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.     

The yeast metabolome addressed by electrospray ionization mass spectrometry: Initiation of a mass spectral library and its applications for metabolic footprinting by direct infusion mass spectrometry

Mass spectrometry (MS) has been a major driver for metabolomics, and gas chromatography (GC)-MS has been one of the primary techniques used for microbial metabolomics. The use of liquid chromatography (LC)-MS has however been limited, but electrospray ionization (ESI) is very well suited for ionization of microbial metabolites without any previous derivatization needed. To address the capabilities of ESI-MS in detecting the metabolome of Saccharomyces cerevisiae, the in silico metabolome of this organism was used as a template to present a theoretical metabolome. This showed that in combination with the specificity of MS up to 84% of the metabolites can be identified in a high mass accuracy ESI-spectrum. A total of 66 metabolites were systematically analyzed by positive and negative ESI-MS/MS with the aim of initiating a spectral library for ESI of microbial metabolites. This systematic analysis gave insight into the ionization and fragmentation characteristics of the different metabolites. With this insight, a small study of metabolic footprinting with ESI-MS demonstrated that biological information can be extracted from footprinting spectra. Statistical analysis of the footprinting data revealed discriminating ions, which could be assigned using the in silico metabolome. By this approach metabolic footprinting can advance from a classification method that is used to derive biological information based on guilt-by-association, to a tool for extraction of metabolic differences, which can guide new targeted biological experiments. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Discovery of biomarker candidates within disease by protein profiling: principles and concepts

Proteins and peptides present within clinical samples represent a valuable library of information regarding the ongoing processes within cells and tissues in health and disease. We have developed and validated novel technology applications that can be used to characterize the patterns of global protein expression in tissue and biofluids in either gel-based systems or by automated multidimensional nanocapillary liquid chromatography. Mass spectrophotometry platforms using MALDI MS and MS/MS or LTQ ion trap MS were capable of delivering sensitive and accurate identifications of hundreds of proteins contained in individual samples including individual forms of processing intermediates such as phospho peptides. The Systems Biology approach of integrating protein expression data with clinical data such as histopathology, clinical functional measurements, medical imaging scores, patient demographics, and clinical outcome provides a powerful tool for linking biomarker expression with biological processes that can be segmented and linked to disease presentation.     

Nanostructure-initiator mass spectrometry: a protocol for preparing and applying NIMS surfaces for high-sensitivity mass analysis

Nanostructure-initiator mass spectrometry (NIMS) is a new surface-based MS technique that uses a nanostructured surface to trap liquid ('initiator') compounds. Analyte materials adsorbed onto this 'clathrate' surface are subsequently released by laser irradiation for mass analysis. In this protocol, we describe the preparation of NIMS surfaces capable of producing low background and high-sensitivity mass spectrometric measurement using the initiator compound BisF17. Examples of analytes that adsorb to this surface are small molecules, drugs, lipids, carbohydrates and peptides. Typically, NIMS is used to analyze samples ranging from simple analytical standards and proteolytic digests to more complex samples such as tissues, cells and biofluids. Critical experimental considerations of NIMS are described. Specifically, NIMS sensitivity is examined as a function of pre-etch cleaning treatment, etching current density, etching time, initiator composition, sample concentration, sample deposition method and laser fluence. Typically, NIMS surface preparation can be completed in less than 2 h. Subsequent sample preparation requires 1-5 min, depending on sample deposition method. Mass spectrometric data acquisition typically takes 1-30 s per sample.     

Direct profiling and imaging of plant metabolites in intact tissues by using colloidal graphite-assisted laser desorption ionization mass spectrometry

Laser desorption/ionization (LDI)-based imaging mass spectrometry (MS) has been applied to several biological systems to obtain information about both the identities of the major chemical species and their localization. Colloidal graphite-assisted LDI (GALDI) MS imaging was introduced for the imaging of small molecules such as phospholipids, cerebrosides, oligosaccharides, flavonoids, and other secondary metabolites with high spatial homogeneity due to finely dispersed particles. Mass profiles and images of Arabidopsis thaliana have been recorded directly from various plant surfaces and cross sections. The main targeted metabolites were flavonoids and cuticular waxes, both of which are important in many aspects of functional genomics, proteomics, and metabolomics. The mass spectral profiles revealed tissue-specific accumulation of flavonoids in flowers and petals. In addition, many other location-specific ions were observed. The location and the degree of light-induced accumulation of flavonoids in stem sections were successfully probed by GALDI MS.     

Recent progress in mass spectrometry for single-cell metabolomics

Metabolites are the end products of cellular vital activities and can reflect the state of cellular to a certain extent. Rapid change of metabolites and the low abundance of signature metabolites cause difficulties in single-cell detection, which is a great challenge in single-cell metabolomics analysis. Mass spectrometry (MS) is a powerful tool that uniquely suited to detect intracellular small-molecule metabolites and has shown good application in single-cell metabolite analysis. In this mini-review, we describe three types of emerging technologies for MS-based single-cell metabolic analysis in recent years, including nano-ESI-MS based single-cell metabolomics analysis, high-throughput analysis via flow cytometry, and cellular metabolic imaging analysis. These techniques provide a large amount of single-cell metabolic data, allowing the potential of MS in single-cell metabolic analysis is gradually being explored and is of great importance in disease and life science research.     

Proteomics and metabolomics: the molecular make-up of toxic aromatic pollutant bioremediation

Microbial-mediated attenuation of toxic aromatic pollutants offers great potential for the restoration of contaminated environments in an ecologically acceptable manner. However, incomplete biological information regarding the regulation of growth and metabolism in many microbial communities restricts progress in the site-specific mineralization process. In the postgenomic era, recent advances in MS have allowed enormous progress in proteomics and elucidated many complex biological interactions. These research forefronts are now expanding toward the analysis of low-molecular-weight primary and secondary metabolites analysis, i.e., metabolomics. The advent of 2-DE in conjunction with MS offers a promising approach to address the molecular mechanisms of bioremediation. The two fields of proteomics and metabolomics have thus far worked separately to identify proteins and primary and secondary metabolites during bioremediation. A simultaneous study combining functional proteomics and metabolomics, i.e., proteometabolomics would create a system-wide approach to studying site-specific microorganisms during active mineralization processes. This article deals with advances in environmental proteomics and metabolomics and advocates the simultaneous study of both technologies to implement cell-free bioremediation.     

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。     

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.     

Combining intensity correlation analysis and MALDI imaging to study the distribution of flavonols and dihydrochalcones in Golden Delicious apples

Mass spectrometry-based imaging techniques applied to small molecules complement the growing research field of metabolomics and can be used to interpret many important biological processes occurring in plants. In untargeted imaging applications, chemical identification is a critical step since it cannot take advantage of separative techniques applied to neutral molecules (e.g. liquid chromatography). The use of high resolution spectrometers is of great help, but fragmentation experiments are often necessary. In many cases, the information on ion fragmentation is embedded in the data sets, because analytes break up during ionization, but the extraction of this information is not easy considering the complexity of the imaging data files. Here an approach is proposed for applying conventional untargeted MALDI (matrix-assisted laser desorption ionization) profiling and advanced data analysis to perform imaging of metabolites in apple tissues. The pipeline, based on intensity correlation analysis, is used to extract fragmentation information from untargeted, high resolution, wide range mass spectra and to reconstruct compound-specific images which can be used for interpretation purposes. The proposed approach was used to investigate the distribution of glycosylated flavonols and dihydrochalcones in Golden Delicious apples. The results indicate that the method is effective, showing a high potential for ascertaining detailed metabolite localization.     

Metabolomic Analysis of Rat Brain by High Resolution Nuclear Magnetic Resonance Spectroscopy of Tissue Extracts

Studies of gene expression on the RNA and protein levels have long been used to explore biological processes underlying disease. More recently, genomics and proteomics have been complemented by comprehensive quantitative analysis of the metabolite pool present in biological systems. This strategy, termed metabolomics, strives to provide a global characterization of the small-molecule complement involved in metabolism. While the genome and the proteome define the tasks cells can perform, the metabolome is part of the actual phenotype. Among the methods currently used in metabolomics, spectroscopic techniques are of special interest because they allow one to simultaneously analyze a large number of metabolites without prior selection for specific biochemical pathways, thus enabling a broad unbiased approach. Here, an optimized experimental protocol for metabolomic analysis by high-resolution NMR spectroscopy is presented, which is the method of choice for efficient quantification of tissue metabolites. Important strengths of this method are (i) the use of crude extracts, without the need to purify the sample and/or separate metabolites; (ii) the intrinsically quantitative nature of NMR, permitting quantitation of all metabolites represented by an NMR spectrum with one reference compound only; and (iii) the nondestructive nature of NMR enabling repeated use of the same sample for multiple measurements. The dynamic range of metabolite concentrations that can be covered is considerable due to the linear response of NMR signals, although metabolites occurring at extremely low concentrations may be difficult to detect. For the least abundant compounds, the highly sensitive mass spectrometry method may be advantageous although this technique requires more intricate sample preparation and quantification procedures than NMR spectroscopy. We present here an NMR protocol adjusted to rat brain analysis; however, the same protocol can be applied to other tissues with minor modifications.     

Imaging of intact tissue sections: moving beyond the microscope

MALDI-imaging MS is a new molecular imaging technology for direct in situ analysis of thin tissue sections. Multiple analytes can be monitored simultaneously without prior knowledge of their identities and without the need for target-specific reagents such as antibodies. Imaging MS provides important insights into biological processes because the native distributions of molecules are minimally disturbed, and histological features remain intact throughout the analysis. A wide variety of molecules can be imaged, including proteins, peptides, lipids, drugs, and metabolites. Several specific examples are presented to highlight the utility of the technology.     

Integrated mass spectrometry imaging and omics workflows on the same tissue section using grid-aided,parafilm-assisted microdissection

BackgroundIn spite of the number of applications describing the use of MALDI MSI, one of its major drawbacks is the limited capability of identifying multiple compound classes directly on the same tissue section.MethodsWe demonstrate the use of grid-aided, parafilm-assisted microdissection to perform MALDI MS imaging and shotgun proteomics and metabolomics in a combined workflow and using only a single tissue section. The grid is generated by microspotting acid dye 25 using a piezoelectric microspotter, and this grid was used as a guide to locate regions of interest and as an aid during manual microdissection. Subjecting the dissected pieces to the modified Folch method allows to separate the metabolites from proteins. The proteins can then be subjected to digestion under controlled conditions to improve protein identification yields.ResultsThe proof of concept experiment on rat brain generated 162 and 140 metabolite assignments from three ROIs (cerebellum, hippocampus and midbrain/hypothalamus) in positive and negative modes, respectively, and 890, 1303 and 1059 unique proteins. Integrated metabolite and protein overrepresentation analysis identified pathways associated with the biological functions of each ROI, most of which were not identified when looking at the protein and metabolite lists individually.ConclusionsThis combined MALDI MS imaging and multi-omics approach further extends the amount of information that can be generated from single tissue sections.General significanceTo the best of our knowledge, this is the first report combining both imaging and multi-omics analyses in the same workflow and on the same tissue section.     

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。     

An innovative approach to grape metabolomics: stilbene profiling by suspect screening analysis

Suspect screening analysis is a targeted metabolomics approach in which identification of compounds relies on specific available information such as their molecular formula and isotopic pattern. This method was applied to the study of grape metabolomics with an UPLC/MS high-resolution Q-TOF mass spectrometer (nominal resolution 40,000) coupled with a Jet Stream ionization source. The present paper describes the detailed qualitative and quantitative study of grape stilbenes, the principal polyphenols associated with the beneficial effects of drinking wine. For identification of compounds, a new database was expressly constructed from the molecular information of potential metabolites of grape and wine from the literature and other electronic databases. Currently, GrapeMetabolomics contains about a thousand putative grape compounds. If untargeted analysis of a sample provides identification of a new compound with a sufficiently confident score, it is added to the database. Thus, by increasing the number of samples studied, GrapeMetabolomics can be expanded. This method is effective for identification of the molecular formulae of several hundred metabolites in two runs (positive and negative ionization) with minimal sample preparation, and can also be used to analyse some single classes of compounds involved in cell and tissue metabolism. With this approach, a total of 18 stilbene derivatives was identified in two grape samples (Raboso Piave and Primitivo) on the basis of accurate mass measurements and isotopic patterns, and identification was confirmed by MS/MS analysis. The approach can also potentially be applied to the metabolomics of other plant varieties.     

Spatial single cell metabolomics: Current challenges and future developments

Single cell metabolomics is a rapidly advancing field of bio-analytical chemistry which aims to observe cellular biology with the greatest detail possible. Mass spectrometry imaging and selective cell sampling (e.g. using nanocapillaries) are two common approaches within the field. Recent achievements such as observation of cell–cell interactions, lipids determining cell states and rapid phenotypic identification demonstrate the efficacy of these approaches and the momentum of the field. However, single cell metabolomics can only continue with the same impetus if the universal challenges to the field are met, such as the lack of strategies for standardisation and quantification, and lack of specificity/sensitivity. Mass spectrometry imaging and selective cell sampling come with unique advantages and challenges which, in many cases are complementary to each other. We propose here that the challenges specific to each approach could be ameliorated with collaboration between the two communities driving these approaches.     

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".     

Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra

Nuclear magnetic resonance (NMR) and Mass Spectroscopy (MS) are the two most common spectroscopic analytical techniques employed in metabolomics. The large spectral datasets generated by NMR and MS are often analyzed using data reduction techniques like Principal Component Analysis (PCA). Although rapid, these methods are susceptible to solvent and matrix effects, high rates of false positives, lack of reproducibility and limited data transferability from one platform to the next. Given these limitations, a growing trend in both NMR and MS-based metabolomics is towards targeted profiling or "quantitative" metabolomics, wherein compounds are identified and quantified via spectral fitting prior to any statistical analysis.?Despite the obvious advantages of this method, targeted profiling is hindered by the time required to perform manual or computer-assisted spectral fitting. In an effort to increase data analysis throughput for NMR-based metabolomics, we have developed an automatic method for identifying and quantifying metabolites in one-dimensional (1D) proton NMR spectra. This new algorithm is capable of using carefully constructed reference spectra and optimizing thousands of variables to reconstruct experimental NMR spectra of biofluids using rules and concepts derived from physical chemistry and NMR theory. The automated profiling program has been tested against spectra of synthetic mixtures as well as biological spectra of urine, serum and cerebral spinal fluid (CSF). Our results indicate that the algorithm can correctly identify compounds with high fidelity in each biofluid sample (except for urine). Furthermore, the metabolite concentrations exhibit a very high correlation with both simulated and manually-detected values.     

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.