MALDI-Mass Spectrometric Imaging for the Investigation of Metabolites in Medicago truncatula Root Nodules

Most techniques used to study small molecules, such as pharmaceutical drugs or endogenous metabolites, employ tissue extracts which require the homogenization of the tissue of interest that could potentially cause changes in the metabolic pathways being studied¹. Mass spectrometric imaging (MSI) is a powerful analytical tool that can provide spatial information of analytes within intact slices of biological tissue samples^1-5. This technique has been used extensively to study various types of compounds including proteins, peptides, lipids, and small molecules such as endogenous metabolites. With matrix-assisted laser desorption/ionization (MALDI)-MSI, spatial distributions of multiple metabolites can be simultaneously detected. Herein, a method developed specifically for conducting untargeted metabolomics MSI experiments on legume roots and root nodules is presented which could reveal insights into the biological processes taking place. The method presented here shows a typical MSI workflow, from sample preparation to image acquisition, and focuses on the matrix application step, demonstrating several matrix application techniques that are useful for detecting small molecules. Once the MS images are generated, the analysis and identification of metabolites of interest is discussed and demonstrated. The standard workflow presented here can be easily modified for different tissue types, molecular species, and instrumentation.     

Scalable Data Analysis in Proteomics and Metabolomics Using BioContainers and Workflows Engines

The recent improvements in mass spectrometry instruments and new analytical methods are increasing the intersection between proteomics and big data science. In addition, bioinformatics analysis is becoming increasingly complex and convoluted, involving multiple algorithms and tools. A wide variety of methods and software tools have been developed for computational proteomics and metabolomics during recent years, and this trend is likely to continue. However, most of the computational proteomics and metabolomics tools are designed as single‐tiered software application where the analytics tasks cannot be distributed, limiting the scalability and reproducibility of the data analysis. In this paper the key steps of metabolomics and proteomics data processing, including the main tools and software used to perform the data analysis, are summarized. The combination of software containers with workflows environments for large‐scale metabolomics and proteomics analysis is discussed. Finally, a new approach for reproducible and large‐scale data analysis based on BioContainers and two of the most popular workflow environments, Galaxy and Nextflow, is introduced to the proteomics and metabolomics communities.     

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。     

Two birds with one stone: Doing metabolomics with your proteomics kit

Proteomic research facilities and laboratories are facing increasing demands for the integration of biological data from multiple ‘‐OMICS’ approaches. The aim to fully understand biological processes requires the integrated study of genomes, proteomes and metabolomes. While genomic and proteomic workflows are different, the study of the metabolome overlaps significantly with the latter, both in instrumentation and methodology. However, chemical diversity complicates an easy and direct access to the metabolome by mass spectrometry (MS). The present review provides an introduction into metabolomics workflows from the viewpoint of proteomic researchers. We compare the physicochemical properties of proteins and peptides with metabolites/small molecules to establish principle differences between these analyte classes based on human data. We highlight the implications this may have on sample preparation, separation, ionisation, detection and data analysis. We argue that a typical proteomic workflow (nLC‐MS) can be exploited for the detection of a number of aliphatic and aromatic metabolites, including fatty acids, lipids, prostaglandins, di/tripeptides, steroids and vitamins, thereby providing a straightforward entry point for metabolomics‐based studies. Limitations and requirements are discussed as well as extensions to the LC‐MS workflow to expand the range of detectable molecular classes without investing in dedicated instrumentation such as GC‐MS, CE‐MS or NMR.     

Design of experiments: an efficient strategy to identify factors influencing extraction and derivatization of Arabidopsis thaliana samples in metabolomic studies with gas chromatography/mass spectrometry

The usual aim in metabolomic studies is to quantify the entire metabolome of each of a series of biological samples. To do this for complex biological matrices, e.g., plant tissues, efficient and reproducible extraction protocols must be developed. However, derivatization protocols must also be developed if GC/MS (one of the mostly widely used analytical methods for metabolomics) is involved. The aim of this study was to investigate how different chemical and physical factors (extraction solvent, derivatization reagents, and temperature) affect the extraction and derivatization of the metabolome from leaves of the plant Arabidopsis thaliana. Using design of experiment procedures, variation was systematically introduced, and the effects of this variation were analyzed using regression models. The results show that this approach allows a reliable protocol for metabolomic analysis of Arabidopsis to be determined with a relatively limited number of experiments. Following two different investigations an extraction and derivatization protocol was chosen. Further, the reproducibility of the analysis of 66 endogenous compounds was investigated, and it was shown that both hydrophilic and lipophilic compounds were detected with high reproducibility.     

Precursor mass prediction by clustering ionization products in LC-MS-based metabolomics

Liquid chromatography-mass spectrometry (LC-MS) is becoming the dominant technology in metabolomics, involving the comprehensive analysis of small molecules in biological systems. However, its use is still limited mainly by challenges in global high-throughput identification of metabolites: LC-MS data is highly complex, particularly due to the formation of multiple ionization products from individual metabolites. To address the limitation in metabolite identification, we developed a principled approach, designed to exploit the multi-dimensional information hidden in the data. The workflow first clusters candidate ionization products of the same metabolite together which typically have similar retention time, then searches for mass relationships among them in order to determine their ion types and metabolite identity. The robustness of our approach was demonstrated by its application to the LC-MS profiles of cell culture supernatant, which accurately predicted most of the known media components in the samples. Compared to conventional methods, our approach was able to generate significantly fewer candidate metabolites without missing out valid ones, thus reducing false-positive matches. Additionally, improved confidence in identification is achieved since each prediction comes with a probable combination of known ion types. Hence, our integrative workflow provides precursor mass predictions with high confidence by identifying various ionization products which account for a large proportion of detected peaks, thus minimizing false positives.     

Metabolomics

Metabolomics is the systematic identification and quantitation of all metabolites in a given organism or biological sample. The enhanced resolution provided by nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), along with powerful chemometric software, allows the simultaneous determination and comparison of thousands of chemical entities, which has lead to an expansion of small molecule biochemistry studies in bacteria, plants, and mammals. Continued development of these analytical platforms will accelerate the widespread use of metabolomics and allow further integration of small molecules into systems biology. Here, recent studies using metabolomics in xenobiotic metabolism and genetically modified mice are highlighted.     

Metabolomics meets lipidomics: Assessing the small molecule component of metabolism

Metabolomics, including lipidomics, is emerging as a quantitative biology approach for the assessment of energy flow through metabolism and information flow through metabolic signaling; thus, providing novel insights into metabolism and its regulation, in health, healthy ageing and disease. In this forward-looking review we provide an overview on the origins of metabolomics, on its role in this postgenomic era of biochemistry and its application to investigate metabolite role and (bio)activity, from model systems to human population studies. We present the challenges inherent to this analytical science, and approaches and modes of analysis that are used to resolve, characterize and measure the infinite chemical diversity contained in the metabolome (including lipidome) of complex biological matrices. In the current outbreak of metabolic diseases such as cardiometabolic disorders, cancer and neurodegenerative diseases, metabolomics appears to be ideally situated for the investigation of disease pathophysiology from a metabolite perspective.     

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。     

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.

     

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.     

Metabolite Imager: customized spatial analysis of metabolite distributions in mass spectrometry imaging

Mass spectrometry (MS) is currently the most utilized analytical instrument for evaluating the metabolite composition of a biological sample at both the qualitative and quantitative level. The exponential growth of raw data generated through increasingly versatile mass spectrometers requires sophisticated algorithms to process and visualize the raw data to address biological questions. The structural and quantitative diversity of a single species’ metabolome (e.g. all metabolite species) under different experimental conditions itself forms a very large and complex dataset to analyze. We have developed a free, Java-based metabolomics application “Metabolite Imager” (www.metaboliteimager.com) that enables customized analysis and visualization of the metabolite distributions in tissues acquired through MS-based imaging approaches. Metabolite Imager algorithms perform customized targeted searching of metabolites through user-defined and publicly-available databases enabling the analysis of spatial distributions of large metabolite numbers in tissue sections. Metabolite Imager’s automated, two-dimensional image generator has several customizable features for producing high-resolution images. Additional Metabolite Imager algorithms support identifying targeted and unknown detected metabolites in selected tissue regions using spatially-based enrichment analysis that could impact metabolic engineering strategies. Co-localization algorithms of metabolites and selected ions by m/z enable analysis of precursor-product relationships in situ that will be important for expanding the biological context of metabolic pathways.     

微流控器件-质谱联用接口技术研究进展与应用

生物分析是生命科学研究中的重要环节,分析仪器的小型化是提高生物分析灵敏度、速度、通量和降低成本的有效途径之一.微流控技术能够方便地操纵微量样品,具有集成度高、样品耗量小、污染少等诸多其他常量流控技术难以具备的优点,适用于进行多通道样品处理和高通量分析.除广泛采用的光学和电化学检测手段外,质谱也被用作这些微流控器件的检测器,并逐渐形成了微流控器件-质谱联用技术专门研究领域,进一步促进了自动化程度好、灵敏度高、特异性强的高通量生物分析方法的迅速发展.在大量调研国内外文献的基础上,对微流控器件-质谱联用领域的研究背景和现状进行了综述,不但介绍了微流控器件的制造技术还着重介绍了微流控器件-质谱联用技术在蛋白质组学等生物质谱分析方面的应用和新近进展,评述了可能的发展趋势.     

Molecular characterization and targeted quantitative profiling of the sphingolipidome in rice

Recent advances in comprehensive metabolite profiling techniques, the foundation of metabolomics, is facilitating our understanding of the functions, regulation and complex networks of various metabolites in organisms. Here, we report a quantitative metabolomics technique for complex plant sphingolipids, composed of various polar head groups as well as structural isomers of hydrophobic ceramide moieties. Rice (Oryza sativa L.) was used as an experimental model of monocotyledonous plants and has been demonstrated to possess a highly complex sphingolipidome including hundreds of molecular species with a wide range of abundance. We established a high‐throughput scheme for lipid preparation and mass spectrometry‐based characterization of complex sphingolipid structures, which provided basic information to create a comprehensive theoretical library for targeted quantitative profiling of complex sphingolipids in rice. The established sphingolipidomic approach combined with multivariate analyses of the large dataset obtained clearly showed that different classes of rice sphingolipids, particularly including subclasses of glycosylinositol phosphoceramide with various sugar‐chain head groups, are distributed with distinct quantitative profiles in various rice tissues, indicating tissue‐dependent metabolism and biological functions of the lipid classes and subclasses. The sphingolipidomic analysis also highlighted that disruption of a lipid‐associated gene causes a typical sphingolipidomic change in a gene‐dependent manner. These results clearly support the utility of the sphingolipidomic approach in application to wide screening of sphingolipid‐metabolic phenotypes as well as deeper investigation of metabolism and biological functions of complex sphingolipid species in plants.     

Identification of 4-hydroxyandrost-4-ene-3,17-dione metabolites in prostatic cancer patients by liquid chromatography—mass spectrometry

Liquid chromatography with thermospray mass spectrometry has proved to be an invaluable technique for the study of metabolic degradation of xenobiotics in complex biological fluids. This paper describes the detection of 4-hydroxyandrost-4-ene-3,17-dione and its metabolites in urinary extracts from prostatic cancer patients. Several metabolites were detected including 4β,5α-dihydroxyandrostan-3,17-dione, 3,17-dihydroxyandrostan-4-ones and 3α-hydroxy-5β-androstan-4,17-dione.     

Data handling for interactive metabolomics: tools for studying the dynamics of metabolome-macromolecule interactions

All published metabolomics studies investigate changes in either absolute or relative quantities of metabolites. However, blood plasma, one of the most commonly studied biofluids for metabolomics applications, is a complex, heterogeneous mixture of lipoproteins, proteins, small organic molecules and ions which together undergo a variety of possible molecular interactions including metal complexation, chemical exchange processes, micellular compartmentation of metabolites, enzyme-mediated biotransformations and small-molecule-macromolecule binding. In particular, many low molecular weight (MW) compounds (including drugs) can exist both ‘free’ in solution and bound to proteins or within organised aggregates of macromolecules. To study the effects of e.g. disease on these interactions we suggest that new approaches are needed. We have developed a technique termed ‘interactive metabolomics’ or i-metabolomics. i-metabolomics can be defined as: “The study of interactions between low MW biochemicals and macromolecules in heterogeneous biosamples such as blood plasma, without pre-selection of the components of interest”. Standard 1D NMR experiments commonly used in metabolomics allow metabolite concentration differences between samples to be investigated because the intensity of each peak depends on the concentration of the compound in question. On the other hand, the instrument can be set-up to measure molecular interactions by monitoring the diffusion coefficients of molecules. According to the Stokes–Einstein equation, the diffusion coefficient of a molecule is inversely proportional to its effective size, as represented by the hydrodynamic radius. Therefore, when low MW compounds are non-covalently bound to proteins, the observed diffusion coefficient for the compound will be intermediate between those of its free and bound forms. By measuring diffusion by NMR, the degree of protein binding can be estimated for either low MW endogenous biochemicals or xenobiotics. This type of experiment is referred to as either Diffusion-Ordered Spectroscopy (DOSY) or Diffusion-Edited Spectroscopy, depending on the type of post-acquisition data processing applied to the spectra. Results presented in this paper demonstrate approaches for the non-selective modelling of metabolite-macromolecule interactions (i-metabolomics), whilst additionally highlighting some of the all too frequently ignored issues associated with interpretation of data derived from profiling of blood plasma.