Review: Microfluidic applications in metabolomics and metabolic profiling

Metabolomics is an emerging area of research focused on measuring small molecules in biological samples. There are a number of different types of metabolomics, ranging from global profiling of all metabolites in a single sample to measurement of a selected group of analytes. Microfluidics and related technologies have been used in this research area with good success. The aim of this review article is to summarize the use of microfluidics in metabolomics. Direct application of microfluidics to the determination of small molecules is covered first. Next, important sample preparation methods developed for microfluidics and applicable to metabolomics are covered. Finally, a summary of metabolomic work as it relates to analysis of cellular events using microfluidics is covered.     

Human Plasma Metabolic Profiles of Coronary Heart Disease by Gas Chromatography-Mass Spectrometry with Monte Carlo Tree Approach

Metabolomics is a useful approach to explore systemic metabolic variation and to elucidate disease mechanisms. In this study, human plasma metabolic profiles of coronary heart disease (CHD) patients and healthy controls were obtained by gas chromatography-mass spectrometry (GC-MS). A relatively new pattern recognition method, the Monte Carlo tree (MCTree) approach, was used to explore metabolic differences between CHD patients and healthy controls. In this way, CHD patients with different severity of coronary atherosclerosis were classified by the corresponding metabolic profiles. Furthermore, important metabolites contributing to the classification were screened and identified by their mass spectra. Several potential biomarkers were discussed in some detail. The results demonstrated that the proposed method might be a useful tool for discovering metabolic abnormalities and potential biomarkers for diseases.     

From differentiating metabolites to biomarkers

The current developments in metabolomics and metabolic profiling technologies have led to the discovery of several new metabolic biomarkers. Finding metabolites present in significantly different levels between sample sets, however, does not necessarily make these metabolites useful biomarkers. The route to valid and applicable biomarkers (biomarker qualification) is long and demands a significant amount of work. In this overview, we critically discuss the current state-of-the-art of metabolic biomarker discovery, with highlights and shortcomings, and suggest a pathway to clinical usefulness.

The correspondence problem for metabonomics datasets

In metabonomics it is difficult to tell which peak is which in datasets with many samples. This is known as the correspondence problem. Data from different samples are not synchronised, i.e., the peak from one metabolite does not appear in exactly the same place in all samples. For datasets with many samples, this problem is nontrivial, because each sample contains hundreds to thousands of peaks that shift and are identified ambiguously. Statistical analysis of the data assumes that peaks from one metabolite are found in one column of a data table. For every error in the data table, the statistical analysis loses power and the risk of missing a biomarker increases. It is therefore important to solve the correspondence problem by synchronising samples and there is no method that solves it once and for all. In this review, we analyse the correspondence problem, discuss current state-of-the-art methods for synchronising samples, and predict the properties of future methods.     

色谱研究最新进展

色谱作为一种强大的分离分析手段,为推动生物医学和人类健康、环境监测与保护、食品安全与质检等领域的发展提供着解决问题的关键技术。最近几年来,我国色谱学科经历了全面快速发展,取得了令人瞩目的成绩,研究水平和国际地位得到巨大提升,色谱相关SCI论文发表数量居世界第一位。色谱应用领域也在不断扩大,尤其在蛋白质组学、代谢组学等新兴领域,以及药物、环境、食品分析等传统领域发挥越来越重要的作用。本文对近年来色谱学科(重点是我国色谱学科)在色谱技术、色谱填料和色谱柱、样品前处理方法、应用领域等方面的进展情况进行介绍。共引用109篇参考文献。     

Metabolic profiling of yeast culture using gas chromatography coupled with orthogonal acceleration accurate mass time-of-flight mass spectrometry: Application to biomarker discovery

Yeast and yeast cultures are frequently used as additives in diets of dairy cows. Beneficial effects from the inclusion of yeast culture in diets for dairy mammals have been reported, and the aim of this study was to develop a comprehensive analytical method for the accurate mass identification of the ‘global’ metabolites in order to differentiate a variety of yeasts at varying growth stages (Diamond V XP, Yea-Sacc and Levucell). Microwave-assisted derivatization for metabolic profiling is demonstrated through the analysis of differing yeast samples developed for cattle feed, which include a wide range of metabolites of interest covering a large range of compound classes. Accurate identification of the components was undertaken using GC-oa-ToFMS (gas chromatography-orthogonal acceleration-time-of-flight mass spectrometry), followed by principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) for data reduction and biomarker discovery. Semi-quantification (fold changes in relative peak areas) was reported for metabolites identified as possible discriminative biomarkers (p-value <0.05, fold change >2), including d-ribose (four fold decrease), myo-inositol (five fold increase), l-phenylalanine (three fold increase), glucopyranoside (two fold increase), fructose (three fold increase) and threitol (three fold increase) respectively.     

Urinary signature of anabolic steroids and glucocorticoids in humans by LC-MS

A metabonomic strategy based on LC-MS was employed to investigate the metabolic profile of urine samples from 20 athletes who had been tested positive for corticoids and anabolic steroids and 29 controls. In this aim, different sample preparations and chromatographic conditions were compared. The acquired LC-MS data of doped athletes and controls were subjected to analysis of variance (ANOVA) and principal component analysis (PCA). Using this approach, molecular signature of human urine was obtained showing that metabonomics could be a complementary tool to discriminate different urinary profiles and to track down metabolic changes in humans.     

Metabolic profiling of intracellular metabolites in fermentation broths from β-lactam antibiotics production by liquid chromatography–tandem mass spectrometry methods

An analytical platform comprising three LC–ESI-MS/MS methods is presented for qualitative and quantitative profiling of more than 200 intracellular metabolites. Employing a silica based zwitterionic stationary phase in the HILIC mode, in total 223 hydrophilic metabolites can be determined. In particular, amino acids, organic acids as well as nucleotide sugars were found to be well separable and detectable under acidic mobile phase conditions, while in comparison especially phosphates such as nucleotides, coenzymes or sugar phosphates as well as sugars and sugar acids performed better at higher pH. Additionally, 21 less polar analytes turned out to be amenable for separation and analysis on a pentafluorophenyl modified silica stationary phase in RP mode. Solutes were detected by tandem mass spectrometry on a triple quadrupole instrument in the selected reaction monitoring (SRM) mode and specific SRM transitions for 258 metabolites are provided. All three methods were validated with respect to the limit of quantification, linear dynamic range, precision and accuracy. Applicability of the analytical platform was evaluated by analysis of the targeted metabolites in extracts of β-lactam antibiotics fermentation broths. Thereby, 87 metabolites were determined qualitatively in penicillin fermentation broths, and 94 compounds were found in cephalosporin extracts. In addition, a number of selected metabolites that can be determined by at least two of the presented LC–MS/MS methods was analyzed quantitatively by both, external calibration using pure standards as well as by matrix-matched calibration performing standard addition. Quantitative results obtained with the different methods agreed well, however, for some analytes external calibration was found to be ill-suited due to matrix effects.     

Development of a method for enhancing metabolomics coverage of human sweat by gas chromatography–mass spectrometry in high resolution mode

Sweat has recently gained popularity as clinical sample in metabolomics analysis as it is a non-invasive biofluid the composition of which could be modified by certain pathologies, as is the case with cystic fibrosis that increases chloride levels in sweat. However, the whole composition of sweat is still unknown and there is a lack of analytical strategies for sweat analysis. The aim of the present study was to develop and validate a method for metabolomic analysis of human sweat by gas chromatography–time of flight/mass spectrometry (GC–TOF/MS) in high resolution mode. Thus, different sample preparation strategies were compared to check their effect on the profile of sweat metabolites. Sixty-six compounds were tentatively identified by the obtained MS information. Amino acids, dicarboxylic acids and other interesting metabolites such as myo-inositol and urocanic acid were identified. Among the tested protocols, methyoxiamination plus silylation after deproteinization was the most suited option to obtain a representative snapshot of sweat metabolome. The intra-day repeatability of the method ranged from 0.60 to 16.99% and the inter-day repeatability from 2.75 to 31.25%. As most of the identified metabolites are involved in key biochemical pathways, this study opens new possibilities to the use of sweat as a source of metabolite biomarkers of specific disorders.     

血清代谢轮廓分析在评估乙肝肝硬化病程中的应用

采用高效液相色谱-质谱联用(HPLC-MS)作为代谢组学研究平台,分析不同Child-Pugh分级肝硬化病人和健康人群的血清标本,获取代谢轮廓.对数据进行主成分分析(PCA)和正交偏最小二乘判别分析(OPLS-DA),用各组病例的80%作为训练数据构建疾病的OPLS-DA区分模型,以剩余的20%作为检测数据,观察模型对...     

Pairwise alignment of chromatograms using an extended Fisher–Rao metric

A conceptually new approach for aligning chromatograms is introduced and applied to examples of metabolite identification in human blood plasma by liquid chromatography–mass spectrometry (LC–MS). A square-root representation of the chromatogram's derivative coupled with an extended Fisher–Rao metric enables the computation of relative differences between chromatograms. Minimization of these differences using a common dynamic programming algorithm brings the chromatograms into alignment. Application to a complex sample, National Institute of Standards and Technology (NIST) Standard Reference Material 1950, Metabolites in Human Plasma, analyzed by two different LC–MS methods having significantly different ranges of elution time is described.     

Parameter selection for peak alignment in chromatographic sample profiling: objective quality indicators and use of control samples

In chromatographic profiling applications, peak alignment is often essential as most chromatographic systems exhibit small peak shifts over time. When using currently available alignment algorithms, there are several parameters that determine the outcome of the alignment process. Selecting the optimum set of parameters, however, is not straightforward, and the quality of an alignment result is at least partly determined by subjective decisions. Here, we demonstrate a new strategy to objectively determine the quality of an alignment result. This strategy makes use of a set of control samples that are analysed both spiked and non-spiked. With this set, not only the system and the method can be checked but also the quality of the peak alignment can be evaluated. The developed strategy was tested on a representative metabolomics data set using three software packages, namely Markerlynx™, MZmine and MetAlign. The results indicate that the method was able to assess and define the quality of an alignment process without any subjective interference of the analyst, making the method a valuable contribution to the data handling process of chromatography-based metabolomics data.