Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives

Metabolomics involves the unbiased quantitative and qualitative analysis of the complete set of metabolites present in cells, body fluids and tissues (the metabolome). By analyzing differences between metabolomes using biostatistics (multivariate data analysis; pattern recognition), metabolites relevant to a specific phenotypic characteristic can be identified. However, the reliability of the analytical data is a prerequisite for correct biological interpretation in metabolomics analysis. In this review the challenges in quantitative metabolomics analysis with regards to analytical as well as data preprocessing steps are discussed. Recommendations are given on how to optimize and validate comprehensive silylation-based methods from sample extraction and derivatization up to data preprocessing and how to perform quality control during metabolomics studies. The current state of method validation and data preprocessing methods used in published literature are discussed and a perspective on the future research necessary to obtain accurate quantitative data from comprehensive GC-MS data is provided.     

Metabolomics and its role in understanding cellular responses in plants

A natural shift is taking place in the approaches being adopted by plant scientists in response to the accessibility of systems-based technology platforms. Metabolomics is one such field, which involves a comprehensive non-biased analysis of metabolites in a given cell at a specific time. This review briefly introduces the emerging field and a range of analytical techniques that are most useful in metabolomics when combined with computational approaches in data analyses. Using cases from Arabidopsis and other selected plant systems, this review highlights how information can be integrated from metabolomics and other functional genomics platforms to obtain a global picture of plant cellular responses. We discuss how metabolomics is enabling large-scale and parallel interrogation of cell states under different stages of development and defined environmental conditions to uncover novel interactions among various pathways. Finally, we discuss selected applications of metabolomics. This special review article is dedicated to the commemoration of the retirement of Dr. Oluf L. Gamborg after 25 years of service as Founding Managing Editor of Plant Cell Reports. RB and KN have contributed equally to this review.     

Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks

Now that complete genome sequences are available for a variety of organisms, the elucidation of gene functions involved in metabolism necessarily includes a better understanding of cellular responses upon mutations on all levels of gene products, mRNA, proteins, and metabolites. Such progress is essential since the observable properties of organisms - the phenotypes - are produced by the genotype in juxtaposition with the environment. Whereas much has been done to make mRNA and protein profiling possible, considerably less effort has been put into profiling the end products of gene expression, metabolites. To date, analytical approaches have been aimed primarily at the accurate quantification of a number of pre-defined target metabolites, or at producing fingerprints of metabolic changes without individually determining metabolite identities. Neither of these approaches allows the formation of an in-depth understanding of the biochemical behaviour within metabolic networks. Yet, by carefully choosing protocols for sample preparation and analytical techniques, a number of chemically different classes of compounds can be quantified simultaneously to enable such understanding. In this review, the terms describing various metabolite-oriented approaches are given, and the differences among these approaches are outlined. Metabolite target analysis, metabolite profiling, metabolomics, and metabolic fingerprinting are considered. For each approach, a number of examples are given, and potential applications are discussed.     

Metabolomics assisted metabolic network modeling and network wide analysis of metabolites in microbiology

Metabolomics is the science of qualitatively and quantitatively analyzing low molecular weight metabolites occur in a given biological system. It provides valuable information to elucidate the functional roles and relations of different metabolites in a metabolic pathway. In recent years, a large amount of research on microbial metabolomics has been conducted. It has become a useful tool for achieving highly efficient synthesis of target metabolites. At the same time, many studies have been conducted over the years in order to integrate metabolomics data into metabolic network modeling, which has yielded many exciting results. Additionally, metabolomics also shows great advantages in analyzing the relationship of metabolites network wide. Integrating metabolomics data into metabolic network construction and applying it in network wide analysis of cell metabolism would further improve our ability to control cellular metabolism and optimize the design of cell factories for the overproduction of valuable biochemicals. This review will examine recent progress in the application of metabolomics approaches in metabolic network modeling and network wide analysis of microbial cell metabolism.     

GC/MS based metabolomics: development of a data mining system for metabolite identification by using soft independent modeling of class analogy (SIMCA)

Background  

The goal of metabolomics analyses is a comprehensive and systematic understanding of all metabolites in biological samples. Many useful platforms have been developed to achieve this goal. Gas chromatography coupled to mass spectrometry (GC/MS) is a well-established analytical method in metabolomics study, and 200 to 500 peaks are routinely observed with one biological sample. However, only ~100 metabolites can be identified, and the remaining peaks are left as "unknowns".     

代谢组学及其在微藻研究中的应用

代谢组学以完整的生物体为研究对象,运用合适的分析测试手段检测靶向或非靶向代谢物,结合统计模型进行分析解释。随着微藻研究的深入,微藻与代谢组学结合探究分子作用机理的研究日益增多。本文总结代谢组学的发展概况、研究流程及常用分析技术特点和代谢组学在微藻领域的研究进展,展望代谢组学在微藻研究的应用前景与发展趋势,并提出实际应用中所面临的困难与挑战。     

Microbial metabolomics: past,present and future methodologies

Microbial metabolomics has received much attention in recent years mainly because it supports and complements a wide range of microbial research areas from new drug discovery efforts to metabolic engineering. Broadly, the term metabolomics refers to the comprehensive (qualitative and quantitative) analysis of the complete set of all low molecular weight metabolites present in and around growing cells at a given time during their growth or production cycle. This review focuses on the past, current and future development of various experimental protocols in the rapid developing area of metabolomics in the ongoing quest to reliably quantify microbial metabolites formed under defined physiological conditions. These developments range from rapid sample collection, instant quenching of microbial metabolic activity, extraction of the relevant intracellular metabolites as well as quantification of these metabolites using enzyme based and or modern high tech hyphenated analytical protocols, mainly chromatographic techniques coupled to mass spectrometry (LC-MSⁿ, GC-MSⁿ, CE-MSⁿ), where n indicates the number of tandem mass spectrometry, and nuclear magnetic resonance spectroscopy (NMR).     

The ABRF Metabolomics Research Group 2013 Study: Investigation of Spiked Compound Differences in a Human Plasma Matrix

Metabolomics is an emerging field that involves qualitative and quantitative measurements of small molecule metabolites in a biological system. These measurements can be useful for developing biomarkers for diagnosis, prognosis, or predicting response to therapy. Currently, a wide variety of metabolomics approaches, including nontargeted and targeted profiling, are used across laboratories on a routine basis. A diverse set of analytical platforms, such as NMR, gas chromatography-mass spectrometry, Orbitrap mass spectrometry, and time-of-flight-mass spectrometry, which use various chromatographic and ionization techniques, are used for resolution, detection, identification, and quantitation of metabolites from various biological matrices. However, few attempts have been made to standardize experimental methodologies or comparative analyses across different laboratories. The Metabolomics Research Group of the Association of Biomolecular Resource Facilities organized a “round-robin” experiment type of interlaboratory study, wherein human plasma samples were spiked with different amounts of metabolite standards in 2 groups of biologic samples (A and B). The goal was a study that resembles a typical metabolomics analysis. Here, we report our efforts and discuss challenges that create bottlenecks for the field. Finally, we discuss benchmarks that could be used by laboratories to compare their methodologies.     

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.     

Microbial metabolomics: toward a platform with full metabolome coverage

Achieving metabolome data with satisfactory coverage is a formidable challenge in metabolomics because metabolites are a chemically highly diverse group of compounds. Here we present a strategy for the development of an advanced analytical platform that allows the comprehensive analysis of microbial metabolomes. Our approach started with in silico metabolome information from three microorganisms-Escherichia coli, Bacillus subtilis, and Saccharomyces cerevisiae-and resulted in a list of 905 different metabolites. Subsequently, these metabolites were classified based on their physicochemical properties, followed by the development of complementary gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry methods, each of which analyzes different metabolite classes. This metabolomics platform, consisting of six different analytical methods, was applied for the analysis of the metabolites for which commercial standards could be purchased (399 compounds). Of these 399 metabolites, 380 could be analyzed with the platform. To demonstrate the potential of this metabolomics platform, we report on its application to the analysis of the metabolome composition of mid-logarithmic E. coli cells grown on a mineral salts medium using glucose as the carbon source. Of the 431 peaks detected, 235 (=176 unique metabolites) could be identified. These include 61 metabolites that were not previously identified or annotated in existing E. coli databases.     

Answering biological questions by analysis of the strawberry metabolome

Background

The qualitative and quantitative analysis of all low molecular weight metabolites within a biological sample, known as the metabolome, provides powerful insights into their roles in biological systems and processes. The study of all the chemical structures, concentrations, and interactions of the thousands of metabolites is called metabolomics. However present state of the art methods and equipment can only analyse a small portion of the numerous, structurally diverse groups of chemical substances found in biological samples, especially with respect to samples of plant origin with their huge diversity of secondary metabolites. Nevertheless, metabolite profiling and fingerprinting techniques have been applied to the analysis of the strawberry metabolome since their early beginnings.

Aim

The application of metabolomics and metabolite profiling approaches within strawberry research was last reviewed in 2011. Here, we aim to summarize the latest results from research of the strawberry metabolome since its last review with a special emphasis on studies that address specific biological questions.

Key scientific concepts

Analysis of strawberry, and other fruits, requires a plethora of analytical methods and approaches encompassing the analysis of primary and secondary metabolites, as well as capturing and quantifying volatile compounds that are related to aroma as well as fruit development, function and plant-to-plant communication. The success and longevity of metabolite and volatile profiling approaches in fruit breeding relies upon the ability of the approach to uncover biologically meaningful insights. The key concepts that must be addressed and are reviewed include: gene function analysis and genotype comparison, analysis of environmental effects and plant protection, screening for bioactive compounds for food and non-food uses, fruit development and physiology as well as fruit sensorial quality. In future, the results will facilitate fruit breeding due to the identification of metabolic QTLs and candidate genes for fruit quality and consumer preference.

     

生态代谢组学研究进展

代谢组学指某一生物系统中产生的或已存在的代谢物组的研究,以质谱和核磁共振技术为分析平台,以信息建模与系统整合为目标。随着代谢组学中的研究方法与技术成为生态学研究的有力工具,生态代谢组学概念应运而生,即研究某一个生物体对环境变化的代谢物组水平的响应。理清代谢组学与生态代谢组学学科发展的脉络,综述代谢组学研究中的常用技术及其优势与局限性,论述代谢组学技术在生态学研究中的应用现状,展望代谢组学技术与其他系统生物学组学技术的结合在生态学中的应用前景,提出生态代谢组学研究者未来要完成的任务和面对的挑战。     

代谢组学技术及其在临床研究中的应用

在后基因组时代, 系统生物学研究成为人们关注的焦点。转录组学、蛋白质组学等功能基因组学研究方法可同时检测药物或其他因素影响下大量基因或蛋白质的表达变化情况, 但这些变化不能与生物学功能的变化建立直接联系。代谢组学方法则可为代谢物含量变化与生物表型变化建立直接相关性。代谢组学研究的目的是定量分析一个生物系统内所有代谢物的含量, 进行全面代谢物分析需要分析化学技术的支撑, 核磁共振和基于质谱的分析技术是代谢组学研究的两种主要技术手段。代谢组学研究可产生大量数据信息, 对这些数据进行分析离不开化学统计学的应用, 比如主成分分析、多维缩放、各种聚类分析技术以及功能差异分析等。文章综述了近年来代谢组学分析技术及数据分析技术的研究进展, 在此基础上, 对代谢组学在临床研究及临床前研究中的应用研究进展进行了综述。对疾病代谢表型图谱的研究有助于人们了解疾病发生、发展以及致死的机制; 在临床条件下, 这些代谢图谱可以作为疾病诊断、预后以及治疗的评判标准。代谢物组成的变化是毒物胁迫对机体造成的最终影响, 利用代谢组技术可以直接反映毒物对机体的影响。质谱技术、核磁共振技术的应用使得药物筛选过程可以快速完成, 并有助于实现个性化用药。此外, 利用代谢组学技术还可以进行已知酶的新活性研究, 也可以研究未知酶。     

Recent advances of metabolomics in plant biotechnology

Biotechnology, including genetic modification, is a very important approach to regulate the production of particular metabolites in plants to improve their adaptation to environmental stress, to improve food quality, and to increase crop yield. Unfortunately, these approaches do not necessarily lead to the expected results due to the highly complex mechanisms underlying metabolic regulation in plants. In this context, metabolomics plays a key role in plant molecular biotechnology, where plant cells are modified by the expression of engineered genes, because we can obtain information on the metabolic status of cells via a snapshot of their metabolome. Although metabolome analysis could be used to evaluate the effect of foreign genes and understand the metabolic state of cells, there is no single analytical method for metabolomics because of the wide range of chemicals synthesized in plants. Here, we describe the basic analytical advancements in plant metabolomics and bioinformatics and the application of metabolomics to the biological study of plants.     

Review of recent developments in GC–MS approaches to metabolomics-based research

Background

Metabolomics aims to identify the changes in endogenous metabolites of biological systems in response to intrinsic and extrinsic factors. This is accomplished through untargeted, semi-targeted and targeted based approaches. Untargeted and semi-targeted methods are typically applied in hypothesis-generating investigations (aimed at measuring as many metabolites as possible), while targeted approaches analyze a relatively smaller subset of biochemically important and relevant metabolites. Regardless of approach, it is well recognized amongst the metabolomics community that gas chromatography-mass spectrometry (GC–MS) is one of the most efficient, reproducible and well used analytical platforms for metabolomics research. This is due to the robust, reproducible and selective nature of the technique, as well as the large number of well-established libraries of both commercial and ‘in house’ metabolite databases available.

Aim of review

This review provides an overview of developments in GC–MS based metabolomics applications, with a focus on sample preparation and preservation techniques. A number of chemical derivatization (in-time, in-liner, offline and microwave assisted) techniques are also discussed. Electron impact ionization and a summary of alternate mass analyzers are highlighted, along with a number of recently reported new GC columns suited for metabolomics. Lastly, multidimensional GC–MS and its application in environmental and biomedical research is presented, along with the importance of bioinformatics.

Key scientific concepts of review

The purpose of this review is to both highlight and provide an update on GC–MS analytical techniques that are common in metabolomics studies. Specific emphasis is given to the key steps within the GC–MS workflow that those new to this field need to be aware of and the common pitfalls that should be looked out for when starting in this area.