climax of the cell cycle, not only deepens our understanding of how life continues, but it will also open up new possibilities of diagnosis/prognosis of cancer cells. Although fluorescence-based methods have been widely employed to address this challenge, they require a fluorophore to be designed for a specific known biomolecule and introduced into the cell. Here, we present a label-free spectral imaging approach based on multivariate curve resolution analysis of Raman hyperspectral data that enables exploratory untargeted studies of mammalian cell cytokinesis. We derived intrinsic vibrational spectra and intracellular distributions of major biomolecular components (lipids and proteins) in dividing and nondividing human colon cancer cells. In addition, we discovered an unusual autofluorescent lipid component that appears predominantly in the vicinity of the cleavage furrow during cytokinesis. This autofluorescence signal could be utilized as an endogenous probe for monitoring and visualizing cytokinesis in vivo.

MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics

BackgroundIn spite of its great promise, metabolomics has proven difficult to execute in an untargeted and generalizable manner. Liquid chromatography–mass spectrometry (LC–MS) has made it possible to gather data on thousands of cellular metabolites. However, matching metabolites to their spectral features continues to be a bottleneck, meaning that much of the collected information remains uninterpreted and that new metabolites are seldom discovered in untargeted studies. These challenges require new approaches that consider compounds beyond those available in curated biochemistry databases.DescriptionHere we present Metabolic In silico Network Expansions (MINEs), an extension of known metabolite databases to include molecules that have not been observed, but are likely to occur based on known metabolites and common biochemical reactions. We utilize an algorithm called the Biochemical Network Integrated Computational Explorer (BNICE) and expert-curated reaction rules based on the Enzyme Commission classification system to propose the novel chemical structures and reactions that comprise MINE databases. Starting from the Kyoto Encyclopedia of Genes and Genomes (KEGG) COMPOUND database, the MINE contains over 571,000 compounds, of which 93% are not present in the PubChem database. However, these MINE compounds have on average higher structural similarity to natural products than compounds from KEGG or PubChem. MINE databases were able to propose annotations for 98.6% of a set of 667 MassBank spectra, 14% more than KEGG alone and equivalent to PubChem while returning far fewer candidates per spectra than PubChem (46 vs. 1715 median candidates). Application of MINEs to LC–MS accurate mass data enabled the identity of an unknown peak to be confidently predicted.ConclusionsMINE databases are freely accessible for non-commercial use via user-friendly web-tools at http://minedatabase.mcs.anl.gov and developer-friendly APIs. MINEs improve metabolomics peak identification as compared to general chemical databases whose results include irrelevant synthetic compounds. Furthermore, MINEs complement and expand on previous in silico generated compound databases that focus on human metabolism. We are actively developing the database; future versions of this resource will incorporate transformation rules for spontaneous chemical reactions and more advanced filtering and prioritization of candidate structures.Graphical abstractMINE database construction and access methods. The process of constructing a MINE database from the curated source databases is depicted on the left. The methods for accessing the database are shown on the right.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-015-0087-1) contains supplementary material, which is available to authorized users.

A New Paradigm for Known Metabolite Identification in Metabonomics/Metabolomics: Metabolite Identification Efficiency

A new paradigm is proposed for assessing confidence in the identification of known metabolites in metabonomics studies using NMR spectroscopy approaches. This new paradigm is based upon the analysis of the amount of metabolite identification information retrieved from NMR spectra relative to the molecular size of the metabolite. Several new indices are proposed including: metabolite identification efficiency (MIE) and metabolite identification carbon efficiency (MICE), both of which can be easily calculated. These indices, together with some guidelines, can be used to provide a better indication of known metabolite identification confidence in metabonomics studies than existing methods. Since known metabolite identification in untargeted metabonomics studies is one of the key bottlenecks facing the science currently, it is hoped that these concepts based on molecular spectroscopic informatics, will find utility in the field.

Chapter Five - Metabolism in Chronic Fatigue Syndrome

Chronic fatigue syndrome (CFS) is a poorly understood condition that presents as long-term physical and mental fatigue with associated symptoms of pain and sensitivity across a broad range of systems in the body. The poor understanding of the disorder comes from the varying clinical diagnostic definitions as well as the broad array of body systems from which its symptoms present. Studies on metabolism and CFS suggest irregularities in energy metabolism, amino acid metabolism, nucleotide metabolism, nitrogen metabolism, hormone metabolism, and oxidative stress metabolism. The overwhelming body of evidence suggests an oxidative environment with the minimal utilization of mitochondria for efficient energy production. This is coupled with a reduced excretion of amino acids and nitrogen in general. Metabolomics is a developing field that studies metabolism within a living system under varying conditions of stimuli. Through its development, there has been the optimisation of techniques to do large-scale hypothesis-generating untargeted studies as well as hypothesis-testing targeted studies. These techniques are introduced and show an important future direction for research into complex illnesses such as CFS.

Sample preparation methods for LC-MS-based global aqueous metabolite profiling.

Metabolite extraction is a key step in metabolomic analyses, particularly for untargeted studies. The extraction determines the types of metabolites that will be detected and the analytical platform to be used. In this chapter we describe two protocols aimed at detecting polar metabolites from biological samples; the first is aimed at detecting reduced species by LC/MS, and the second satisfies the requirements for both NMR and LC/MS analysis simultaneously.

Mass spectrometry and metabolomics: past, present and future

From humble beginnings, the scientific discipline of metabolomics has developed with a multitude of small but significant steps in the last 15 years. Today, we are interrogating the metabolite complement of biological systems in significantly greater depth and with higher accuracy and quality than we could ever predict 15 years ago. Many developments that have emerged during this journey have been related to new tools, from analytical instruments to computational software and including novel methodologies associated with instruments and software. With these innovative tools we are deriving new knowledge related to metabolism, biochemical synthesis and the regulation of complex biological networks in diverse organisms from microbes and alga, through plants and model organisms including Arabidopsis, Daphnia and Drosophila to mammals. The application of analytical platforms is one hugely important area where development in the past has, and in the future will shape our capabilities. The technological advances which allowed us to detect hundreds or thousands of metabolites in an untargeted approach, applying mass spectrometry (MS) and NMR spectroscopy, was one of the significant driving forces in the expansion of metabolomics at the end of the 20th century and in the early years of the 21st century. The two techniques of MS and NMR are highly complementary and when applied synergistically in studies they can increase our capabilities to answer biological questions. Today, MS and NMR are applied in greater than 80 % of all published metabolomics studies. Mass spectrometry, including its integration with separation techniques including liquid chromatography and associated variants, gas chromatography and capillary electrophoresis, is a powerful bio-analytical tool to apply in metabolomics. In studies where the metabolites of interest are known and chemical standards are available absolute quantification can be applied with high precision, accuracy and sensitivity; although the metabolites still have to be extracted from the samples in a robust and quantitative (or at least known) extent, which is an important aspect in these studies. These techniques have been applied in many areas including pharmaceutical drug development, environmental testing laboratories and anti-doping laboratories such as were applied in the 2012 Olympic Games. Mass spectrometry can also be applied to study thousands of metabolites in a single sample in untargeted studies where the metabolites of biological importance are not known a priori; a true discovery platform providing an unparalleled phenotypic readout comprising thousands of metabolites. It is with great pleasure that as Guest Editors we present a special issue of the journal Metabolomics focused on the development and application of mass spectrometry. Here we will focus on past achievements, current tools, developments for the future and novel applications. We have planned this special issue to provide an overview of the current role and potential of MS for metabolomics which have not been previously observed. We wish to thank our contributing authors and provide special thanks to our sponsors for this special issue; Agilent Technologies, LECO and Thermo Fisher Scientific. W. B. Dunn (&) School of Biosciences, The University of Birmingham, Edgbaston, Birmingham B15 2TT, UK e-mail: w.dunn@bham.ac.uk

The Importance of Experimental Design And Qc Samples in Large-Scale And Ms-Driven Untargeted Metabolomic Studies of Humans

The metabolic investigation of the human population is becoming increasingly important in the study of health and disease. The phenotypic variation can be investigated through the application of metabolomics; to provide a statistically robust investigation, the study of hundreds to thousands of individuals is required. In untargeted and MS-focused metabolomic studies this once provided significant hurdles. However, recent innovations have enabled the application of MS platforms in large-scale, untargeted studies of humans. Herein we describe the importance of experimental design, the separation of the biological study into multiple analytical experiments and the incorporation of QC samples to provide the ability to perform signal correction in order to reduce analytical variation and to quantitatively determine analytical precision. In addition, we describe how to apply this in quality assurance processes. These innovations have opened up the capabilities to perform routine, large-scale, untargeted, MS-focused studies.

Characterization of the natural variation in Arabidopsis thaliana metabolome by the analysis of metabolic distance

Metabolite fingerprinting is widely used to unravel the chemical characteristics of biological samples. Multivariate data analysis and other statistical tools are subsequently used to analyze and visualize the plasticity of the metabolome and/or the relationship between those samples. However, there are limitations to these approaches for example because of the multi-dimensionality of the data that makes interpretation of the data obtained from untargeted analysis almost impossible for an average human being. These limitations make the biological information that is of prime importance in untargeted studies be partially exploited. Even in the case of full exploitation, current methods for relationship elucidation focus mainly on between groups variation and differences. Therefore, a measure that is capable of exploiting both between- and within-group biological variation would be of great value. Here, we examined the natural variation in the metabolome of nine Arabidopsis thaliana accessions grown under various environmental conditions and established a measure for the metabolic distance between accessions and across environments. This data analysis approach shows that there is just a minor correlation between genetic and metabolic diversity of the nine accessions. On the other hand, it delivers so far in Arabidopsis unexplored chemical information and is shown to be biologically relevant for resistance studies.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-011-0375-3) contains supplementary material, which is available to authorized users.

Characterization of The Human Tear Metabolome by LC–MS/MS

The tear film overlying the epithelial cells of the eye's surface is vital to visual function, and its composition is reflective of ocular surface health. The ultrasmall volume of tears poses challenges in its analysis, contributing to the limited number of reports on the tear metabolome. In addition, using a standard clinical method of tear collection posed some confounding factors in metabonomic analysis. We sought to establish an analytical platform for the global characterization of human tear metabolites. Following information dependent acquisition (IDA) directed liquid chromatography-tandem mass spectrometry (LC-MS/MS), isotope pattern matched peak mining was performed using Extracted Ion Chromatogram (XIC) manager within the PeakView software. Sixty metabolites representing diverse compound classes were identified in human tears, most of which have not been previously reported. Selected metabolites were verified using pure standards. Unsupervised chemometric analysis showed good separation between tear samples and blanks (PC1 = 87%, R(2) = 0.91, Q(2) = 0.87). The results demonstrated the potential of our platform for untargeted metabonomic studies of eye diseases.

Peripapillary Retinoschisis in High Myopia: A Novel Clinical Entity Revealed by SD-OCT

We reviewed the literature on the aqueous humor (AH) proteome of primary open angle glaucoma (POAG) patients in order to obtain deeper insight into the pathophysiology of POAG.We searched Pubmed and Embase up to May 2019 for studies that compared AH protein composition between POAG (cases) and cataract (controls). Untargeted studies (measuring the whole proteome, by LC-MS/MS) were divided into two subgroups depending on the type of surgery during which POAG AH was collected: glaucoma filtration surgery (subgroup 1) or cataract surgery (subgroup 2). We reanalyzed the raw data (subgroup 1) or combined the reported data (subgroup 2) to perform GO enrichment (GOrilla) and pathway analysis (Pathvisio).Out of 93 eligible proteomic studies, seven were untargeted studies that identified 863 AH proteins. We observed 73 differentially expressed proteins in subgroup 1 and 87 differentially expressed proteins in subgroup 2. Both subgroups were characterized by activation of the acute immune response, dysregulation of folate metabolism and dysregulation of the selenium micronutrient network. For subgroup 1 but not for subgroup 2, proteins of the complement system were significantly enriched.AH proteome of POAG patients shows strong activation of the immune system. In addition, analysis suggests dysregulation of folate metabolism and dysregulation of selenium as underlying contributors. In view of their glaucoma surgery, POAG patients of subgroup 1 most likely are progressive whereas POAG patients in subgroup 2 most likely have stable POAG. The proteome difference between these subgroups suggests that the complement system plays a role in POAG progression.

Expanding Coverage of the Metabolome for Global Metabolite Profiling

Mass spectrometry-based metabolomics is the comprehensive study of naturally occurring small molecules collectively known as the metabolome. Given the vast structural diversity and chemical properties of endogenous metabolites, biological extraction and chromatography methods bias the number, property, and concentration of metabolites detected by mass spectrometry and creates a challenge for global untargeted studies. In this work, we used Escherichia coli bacterial cells to explore the influence of solvent polarity, temperature, and pH in extracting polar and nonpolar metabolites simultaneously. In addition, we explored chromatographic conditions involving different stationary and mobile phases that optimize the separation and ionization of endogenous metabolite extracts as well as a mixture of synthetic standards. Our results reveal that hot polar solvents are the most efficient in extracting both hydrophilic and hydrophobic metabolites simultaneously. In addition, ammonium fluoride in the mobile phase substantially improved ionization efficiency in negative electrospray ionization mode by an average increase in signal intensity of 5.7 and over a 2-fold increase in the total number of features detected. The improvement in sensitivity with ammonium fluoride resulted in 3.5 times as many metabolite hits in databases compared to ammonium acetate or formic acid enriched mobile phases and allowed for the identification of unique metabolites involved in fundamental cellular pathways.