Extraction parameters for metabolomics from cultured cells

The successful extraction of metabolites is a critical step in metabolite profiling. By optimizing metabolite extraction, the range and quantitative capacity of metabolomics studies can be improved. We considered eight separate extraction protocols for the preparation of a metabolite extract from cultured mammalian cells. Parameters considered included temperature, pH, and cell washing before extraction. The effects on metabolite recovery were studied using a liquid chromatography high-resolution mass spectrometry (LC–HRMS) platform that measures metabolites of diverse chemical classes, including amino acids, lipids, and sugar derivatives. The temperature considered during the extraction or the presence of formic acid, a commonly used additive, was shown to have minimal effects on the measured ion intensities of metabolites. However, washing of samples before metabolite extraction, whether with water or phosphate-buffered saline, exhibited dramatic effects on measured intensities of both intracellular and extracellular metabolites. Together, these findings present a systematic assessment of extraction conditions for metabolite profiling.     

An optimized protocol for metabolome analysis in yeast using direct infusion electrospray mass spectrometry

A method for the global analysis of yeast intracellular metabolites, based on electrospray mass spectrometry (ES-MS), has been developed. This has involved the optimization of methods for quenching metabolism in Saccharomyces cerevisiae and extracting the metabolites for analysis by positive-ion electrospray mass spectrometry. The influence of cultivation conditions, sampling, quenching and extraction conditions, concentration step, and storage have all been studied and adapted to allow direct infusion of samples into the mass spectrometer and the acquisition of metabolic profiles with simultaneous detection of more than 25 intracellular metabolites. The method, which can be applied to other micro-organisms and biological systems, may be used for comparative analysis and screening of metabolite profiles of yeast strains and mutants under controlled conditions in order to elucidate gene function via metabolomics. Examples of the application of this analytical strategy to specific yeast strains and single-ORF yeast deletion mutants generated through the EUROFAN programme are presented.     

Gas chromatographic metabolic profiling: A sensitive tool for functional microbial ecology

Microbial metabolomics, which consists of a non-targeted analysis of the metabolites released from (‘exometabolome’) or existing in (‘endometabolome’) a cell has mostly been used to study the metabolism of particular microbes. Metabolomes also represent a picture of microbial activity and we suggest that the exometabolome may also contain pertinent information for studying microbial interaction networks. Gas chromatography coupled to mass spectrometry is the most commonly used technique in metabolomics studies. It allows a wide range of metabolites to be detected but requires the derivatisation of compounds prior to detection. This type of non-targeted analysis can introduce biases to the detection and quantification of the different metabolites, particularly at the extraction and derivatisation steps. The aims of this study, therefore, were to quantify the sources of variability and to test the sensitivity of the GC metabolic profiling approach to small environmental changes such as shifts in temperature. The temperature sensitivity of metabolic profiles was compared with that of catabolic profiles obtained using Biolog® microplates. Analytical variability was compared with biological variability by incubating bacterial strains isolated from soil with fructose at 20 °C and by replicating each step of the protocol (incubation, extraction and derivatisation). For both the endo- and the exometabolome, more than 70% of the total variability was of biological origin and principal components analysis clearly separated the strains along the first ordination axis. The endometabolome distinguished bacterial strains at the species level only, whereas separation was evident at the species and group level with the exometabolome. Temperature had a significant but differential effect on the metabolite production of the bacterial strains whilst their catabolic profiles remained relatively unaffected. The exometabolome was more sensitive to temperature shifts than the endometabolome, suggesting that this pool may be of interest for studies in environmental functional ecology.     

Gas chromatographic metabolic profiling: a sensitive tool for functional microbial ecology

Microbial metabolomics, which consists of a non-targeted analysis of the metabolites released from ('exometabolome') or existing in ('endometabolome') a cell has mostly been used to study the metabolism of particular microbes. Metabolomes also represent a picture of microbial activity and we suggest that the exometabolome may also contain pertinent information for studying microbial interaction networks. Gas chromatography coupled to mass spectrometry is the most commonly used technique in metabolomics studies. It allows a wide range of metabolites to be detected but requires the derivatisation of compounds prior to detection. This type of non-targeted analysis can introduce biases to the detection and quantification of the different metabolites, particularly at the extraction and derivatisation steps. The aims of this study, therefore, were to quantify the sources of variability and to test the sensitivity of the GC metabolic profiling approach to small environmental changes such as shifts in temperature. The temperature sensitivity of metabolic profiles was compared with that of catabolic profiles obtained using Biolog microplates. Analytical variability was compared with biological variability by incubating bacterial strains isolated from soil with fructose at 20 degrees C and by replicating each step of the protocol (incubation, extraction and derivatisation). For both the endo- and the exometabolome, more than 70% of the total variability was of biological origin and principal components analysis clearly separated the strains along the first ordination axis. The endometabolome distinguished bacterial strains at the species level only, whereas separation was evident at the species and group level with the exometabolome. Temperature had a significant but differential effect on the metabolite production of the bacterial strains whilst their catabolic profiles remained relatively unaffected. The exometabolome was more sensitive to temperature shifts than the endometabolome, suggesting that this pool may be of interest for studies in environmental functional ecology.     

Metabolomics reveals stage-specific metabolic pathways of microbial communities in two-stage anaerobic fermentation of corn-stalk

Analysis of intracellular metabolites is essential to delineate metabolic pathways of microbial communities for evaluation and optimization of anaerobic fermentation processes. The metabolomics are reported for a microbial community during two stages of anaerobic fermentation of corn stalk in a biogas digester using GC–MS. Acetonitrile/methanol/water (2:2:1, by vol) was the best extraction solvent for microbial community analysis because it yielded the largest number of peaks (>200), the highest mean summed value of identified metabolites (23) and the best reproducibility with a coefficient of variation of 30 % among four different extraction methods. Inter-stage comparison of metabolite profiles showed increased levels of sugars and sugar alcohols during methanogenesis and fatty acids during acidogenesis. Identification of stage-specific metabolic pathways using metabolomics can therefore assist in monitoring and optimization of the microbial community for increased biogas production during anaerobic fermentation.     

电热蒸发-催化热解-原子吸收法直接测定茶叶中镉

重金属镉（Cd）一直是茶叶产品质量安全关注的重点。本研究基于电热蒸发-催化热解-原子吸收光谱仪（SS-ETV-AAS）,使用镍材质样品舟,在300 mL/min空气条件下,350 ℃干燥20 s,350~725 ℃灰化55 s;引入300 mL/min氢气与空气反应形成氮氢混合气氛,在725~800 ℃（50 s）下完成Cd的蒸发;之后,在高岭土填料催化热解炉800 ℃和准直管700 ℃条件下,氮氢火焰原子吸收测定镉的含量。方法检出限（LOD）为0.3 ng/g、定量限（LOQ）为1.0 ng/g,R2>0.998,多次测定的相对标准偏差（RSD）为1.8%~8.6%,多种茶叶样品中Cd的测定值与微波消解石墨炉原子吸收光谱法（GFAAS）无显著性差异（P>0.05）,Cd的回收率在92%~107%之间。试验结果表明,该方法灵敏度高、稳定性好、简单高效,且无需消解处理,样品分析时间仅为3min,适用于茶叶中Cd的快速检测。     

Tissue disruption and extraction methods for metabolic profiling of an invertebrate sentinel species

Metabolic profiling of tissues needs special attention, because the compartmentalization of cellular constituents will be abolished by sample homogenization. This loss of partitioning leads to protein and metabolite instability in extracts, and therefore metabolite extraction protocols need to ensure very rapid inactivation of macromolecules as well as solubilization of metabolites. There are many published methods for tissue metabolome analysis, but no universally accepted standard, and a lack of measurable quality benchmarks. We developed a protocol for efficient tissue disruption and metabolite extraction of the earthworm Lumbricus rubellus guided by prior biological knowledge as well as metrics based on the data. In particular, we identified an unusual degree of instability of L. rubellus tissue extracts, and evaluated different approaches such as heating and filtration to counteract this. Finally, we evaluated four different solvent systems for comprehensive metabolite extraction using three analytical platforms (¹H NMR spectroscopy, GC?CMS, and direct-infusion FT-ICR-MS), and also compared bead-beating and cryogenic milling for tissue disruption. Initially we ranked methods by common analytical criteria (e.g. numbers and total intensity of detected peaks) in order to compare protocols. These approaches to assess protocol suitability proved to be inadequate to judge earthworm tissue extraction methods because of sample instability. Existing tissue extraction protocols should not be assumed to be automatically applicable to novel species.     

An improved sampling protocol for analysis of intracellular metabolites in Mortierella alpina

Sampling of intracellular metabolites in Mortierella alpina was investigated as part of a metabolomics study. After comparison of four sampling protocols, rapid filtration of the culture using a laboratory-made nylon filter and absorbent gauze under normal pressure followed by quenching in liquid N₂ and grinding (the improved protocol) was the most effective. Rapid filtration under normal pressure decreased intracellular metabolites leakage and subsequent grinding of cells contributed to intracellular metabolites extraction. The above quenching method together with 75?% (v/v) ethanol, buffered with 60?mM HEPES, at 80?°C for 3?min is therefore suitable for sampling intracellular metabolites in M. alpina.     

Global metabolomics characterization of bacteria: pre-analytical treatments and profiling

Pre-analytical treatments of bacteria are crucial steps in bacterial metabolomics studies. In order to achieve reliable samples that can best represent the global metabolic profile in vivo both qualitatively and quantitatively, many sample treatment procedures have been developed. The use of different methods makes it difficult to compare the results among different groups. In this work, E. coli samples were tested by using NMR spectroscopy. Both liquid N₂ and cold methanol quenching procedures reduce the cell membrane integrity and cause metabolites leakage. However, liquid N₂ quenching affected the cell viability and the NMR metabolites’ profile less than cold methanol procedure. Samples obtained by metabolite extraction were significantly superior over cell suspensions and cell lysates, with a higher number of detectable metabolites. Methanol/chloroform extraction proved most efficient at extraction of intracellular metabolites from both qualitative and quantitative points of view. Finally, standard operating procedures of bacterial sample treatments for NMR metabolomics study are presented.     

Metabolomic Method: UPLC-q-ToF Polar and Non-Polar Metabolites in the Healthy Rat Cerebellum Using an In-Vial Dual Extraction

Unbiased metabolomic analysis of biological samples is a powerful and increasingly commonly utilised tool, especially for the analysis of bio-fluids to identify candidate biomarkers. To date however only a small number of metabolomic studies have been applied to studying the metabolite composition of tissue samples, this is due, in part to a number of technical challenges including scarcity of material and difficulty in extracting metabolites. The aim of this study was to develop a method for maximising the biological information obtained from small tissue samples by optimising sample preparation, LC-MS analysis and metabolite identification. Here we describe an in-vial dual extraction (IVDE) method, with reversed phase and hydrophilic liquid interaction chromatography (HILIC) which reproducibly measured over 4,000 metabolite features from as little as 3mg of brain tissue. The aqueous phase was analysed in positive and negative modes following HILIC separation in which 2,838 metabolite features were consistently measured including amino acids, sugars and purine bases. The non-aqueous phase was also analysed in positive and negative modes following reversed phase separation gradients respectively from which 1,183 metabolite features were consistently measured representing metabolites such as phosphatidylcholines, sphingolipids and triacylglycerides. The described metabolomics method includes a database for 200 metabolites, retention time, mass and relative intensity, and presents the basal metabolite composition for brain tissue in the healthy rat cerebellum.     

Effects of heat-treatment on the stability and composition of metabolomic extracts from the earthworm <Emphasis Type="Italic">Eisenia fetida</Emphasis>

Environmental metabolomics studies employing earthworms as sentinels for soil contamination are numerous, but the instability of the metabolite extracts from these organisms has been minimally addressed. This study evaluated the efficacy of adding a heat-treatment step in two commonly used extraction protocols (Bligh and Dyer and D₂O phosphate buffer) as a pre-analytical stabilization method. The resulting metabolic profiles of Eisenia fetida were assessed using principal component analysis and NMR spectral evaluations. The heated Bligh and Dyer extractions produced stabilized profiles with minimal variation of the extracted metabolomic profiles over time, providing a more suitable method for metabolomic analysis of earthworm extracts.     

Comparison of quenching and extraction methodologies for metabolome analysis of Lactobacillus plantarum

Background

A reliable quenching and metabolite extraction method has been developed for Lactobacillus plantarum. The energy charge value was used as a critical indicator for fixation of metabolism.

Results

Four different aqueous quenching solutions, all containing 60% of methanol, were compared for their efficiency. Only the solutions containing either 70 mM HEPES or 0.85% (w/v) ammonium carbonate (pH 5.5) caused less than 10% cell leakage and the energy charge of the quenched cells was high, indicating rapid inactivation of the metabolism. The efficiency of extraction of intracellular metabolites from cell cultures depends on the extraction methods, and is expected to vary between micro-organisms. For L. plantarum, we have compared five different extraction methodologies based on (i) cold methanol, (ii) perchloric acid, (iii) boiling ethanol, (iv) chloroform/methanol (1:1) and (v) chloroform/water (1:1). Quantification of representative intracellular metabolites showed that the best extraction efficiencies were achieved with cold methanol, boiling ethanol and perchloric acid.

Conclusion

The ammonium carbonate solution was selected as the most suitable quenching buffer for metabolomics studies in L. plantarum because (i) leakage is minimal, (ii) the energy charge indicates good fixation of metabolism, and (iii) all components are easily removed during freeze-drying. A modified procedure based on cold methanol extraction combined good extractability with mild extraction conditions and high enzymatic inactivation. These features make the combination of these quenching and extraction protocols very suitable for metabolomics studies with L. plantarum.     

Global metabolite analysis: the influence of extraction methodology on metabolome profiles of Escherichia coli

The global pool of all metabolites in a cell, or metabolome, is a reflection of all the metabolic functions of an organism under any particular growth condition. In the absence of in situ methods capable of universally measuring metabolite pools, intracellular metabolite measurements need to be performed in vitro after extraction. In the past, a variety of cell lysis methods were adopted for assays of individual metabolites or groups of intermediates in pathways. In this study, metabolites were extracted from Escherichia coli using six different commonly used procedures including acid or alkaline treatments, permeabilization by freezing with methanol, high-temperature extraction in the presence of ethanol or methanol, and by lysis with chloroform-methanol. Metabolites were extracted by the six methods from cells grown under identical conditions and labeled with [14C]glucose. The metabolomes were compared after 2-dimensional thin-layer chromatography of labeled compounds. For global analysis, extraction with cold (-40 degrees C) methanol showed the greatest promise, allowing simultaneous resolution of more than 95 metabolite spots. In contrast, 80 or less spots were obtained with other extraction methods. Extraction also influenced quantitative analysis of particular compounds. Metabolites such as adenosine exhibited up to 20-fold higher abundance after cold methanol extraction than after extraction with acid, alkali, or chloroform. The simplicity, rapidity, and universality of cold methanol extraction offer great promise if a single method of lysis is to be adopted in metabolome analysis.     

Widely Targeted Metabolomics Based on Large-Scale MS/MS Data for Elucidating Metabolite Accumulation Patterns in Plants

Metabolomics is an ‘omics’ approach that aims toanalyze all metabolites in a biological sample comprehensively.The detailed metabolite profiling of thousands of plant sampleshas great potential for directly elucidating plant metabolicprocesses. However, both a comprehensive analysis and a highthroughput are difficult to achieve at the same time due tothe wide diversity of metabolites in plants. Here, we have establisheda novel and practical metabolomics methodology for quantifyinghundreds of targeted metabolites in a high-throughput manner.Multiple reaction monitoring (MRM) using tandem quadrupole massspectrometry (TQMS), which monitors both the specific precursorions and product ions of each metabolite, is a standard techniquein targeted metabolomics, as it enables high sensitivity, reproducibilityand a broad dynamic range. In this study, we optimized the MRMconditions for specific compounds by performing automated flowinjection analyses with TQMS. Based on a total of 61,920 spectrafor 860 authentic compounds, the MRM conditions of 497 compoundswere successfully optimized. These were applied to high-throughputautomated analysis of biological samples using TQMS coupledwith ultra performance liquid chromatography (UPLC). By thisanalysis, approximately 100 metabolites were quantified in eachof 14 plant accessions from Brassicaceae, Gramineae and Fabaceae.A hierarchical cluster analysis based on the metabolite accumulationpatterns clearly showed differences among the plant families,and family-specific metabolites could be predicted using a batch-learningself-organizing map analysis. Thus, the automated widely targetedmetabolomics approach established here should pave the way forlarge-scale metabolite profiling and comparative metabolomics.     

Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism

With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.     

Quality control for plant metabolomics: reporting MSI-compliant studies

The Metabolomics Standards Initiative (MSI) has recently released documents describing minimum parameters for reporting metabolomics experiments, in order to validate metabolomic studies and to facilitate data exchange. The reporting parameters encompassed by MSI include the biological study design, sample preparation, data acquisition, data processing, data analysis and interpretation relative to the biological hypotheses being evaluated. Herein we exemplify how such metadata can be reported by using a small case study – the metabolite profiling by GC-TOF mass spectrometry of Arabidopsis thaliana leaves from a knockout allele of the gene At1g08510 in the Wassilewskija ecotype. Pitfalls in quality control are highlighted that can invalidate results even if MSI reporting standards are fulfilled, including reliable compound identification and integration of unknown metabolites. Standardized data processing methods are proposed for consistent data storage and dissemination via databases.     

Mass spectrometry methods in metabolomics

The review deals with metabolomics, a new and rapidly growing area directed to the comprehensive analysis of metabolites of biological objects. Metabolites are characterized by various physical and chemical properties, traditionally studied by methods of analytical chemistry focused on certain groups of chemical substances. However, current progress in mass spectrometry has led to formation of rather unified methods, such as metabolic fingerprinting and metabolomic profiling, which allow defining thousands of metabolites in one biological sample and therefore draw “a modern portrait of metabolomics.” This review describes basic characteristics of these methods, ways of metabolite separation, and analysis of metabolites by mass spectrometry. The examples shown in this review, allow to estimate these methods and to compare their advantages and disadvantages. Besides that, we consider the methods, which are of the most frequent use in metabolomics; these include the methods for data processing and the required resources, such as software for mass spectra processing and metabolite search database. In the conclusion, general suggestions for successful metabolomic experiments are given.     

Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research

Mass spectrometry (MS) techniques, because of their sensitivity and selectivity, have become methods of choice to characterize the human metabolome and MS-based metabolomics is increasingly used to characterize the complex metabolic effects of nutrients or foods. However progress is still hampered by many unsolved problems and most notably the lack of well established and standardized methods or procedures, and the difficulties still met in the identification of the metabolites influenced by a given nutritional intervention. The purpose of this paper is to review the main obstacles limiting progress and to make recommendations to overcome them. Propositions are made to improve the mode of collection and preparation of biological samples, the coverage and quality of mass spectrometry analyses, the extraction and exploitation of the raw data, the identification of the metabolites and the biological interpretation of the results.     

Patterns of time since last meal revealed by sparse PCA in an observational LC–MS based metabolomics study

In metabolomics studies, liquid chromatography mass spectrometry (LC–MS) provides comprehensive information on biological samples. However, extraction of few relevant metabolites from this large and complex data is cumbersome. To resolve this issue, we have employed sparse principal component analysis (SPCA) to capture the underlying patterns and select relevant metabolites from LC–MS plasma profiles. The study involves a small pilot cohort with 270 subjects where each subject’s time since last meal (TSLM) has been recorded prior to plasma sampling. Our results have demonstrated that both PCA and SPCA can capture the TSLM patterns. Nevertheless, SPCA provides more easily interpretable loadings in terms of selection of relevant metabolites, which are identified as amino acids and lyso-lipids. This study demonstrates the utility of SPCA as a pattern recognition and variable selection tool in metabolomics. Furthermore, amino acids and lyso-lipids are determined as dominating compounds in response to TSLM.