High-throughput Metabolite Analysis Research Articles

High-Throughput Metabolite Analysis of Unicellular Microalgae by Orthogonal Hybrid Ionization Label-Free Mass Cytometry.

Microalgae metabolite analysis is fundamental for the rational design of metabolic engineering strategies for the biosynthesis of high-value products. Mass spectrometry (MS) has been utilized for single-cell microalgae analysis. However, limitations in the detection throughput and polarities of detectable substances make it difficult to realize high-throughput screening of high-performance microalgae. Herein, a plasma-assisted label-free mass cytometry, named as PACyESI-MS, was proposed combining the advantages of orthogonal hybrid ionization and high-throughput MS analysis, which realized rapid metabolite detection of single microalgae. The cell detection throughput of PACyESI-MS was up to 52 cells/min. Dozens of the critical primary and secondary metabolites within single microalgae were detected simultaneously, including pigments, lipids, and energy metabolites. Furthermore, metabolite changes of Chlamydomonas reinhardtii and Haematococcus pluvialis under nitrogen deficiency stress were studied. Discrimination of Chlamydomonas under different nutrient conditions was realized using single-cell metabolite profiles obtained by PACyESI-MS. The relationships between the accumulation of bioactive astaxanthin and changes in functional primary metabolites of Haematococcus were investigated. It was demonstrated that PACyESI-MS can detect the flexible change of metabolites in single microalgae cells under different nutritional conditions and during the synthesis of high-value products, which is expected to become an important tool for the design of metabolic engineering-based high-performance microalgae factories.

Mass spectrometry-based approaches to explore metabolism regulating ferroptosis

Ferroptosis is a type of programmed cell death distinct from apoptosis or necroptosis. Ferroptosis is well characterized by an iron-dependent accumulation of lipid peroxides and disruption of cellular membrane integrity. Many metabolic alterations can prevent or accelerate ferroptosis induction. Recent advances in analytical techniques of mass spectrometry have allowed high-throughput analysis of metabolites known to be critical for understanding ferroptosis regulatory metabolism. In this review, we introduce mass spectrometry-based analytical methods contributing to recent discovery of various metabolic pathways regulating ferroptosis, focusing on cysteine metabolism, antioxidant metabolism, and poly-unsaturated fatty acid metabolism.

High-Throughput Single-Step plasma sample extraction optimization strategies with experimental design for LC-MS and GC–MS integrated metabolomics and lipidomics analysis

To acquire deeper insights into the underlying molecular mechanisms in biological systems, a comprehensive and in-depth examination of a wide diversity of molecular species is necessary. Especially, it is very crucial to examine intermediary molecular levels such as protein, metabolite, and lipid to demonstrate direct causative and functional linkages between genotype and phenotype. Therefore, multiple sample sets/aliquots and analytical methods are required to cover different intermediary molecular levels due to the dissimilarity of physicochemical characteristics of biomolecules. However, existing methods used for simultaneous analysis of metabolites and lipids have not been thoroughly tested for reproducibility and wide applicability. Here, we have developed and optimized high-throughput and robust metabolomics and lipidomics analysis using experimental design strategies for single-step extraction protocols and also method parameters. Due to the high number of variables, an approach based on a desirability function was applied to optimize sample preparation and chromatographic parameters of metabolomics and lipidomics analysis. In these experiments, metabolite extraction efficiency was tested with acetone, acetonitrile, ethanol, methanol, water, and their combination, while lipids with hexane, chloroform, dichloromethane, and methyl tert-butyl ether. In the presented study, it is found that methanol:water:chloroform (3:1:3, v/v/v) mixture was superior to the other extraction solvent combinations in all performance measures. Moreover, the derivatization steps in GC–MS and chromatographic parameters in LC-qTOF-MS were tested. The derivatization efficiency increased with a higher concentration of methoxyamine hydrochloride and incubation time with the derivatization reagent. For LC-qTOF-MS, reconstitution solution, and the column temperature was found critical for high throughput metabolomics and lipidomics analysis, respectively. This systematic optimization for sample preparation and method parameters for integrated GC–MS and LC-qTOF-MS allows simultaneous and high-throughput analysis of metabolites as well as lipids from a single sample, making it a simple and practical strategy for different sample types.

Metabolomics, Lipidomics, and Immunometabolism.

Metabolomics, lipidomics, and the study of cellular metabolism are gaining increasing interest particularly in the field of immunology, since the activation and effector functions of immune cells are profoundly controlled by changes in cellular metabolic asset. Among the different techniques that can be used for the evaluation of cellular metabolism, the Seahorse Extracellular Flux Analyzer allows the real time measurement of both glycolytic and mitochondrial respiration pathways in cells of interest, through the assessment of extracellular acidification and oxygen consumption rate. Metabolomics, on the other hand, is the high-throughput analysis of metabolites, i.e., the substrates, intermediates, and products of cellular metabolism, starting from biofluids, cells or tissues. The metabolome does not include lipids as their properties are different from water-soluble metabolites and are classified under the lipidome. Lipidomics analysis allows the identification and quantification of lipid species. Metabolomics and lipidomics are currently performed with mass-spectrometry coupled with liquid or gas chromatography (LC-MS or GC-MS) and/or nuclear-magnetic resonance (NMR). Here we describe the protocol for the evaluation of metabolic rate, metabolomics, and lipidomics in T cells, examining the detailed experimental approaches.

A preliminary study of rapid-fire high-throughput metabolite analysis using nano-flow injection/Q-TOFMS.

In this study, we demonstrated nano-flow injection analysis (nano-FIA) with quadrupole time-of-flight mass spectrometry (Q-TOFMS) for 17 highly polar intermediates produced during glycolysis, the tricarboxylic acid (TCA) cycle, and the pentose phosphate pathway (PPP). We optimized the analytical conditions for nano-flow injection/Q-TOFMS, and set the flow rate and ion source temperature to 1000nL/min and 150°C, respectively. Under optimal conditions, a single run was finished within 3min, and the RSD value of 50 sequential injections was 4.2%. The method also showed quantitativity of four stable-isotope-labeled compounds (r2 > 0.99), demonstrating its robustness, high repeatability, and specificity. In addition, we compared three sample-preparation methods for rodent blood samples and found that protein precipitation with threefold methanol was the most effective. Finally, we applied the method to plasma samples from the serotonin syndrome (SS) model and control rats, the results of which were evaluated by principal component analysis (PCA). The two groups showed clearly separated PCA score plots, suggesting that the method could successfully catch the differences in metabolic profiles between SS and control rats. The results obtained from our new method were further validated by using the established gas chromatography/tandem mass spectrometry method, which demonstrated that there were good correlations between the two methods (R = 0.902 and 0.958 for lactic acid and malic acid, respectively, each at p < 0.001), thus proving the validity of our method. The method described here enables high-throughput analysis of metabolites and will be of use for the rapid analysis of metabolic profiles. Graphical abstract.

Metabolomics Analysis of Glutamate Receptor Function.

Metabolomics analysis provides a unique opportunity for comprehensive mapping of glutamatergic system function using high-throughput metabolite analysis and providing a perspective of the enzymatic activity, which cannot be obtained by other systems biology techniques.

Metabolomic Analysis of Glioma Cells Using Nanoflow Liquid Chromatography-Tandem Mass Spectrometry.

Mass spectrometry (MS)-based techniques have been finding utility as sensitive, high throughput metabolite analysis tools for complex biological samples. We describe here a nanoflow liquid chromatography-tandem mass spectrometry (nano-LC-MS/MS) system we developed and applied to metabolic profiling of human cells. Metabolites are extracted from cells using methanol, and filtered through a C18 StageTip to remove large particles. Metabolite samples are separated by HPLC at a flow rate of 400-500 nl/min, then analyzed in both positive and negative ion modes in an LTQ-Orbitrap MS. Metabolite identification and differential analysis are performed using commercial or open source software. Protocols outlined in this chapter describe how nano-LC-MS can be applied to investigate metabolic profiling with limited biomass amount.

Toward Single-Cell Analysis by Plume Collimation in Laser Ablation Electrospray Ionization Mass Spectrometry

Ambient ionization methods for mass spectrometry have enabled the in situ and in vivo analysis of biological tissues and cells. When an etched optical fiber is used to deliver laser energy to a sample in laser ablation electrospray ionization (LAESI) mass spectrometry, the analysis of large single cells becomes possible. However, because in this arrangement the ablation plume expands in three dimensions, only a small portion of it is ionized by the electrospray. Here we show that sample ablation within a capillary helps to confine the radial expansion of the plume. Plume collimation, due to the altered expansion dynamics, leads to greater interaction with the electrospray plume resulting in increased ionization efficiency, reduced limit of detection (by a factor of ~13, reaching 600 amol for verapamil), and extended dynamic range (6 orders of magnitude) compared to conventional LAESI. This enhanced sensitivity enables the analysis of a range of metabolites from small cell populations and single cells in the ambient environment. This technique has the potential to be integrated with flow cytometry for high-throughput metabolite analysis of sorted cells.

Application of Metabolomics in Drug Discovery, Development and Theranostics

High throughput analysis of metabolites combined with systems biology is opening new opportunities in medical research and clinical applications. Investigation of metabolic phenotype and metabolic requirements of a disease opens new avenues for treatments. Analysis of metabolic changes in patients and in disease models following treatments provides new methods for investigation of therapeutic response. Combined examination of disease metabolic characteristics and treatment response provides markers for personalization of therapies. Metabolomics finds itself at an increasingly important junction in all of these applications. In this review, we will explore the advantages of combining metabolomics with systems biology to optimize the drug discovery process via in-depth analysis of biological systems and novel metabolism- targeted therapeutics. Assessment of efficacy and specificity of drugs that directly or indirectly target metabolism through analyses of metabolic changes will also be presented. Finally, we will highlight examples of metabolic biomarkers explored as promising theranostics and theranostic imaging tools aimed at therapy optimization and combined therapy and diagnostics. The focus of this review will be directed towards cancer, however, the overarching ideas as well as experimental and analysis approaches are easily transferable to other applications. In cancer research, metabolism is once again raising interest due to a better appreciation of the significance of altered metabolic phenotype in cancer development and progression. Investigation of metabolic changes in cancer following drug applications and identification of clinically relevant metabolic markers for particular cancer subtypes are becoming increasingly appreciated in treatment development and will be explored in this review. Keywords: Bioreactors, drug discovery, drug testing, fluxomics, metabolomics, theragnostics imaging, theragnostics treatment planning, systems biology.

EXPLORATION OF REACTANT-PRODUCT LIPID PAIRS IN MUTANT-WILD TYPE LIPIDOMICS EXPERIMENTS

High-throughput metabolite analysis is very important for biologists to identify the functions of genes. A mutation in a gene encoding an enzyme is expected to alter the level of the metabolites which serve as the enzyme’s reactant(s) (also known as substrate) and product(s). To find the function of a mutated gene, metabolite data from a wild-type organism and a mutant are compared and candidate reactants and products are identified. The screening principle is that the concentration of reactants will be higher and the concentration of products will be lower in the mutant than in wild type. This is because the mutation reduces the reaction between the reactant and the product in the mutant organism. Based upon this principle, we suggest a method to screen metabolite pairs for candidate reactant-product pairs. Metrics are defined that quantify the effect of a mutation on each potential reaction, represented by a metabolite pair. For reactions catalyzed by well-characterized enzymes, one or more biologically functioning reactant-product pairs are known. Knowledge of the functional reactant-product pairs informs the development of the metrics. The goal is for ranking of the metrics for all possible pairs to reflect the likelihood that a particular metabolite pair is a functional reactant-product pair.

MIPHENO: data normalization for high throughput metabolite analysis

BackgroundHigh throughput methodologies such as microarrays, mass spectrometry and plate-based small molecule screens are increasingly used to facilitate discoveries from gene function to drug candidate identification. These large-scale experiments are typically carried out over the course of months and years, often without the controls needed to compare directly across the dataset. Few methods are available to facilitate comparisons of high throughput metabolic data generated in batches where explicit in-group controls for normalization are lacking.ResultsHere we describe MIPHENO (Mutant Identification by Probabilistic High throughput-Enabled Normalization), an approach for post-hoc normalization of quantitative first-pass screening data in the absence of explicit in-group controls. This approach includes a quality control step and facilitates cross-experiment comparisons that decrease the false non-discovery rates, while maintaining the high accuracy needed to limit false positives in first-pass screening. Results from simulation show an improvement in both accuracy and false non-discovery rate over a range of population parameters (p < 2.2 × 10-16) and a modest but significant (p < 2.2 × 10-16) improvement in area under the receiver operator characteristic curve of 0.955 for MIPHENO vs 0.923 for a group-based statistic (z-score). Analysis of the high throughput phenotypic data from the Arabidopsis Chloroplast 2010 Project (http://www.plastid.msu.edu/) showed ~ 4-fold increase in the ability to detect previously described or expected phenotypes over the group based statistic.ConclusionsResults demonstrate MIPHENO offers substantial benefit in improving the ability to detect putative mutant phenotypes from post-hoc analysis of large data sets. Additionally, it facilitates data interpretation and permits cross-dataset comparison where group-based controls are missing. MIPHENO is applicable to a wide range of high throughput screenings and the code is freely available as Additional file 1 as well as through an R package in CRAN.

Metabonomics and its role in amino acid nutrition research

Metabonomics combines metabolic profiling and multivariate data analysis to facilitate the high-throughput analysis of metabolites in biological samples. This technique has been developed as a powerful analytical tool and hence has found successful widespread applications in many areas of bioscience. Metabonomics has also become an important part of systems biology. As a sensitive and powerful method, metabonomics can quantitatively measure subtle dynamic perturbations of metabolic pathways in organisms due to changes in pathophysiological, nutritional, and epigenetic states. Therefore, metabonomics holds great promise to enhance our understanding of the complex relationship between amino acids and metabolism to define the roles for dietary amino acids in maintaining health and the development of disease. Such a technique also aids in the studies of functions, metabolic regulation, safety, and individualized requirements of amino acids. Here, we highlight the common workflow of metabonomics and some of the applications to amino acid nutrition research to illustrate the great potential of this exciting new frontier in bioscience.

MALDI−MS-Based High-Throughput Metabolite Analysis for Intracellular Metabolic Dynamics

In the present study, a high-throughput analytical method for intracellular metabolites using MALDI-MS has been developed. As an analytical tool, the quantitative performance and dynamic range of MALDI-TOF-MS was confirmed to be suitable for characterizing the trends of intracellular metabolism. The technique was tested by investigating the intracellular metabolism of Escherichia coli by analyzing whole cell samples taken consecutively before and after a perturbation of the environmental carbon source. As the result, dramatic changes of metabolite concentrations responding to the perturbation were observed. The whole analysis process (i.e., sample preparation and MALDI-MS analysis for 24 time points in triplicate) was completed within 4 hours. MALDI-FTICR-MS was used to identify the elemental compositions of detected metabolites to support the reliability of the MALDI-MS-based analysis. The MALDI-MS-based analytical method developed herein should be suitable for high-throughput analysis of dynamic intracellular metabolism events.

Method for Determining Molar Concentrations of Metabolites in Complex Solutions from Two-Dimensional 1H−13C NMR Spectra

One-dimensional (1D) (1)H nuclear magnetic resonance (NMR) spectroscopy is used extensively for high-throughput analysis of metabolites in biological fluids and tissue extracts. Typically, such spectra are treated as multivariate statistical objects rather than as collections of quantifiable metabolites. We report here a two-dimensional (2D) (1)H-(13)C NMR strategy (fast metabolite quantification, FMQ, by NMR) for identifying and quantifying the approximately 40 most abundant metabolites in biological samples. To validate this technique, we prepared mixtures of synthetic compounds and extracts from Arabidopsis thaliana, Saccharomyces cerevisiae, and Medicago sativa. We show that accurate (technical error 2.7%) molar concentrations can be determined in 12 min using our quantitative 2D (1)H-(13)C NMR strategy. In contrast, traditional 1D (1)H NMR analysis resulted in 16.2% technical error under nearly ideal conditions. We propose FMQ by NMR as a practical alternative to 1D (1)H NMR for metabolomics studies in which 50-mg (extract dry weight) samples can be obtained.

Extracellular metabolomics: A metabolic footprinting approach to assess fiber degradation in complex media

This work reports the implementation and optimization of a method for high-throughput analysis of metabolites produced by the breakdown of natural polysaccharides by microorganisms. Our simple protocol enables simultaneous separation and quantification of more than 40 different sugars and sugar derivatives, in addition to several organic acids in complex media, using 50-μl samples and a standard gas chromatography–mass spectrometry platform that was fully optimized for this purpose. As an implementation proof-of-concept, we assayed extracellular metabolite levels of three bacterial strains cultivated on complex medium rich in polysaccharides and under identical growth conditions. We demonstrate that the metabolic footprinting profile data distinguish among sample types such as typical metabolomics data. Moreover, we demonstrate that the differential metabolite-level data provide insight on specific fibrolytic activity of the different microbial strains and lay the groundwork for integrated proteome–metabolome studies of fiber-degrading microorganisms.

Review: Metabolome characterisation in plant system analysis.

Plant biology, like all other fields of biology, has undergone a major transition in the last few years. With increasing access to ever-improving technologies, the reductionist approach is being augmented or replaced by holistic, system based approaches. In plant species, this transition has been greatly facilitated both by completion of the Arabidopsis genome sequence and development of novel tools to generate and characterise genetic diversity. Following these advances, the focus of plant genetic research has shifted from sequencing and identification of genes to functional characterisation of gene products. The tools currently being adopted to ensure as comprehensive a description of either environmental or genetic perturbations of biological systems as possible range from relatively simple morphological analyses to the more complex analytical profiling of mRNA transcripts, protein or metabolites. In this review, methods of high-throughput metabolite analysis are highlighted, with particular emphasis on those that allow determination of steady-state concentrations of a broad spectrum of metabolites. In addition, possibilities of adopting metabolic flux analytical techniques from medicinal and microbial sciences for the study of plant systems are discussed. Finally, I attempt to define future prospects for metabolome analyses within the wider context of systems biology.