Metabolomics approach for determining growth-specific metabolites based on Fourier transform ion cyclotron resonance mass spectrometry

Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR/MS) is the best MS technology for obtaining exact mass measurements owing to its great resolution and accuracy, and several outstanding FT-ICR/MS-based metabolomics approaches have been reported. A reliable annotation scheme is needed to deal with direct-infusion FT-ICR/MS metabolic profiling. Correlation analyses can help us not only uncover relations between the ions but also annotate the ions originated from identical metabolites (metabolite derivative ions). In the present study, we propose a procedure for metabolite annotation on direct-infusion FT-ICR/MS by taking into consideration the classification of metabolite-derived ions using correlation analyses. Integrated analysis based on information of isotope relations, fragmentation patterns by MS/MS analysis, co-occurring metabolites, and database searches (KNApSAcK and KEGG) can make it possible to annotate ions as metabolites and estimate cellular conditions based on metabolite composition. A total of 220 detected ions were classified into 174 metabolite derivative groups and 72 ions were assigned to candidate metabolites in the present work. Finally, metabolic profiling has been able to distinguish between the growth stages with the aid of PCA. The constructed model using PLS regression for OD₆₀₀ values as a function of metabolic profiles is very useful for identifying to what degree the ions contribute to the growth stages. Ten phospholipids which largely influence the constructed model are highly abundant in the cells. Our analyses reveal that global modification of those phospholipids occurs as E. coli enters the stationary phase. Thus, the integrated approach involving correlation analyses, metabolic profiling, and database searching is efficient for high-throughput metabolomics. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Software-assisted serum metabolite quantification using NMR

The goal of metabolomics is to analyze a whole metabolome under a given set of conditions, and accurate and reliable quantitation of metabolites is crucial. Absolute concentration is more valuable than relative concentration; however, the most commonly used method in NMR-based serum metabolic profiling, bin-based and full data point peak quantification, provides relative concentration levels of metabolites and are not reliable when metabolite peaks overlap in a spectrum. In this study, we present the software-assisted serum metabolite quantification (SASMeQ) method, which allows us to identify and quantify metabolites in NMR spectra using Chenomx software. This software uses the ERETIC2 utility from TopSpin to add a digitally synthesized peak to a spectrum. The SASMeQ method will advance NMR-based serum metabolic profiling by providing an accurate and reliable method for absolute quantification that is superior to bin-based quantification.     

Liquid chromatography–mass spectrometry based global metabolite profiling: A review

Untargeted, global metabolite profiling (often described as metabonomics or metabolomics) represents an expanding research topic and is, potentially, a major pillar for systems biology studies. To obtain holistic metabolic profiles from complex samples, such as biological fluids or tissue extracts, requires powerful, high resolution and information-rich analytical methods and for this spectroscopic technologies are generally used. Mass spectrometry, coupled to liquid chromatography (LC–MS), is increasingly being used for such investigations as a result of the significant advances in both technologies over the past decade. Here we try to critically review the topic of LC–MS-based global metabolic profiling and describe and compare the results offered by different analytical strategies and technologies. This review highlights the current challenges, limitations and opportunities of the current methodology.     

基于超高效液相色谱-四极杆飞行时间高分辨质谱的高通量血清代谢组学方法

采用超高效液相色谱-四极杆飞行时间高分辨质谱(UPLC-QTOFMS)联用技术, 对血清前处理进行了考察和优化, 并对液-质联用分析条件进行了优化, 旨在建立用于血清中广谱小分子代谢物分析的高通量强耐用代谢组学方法, 以期满足大批次生物样本数代谢组学研究的要求(样本数≥400个). 通过考察不同有机溶剂沉淀蛋白对血清中蛋白的去除程度及38个代表性标准品化合物的提取效果, 确定采用甲醇/乙腈(体积比1: 9)沉淀蛋白法. 血清和有机溶剂的体积比不小于1: 4时, 达到最佳的沉淀蛋白处理效果, 符合大批量样本测试的要求; 预先冷藏沉淀蛋白的有机溶剂, 涡旋2 min, 超声1 min, 加入有机溶剂后冷冻静置10 min, 可以进一步提高前处理沉淀蛋白和化合物提取的效果. 通过对不同流动相体系和梯度条件的考察, 对液-质联用分析条件进行了优化. 方法学考察结果表明, 本方法重现性、 精密度及稳定性均良好. 对重现性和48 h稳定性数据进行变异系数分析和主成分分析法的多维分析, 证明本方法在代谢组学研究中的可重复性及所得数据的可靠性. 本方法高通量强耐用, 每天可测定100多个血清样本(13 min测定1个样本), 适用于代谢组学研究, 特别是大批次生物样本的代谢组学研究.     

UPLC-ESI-QTOF/MS and multivariate data analysis for blood plasma and serum metabolomics: Effect of experimental artefacts and anticoagulant

Clotting and anticoagulation of blood samples may give rise to different metabolic profiles of serum and plasma samples, respectively. The anticoagulant used for blood plasma preparation may affect the resulting metabolic profile due to different mechanisms involved in anticoagulation by various agents, e.g. heparin, EDTA and citrate. In the present study, we looked into metabolite and other differences in matched serum and plasma samples and different plasma preparations by using untargeted UPLC-ESI-QTOF/MS profiling and multivariate data analysis (PCA and OPLS-DA). Metabolite differences between serum and plasma samples were mainly related to small peptides reflecting presence or absence of coagulation. Only subtle metabolite differences between the different plasma preparations were noticed, which were primarily related to ion suppression or enhancement caused by citrate and EDTA anticoagulants. For the first time, we also report that anticoagulant counter cation (Na+ or K+) in Na-citrate and K-EDTA plasma can make some metabolites more dominant in ESI-MS. Polymeric material residues originating from blood collection tubes for serum preparation were observed only in serum samples. Hypoxanthine and xanthine were found at higher levels in serum than in plasma samples, possibly due to release from the clot. Mass spectral features of sodium formate and potassium formate ion clusters were detected in citrate and EDTA plasma samples, respectively, originating from formate in mobile phase and Na⁺ (in Na-citrate tubes) and K⁺ (in K-EDTA tubes). Among the anticoagulants, heparin is recommended for plasma samples used for LC-ESI/MS-based metabolomics of hydrophilic compounds because no plasma interferences or matrix effects were noticed for this polarity range. Citrate and EDTA should be avoided since interferences and serious matrix effects were encountered on some co-eluting polar metabolites. Serum is recommended as a second choice and an alternative to plasma. In conclusion, heparin plasma or serum should be the order of best choice for LC-ESI/MS-based metabolomics research.     

CE–MS for anionic metabolic profiling: An overview of methodological developments

The efficient profiling of highly polar and charged metabolites in biological samples remains a huge analytical challenge in metabolomics. Over the last decade, new analytical techniques have been developed for the selective and sensitive analysis of polar ionogenic compounds in various matrices. Still, the analysis of such compounds, notably for acidic ionogenic metabolites, remains a challenging endeavor, even more when the available sample size becomes an issue for the total analytical workflow. In this paper, we give an overview of the possibilities of capillary electrophoresis‐mass spectrometry (CE–MS) for anionic metabolic profiling by focusing on main methodological developments. Attention is paid to the development of improved separation conditions and new interfacing designs in CE–MS for anionic metabolic profiling. A complete overview of all CE–MS‐based methods developed for this purpose is provided in table format (Table 1) which includes information on sample type, separation conditions, mass analyzer and limits of detection (LODs). Selected applications are discussed to show the utility of CE–MS for anionic metabolic profiling, especially for small‐volume biological samples. On the basis of the examination of the reported literature in this specific field, we conclude that there is still room for the design of a highly sensitive and reliable CE–MS method for anionic metabolic profiling. A rigorous validation and the availability of standard operating procedures would be highly favorable in order to make CE–MS an alternative, viable analytical technique for metabolomics.     

Metabolomics approach based on utra-performance liquid chromatography coupled to mass spectrometry with chemometrics methods for high-throughput analysis of metabolite biomarkers to explore the abnormal metabolic pathways associated with myocardial dysfunction

Ultra-performance liquid chromatography/mass spectrometry-based metabolomics can been used for discovery of metabolite biomarkers to explore the metabolic pathway of diseases. Identification of metabolic pathways is key to understanding the pathogenesis and mechanism of disease. Myocardial dysfunction induced by sepsis (SMD) is a severe complication of septic shock and represents major causes of death in intensive care units; however its pathological mechanism is still not clear. In this study, ultrahigh-pressure liquid chromatography with mass spectrometry-based metabolomics with chemometrics anaylsis and multivariate pattern recognition analysis were used to detect urinary metabolic profile changes in a lipopolysaccharide-induced SMD mouse model. Multivariate statistical analysis including principal component analysis and orthogonapartial least squares discriminant analysis for the discrimination of SMD was conducted to identify potential biomarkers. A total of 19 differential metabolites were discovered by high-resolution mass spectrometry-based urinary metabolomics strategy. The altered biochemical pathways based on these metabolites showed that tyrosine metabolism, phenylalanine metabolism, ubiquinone biosynthesis and vitamin B6 metabolism were closely connected to the pathological processes of SMD. Consequently, integrated chemometric analyses of these metabolic pathways are necessary to extract information for the discovery of novel insights into the pathogenesis of disease.     

Dealing with the unknown: Metabolomics and Metabolite Atlases

Metabolomics is the comprehensive profiling of the small molecule composition of a biological sample. Since metabolites are often the indirect products of gene expression, this approach is being used to provide new insights into a variety of biological systems (clinical, bioenergy, etc.). A grand challenge for metabolomics is the complexity of the data, which often include many experimental artifacts. This is compounded by the tremendous chemical diversity of metabolites. Identification of each uncharacterized metabolite is in many ways its own puzzle (compared with proteomics, which is based on predictable fragmentation patterns of polypeptides). Therefore, effective data reduction/prioritization strategies are critical for this rapidly developing field. Here we review liquid chromatography electrospray ionization mass spectrometry (LC/MS)-based metabolomics, methods for feature finding/prioritization, approaches for identifying unknown metabolites, and construction of method specific ‘Metabolite Atlases’.     

Development of an integrated metabolomic profiling approach for infectious diseases research

Metabolomic profiling offers direct insights into the chemical environment and metabolic pathway activities at sites of human disease. During infection, this environment may receive important contributions from both host and pathogen. Here we apply an untargeted metabolomics approach to identify compounds associated with an E. coli urinary tract infection population. Correlative and structural data from minimally processed samples were obtained using an optimized LC-MS platform capable of resolving ~2300 molecular features. Principal component analysis readily distinguished patient groups and multiple supervised chemometric analyses resolved robust metabolomic shifts between groups. These analyses revealed nine compounds whose provisional structures suggest candidate infection-associated endocrine, catabolic, and lipid pathways. Several of these metabolite signatures may derive from microbial processing of host metabolites. Overall, this study highlights the ability of metabolomic approaches to directly identify compounds encountered by, and produced from, bacterial pathogens within human hosts.     

Metabolomic Profiling of Serum from Human Pancreatic Cancer Patients Using <Superscript>1</Superscript>H NMR Spectroscopy and Principal Component Analysis

Pancreatic cancer is a malignant tumor with the worst prognosis among all cancers. At the time of diagnosis, surgical cure is no longer a feasible option for most patients, thus early detection of pancreatic cancer is crucial for its treatment. Metabolomics is a powerful new analytical approach to detect the metabolome of cells, tissue, or biofluids. Here, we report the application of ¹H nuclear magnetic resonance (NMR) combined with principal components analysis to discriminate pancreatic cancer patients from healthy controls based on metabolomic profiling of the serum. The metabolic analysis revealed significant lower of 3-hydroxybutyrate, 3-hydroxyisovalerate, lactate, and trimethylamine-N-oxide as well as significant higher level of isoleucine, triglyceride, leucine, and creatinine in the serum from pancreatic cancer patients compared to that of healthy controls. Our data demonstrate that the subtle differences in metabolite profiles in serum of pancreatic cancer patients and that of healthy subjects as a result of physiological and pathological variations could be identified by NMR-based metabolomics and exploited as metabolic markers for the early detection of pancreatic cancer.     

CE-MS for metabolomics: Developments and applications in the period 2018–2020

Capillary electrophoresis-mass spectrometry (CE-MS) is now a mature analytical technique in metabolomics, notably for the efficient profiling of polar and charged metabolites. Over the past few years, (further) progress has been made in the design of improved interfacing techniques for coupling CE to MS; also, in the development of CE-MS approaches for profiling metabolites in volume-restricted samples, and in strategies that further enhance the metabolic coverage. In this article, which is a follow-up of a previous review article covering the years 2016–2018 (Electrophoresis 2019, 40, 165–179), the main (technological) developments in CE-MS methods and strategies for metabolomics are discussed covering the literature from July 2018 to June 2020. Representative examples highlight the utility of CE-MS in the fields of biomedical, clinical, microbial, plant and food metabolomics. A complete overview of recent CE-MS-based metabolomics studies is given in a table, which provides information on sample type and pretreatment, capillary coatings, and MS detection mode. Finally, some general conclusions and perspectives are given.     

The Effect of Blood Contained in the Samples on the Metabolomic Profile of Mouse Brain Tissue: A Study by NMR Spectroscopy

(1) Recently, metabolic profiling of the tissue in the native state or extracts of its metabolites has become increasingly important in the field of metabolomics. An important factor, in this case, is the presence of blood in a tissue sample, which can potentially lead to a change in the concentration of tissue metabolites and, as a result, distortion of experimental data and their interpretation. (2) In this paper, the metabolomic profiling based on NMR spectroscopy was performed to determine the effect of blood contained in the studied samples of brain tissue on their metabolomic profile. We used 13 male laboratory CD-1^® IGS mice for this study. The animals were divided into two groups. The first group of animals (n = 7) was subjected to the perfusion procedure, and the second group of animals (n = 6) was not perfused. The brain tissues of the animals were homogenized, and the metabolite fraction was extracted with a water/methanol/chloroform solution. Samples were studied by high-frequency ¹H-NMR spectroscopy with subsequent statistical data analysis. The group comparison was performed with the use of the Student’s test. We identified 36 metabolites in the brain tissue with the use of NMR spectroscopy. (3) For the major set of studied metabolites, no significant differences were found in the brain tissue metabolite concentrations in the native state and after the blood removal procedure. (4) Thus, it was shown that the presence of blood does not have a significant effect on the metabolomic profile of the brain in animals without pathologies.     

色谱-质谱联用技术在代谢组学中的应用

代谢组学是对生物体受外部刺激所产生的小分子代谢产物的变化或其随时间的变化进行研究的一门学科,以实现对体液、细胞以及组织提取物等复杂的生物样本中所有代谢产物的定性和定量分析为研究目标。色谱-质谱联用技术在代谢组学的研究中已显示出极大的发展潜力。本文主要综述近年来代谢组学研究中涉及的色谱-质谱联用技术及其数据处理方法,重点介绍各种分离技术的特点及其在应用中的关键问题,并对其在代谢组学应用中的未来发展给予展望。     

Metabolomic analysis of amino acids and organic acids in aging mouse eyes using gas chromatography–tandem mass spectrometry

This is a metabolomics study for monitoring altered amino acid (AA) and organic acid (OA) metabolism of in eyes from aging an mouse model at 8 and 18 weeks and 18 months. Simultaneous metabolic profiling analysis of OAs and AAs was performed as ethoxycarbonyl/methoxime/tert-butyldimethylsilyl derivatives by gas chromatography–tandem mass spectrometry. A total of 42 metabolites—24 AAs and 18 OAs—were determined and their composition values were normalized to the corresponding mean values of 8-week-old mice as the control group. Then their normalized values were plotted as star graphs, which were distorted and readily distinguishable for each age-related group. Among the 42 metabolites, 18 AAs and 11 OAs were age dependent and significantly different (p < 0.05). Principal component analysis and partial least squares discriminant analysis showed unclear separation between 8- and 18-week-old mice but clear separation between these and 18-month-old mice. In particular, the variable importance in projection scores of 4-hydroxyproline, cis-aconitic acid, glycine, isocitric acid, leucine, pipecolic acid and lysine from partial least-squares–discriminant analysis were higher than 1.3. A heatmap for the classification and visualization of 42 metabolites showed differences in metabolite changes with aging. Altered AA and OA profiles were monitored, which may explain the metabolic disturbance of AA and OA. These findings are related to mitochondrial dysfunctions related to energy metabolism and the impaired antioxidant system in the aging eye. Therefore, the present metabolomics results of the association between physiological states and altered metabolism of AA and OA will be useful for understanding the aging eye and related diseases.     

An enzyme that regulates ether lipid signaling pathways in cancer annotated by multidimensional profiling

Hundreds, if not thousands, of uncharacterized enzymes currently populate the human proteome. Assembly of these proteins into the metabolic and signaling pathways that govern cell physiology and pathology constitutes a grand experimental challenge. Here, we address this problem by using a multidimensional profiling strategy that combines activity-based proteomics and metabolomics. This approach determined that KIAA1363, an uncharacterized enzyme highly elevated in aggressive cancer cells, serves as a central node in an ether lipid signaling network that bridges platelet-activating factor and lysophosphatidic acid. Biochemical studies confirmed that KIAA1363 regulates this pathway by hydrolyzing the metabolic intermediate 2-acetyl monoalkylglycerol. Inactivation of KIAA1363 disrupted ether lipid metabolism in cancer cells and impaired cell migration and tumor growth in vivo. The integrated molecular profiling method described herein should facilitate the functional annotation of metabolic enzymes in any living system.     

Use of ultra-performance liquid chromatography/time-of-flight mass spectrometry with nozzle-skimmer fragmentation for comprehensive quantitative analysis of secondary metabolites in Arabidopsis thaliana

This study sought to develop techniques for LC/MS-based metabolomics and to verify that an MS/MS spectral tag (MS2T) could be used in practical secondary metabolite profiling. The retention time (RT), precursor ions, and fragment ions generated by nozzle-skimmer fragmentation were determined using ultra-performance liquid chromatography/time-of-flight mass spectrometry (UPLC/TOF-MS) and compared with the MS2T. A standard mix was analyzed with UPLC/TOF-MS under the same conditions as were used to construct the MS2T. The difference in RT for the standards was less than 0.15 min and the average RSD was about 2.8%, suggesting that the analysis was highly repeatable. Both precursor ions and fragment ions were observed when the cone voltage was 75 V. Experimental data and fragmentation pattern in the MS2T annotation list were highly similar. Wild-type and cas-1 mutant Arabidopsis thaliana samples treated with an elicitor were analyzed using UPLC/TOF-MS. Sixty-five peaks were successfully annotated. Fragment ions were observed with nozzle-skimmer fragmentation in 50 of 65 (77%) peaks. The reliability of annotation may have increased as a result of fragment ions. Results of multivariate analysis suggested that cas-1 was related to induction of the biosynthesis of these flavonoids. The devised method facilitated practical secondary metabolite profiling.     

Phenotypic taxonomy and metabolite profiling in microbial drug discovery

Microorganisms and in particular actinomycetes and microfungi are known to produce a vast number of bioactive secondary metabolites. For industrially important fungal genera such as Penicillium and Aspergillus the production of these compounds has been demonstrated to be very consistent at the species level. This means that direct metabolite profiling techniques such as direct injection mass spectrometry or NMR can easily be used for chemotyping/metabolomics of strains from both culture collections and natural samples using modern informatics tools. In this review we discuss chemotyping/metabolomics as part of intelligent screening and highlight how it can be used for identification and classification of filamentous fungi and for the discovery of novel compounds when used in combination with modern methods for dereplication. In our opinion such approaches will be important for future effective drug discovery strategies, especially for dereplication of culture collections in order to avoid redundancy in the selection of species. This will maximize the chemical diversity of the microbial natural product libraries that can be generated from fungal collections.     

Amino acid profiling in plant cell cultures: an inter-laboratory comparison of CE-MS and GC-MS

A CE-MS method for metabolic profiling of amino acids was developed and used in an integrated functional genomics project to study the response of Medicago truncatula liquid suspension cell cultures to stress. This project required the analysis of more than 500 root cell culture extracts. The CE-MS method profiled 20 biologically important amino acids. The CE-MS method required no sample derivatization prior to injection and used minimal sample preparation. The method is described in terms of CE and MS operational parameters, reproducibility of migration times and response ratios, sample preparation, sample throughput, and reliability. This method was then compared with a previously published report that used GC-MS metabolic profiling for the same tissues. The data reveal a high level of similarity between the CE-MS and GC-MS amino acid profiling methods, thus supporting these as complementary technologies for metabolomics. We conclude that CE-MS is a valid alternative to GC-MS for targeted profiling of metabolites, such as amino acids, and possesses some significant advantages over GC-MS.     

Comparison of different liquid chromatography stationary phases in LC-HRMS metabolomics for the detection of recombinant growth hormone doping control

Growth hormone (GH) is a polypeptide suspected of being used in horse racing to speed up physical performances. Despite scientific advances in the recent years, the control of its administration remains difficult. In order to improve it, a metabolomics study through LC-high resolution mass spectrometry measurements was recently initiated to assess the metabolic perturbations caused by recombinant equine growth hormone administration. Few tens of ions not identified structurally were highlighted as compounds responsible for the modification of metabolic profiling observed in treated animals. This previous work was based on the use of Uptisphere Strategy NEC as the chromatographic column. In parallel, more and more metabolomics studies showed the interest of the use of new chromatographic supports such as hydrophilic interaction chromatography for the analysis of polar compounds. It is in this context that an investigation was conducted on Uptisphere HDO and Luna hydrophilic interaction chromatography stationary phases to generate and process urinary metabolomics fingerprints, which could allow to establish a comparison with Uptisphere Strategy NEC. The chromatographic column the most adapted for the detection of new biomarkers of GH administration has been used to set up a relevant statistical model based on the analysis of more than hundred biological samples.     

Utilization of metabolomics to identify serum biomarkers for hepatocellular carcinoma in patients with liver cirrhosis

Characterizing the metabolic changes pertaining to hepatocellular carcinoma (HCC) in patients with liver cirrhosis is believed to contribute towards early detection, treatment, and understanding of the molecular mechanisms of HCC. In this study, we compare metabolite levels in sera of 78 HCC cases with 184 cirrhotic controls by using ultra performance liquid chromatography coupled with a hybrid quadrupole time-of-flight mass spectrometry (UPLC–QTOF MS). Following data preprocessing, the most relevant ions in distinguishing HCC cases from patients with cirrhosis are selected by parametric and non-parametric statistical methods. Putative metabolite identifications for these ions are obtained through mass-based database search. Verification of the identities of selected metabolites is conducted by comparing their MS/MS fragmentation patterns and retention time with those from authentic compounds. Quantitation of these metabolites is performed in a subset of the serum samples (10 HCC and 10 cirrhosis) using isotope dilution by selected reaction monitoring (SRM) on triple quadrupole linear ion trap (QqQLIT) and triple quadrupole (QqQ) mass spectrometers. The results of this analysis confirm that metabolites involved in sphingolipid metabolism and phospholipid catabolism such as sphingosine-1-phosphate (S-1-P) and lysophosphatidylcholine (lysoPC 17:0) are up-regulated in sera of HCC vs. those with liver cirrhosis. Down-regulated metabolites include those involved in bile acid biosynthesis (specifically cholesterol metabolism) such as glycochenodeoxycholic acid 3-sulfate (3-sulfo-GCDCA), glycocholic acid (GCA), glycodeoxycholic acid (GDCA), taurocholic acid (TCA), and taurochenodeoxycholate (TCDCA). These results provide useful insights into HCC biomarker discovery utilizing metabolomics as an efficient and cost-effective platform. Our work shows that metabolomic profiling is a promising tool to identify candidate metabolic biomarkers for early detection of HCC cases in high risk population of cirrhotic patients.