Multi-dimensional mass spectrometry-based shotgun lipidomics and novel strategies for lipidomic analyses

Since our last comprehensive review on multi-dimensional mass spectrometry-based shotgun lipidomics (Mass Spectrom. Rev. 24 (2005), 367), many new developments in the field of lipidomics have occurred. These developments include new strategies and refinements for shotgun lipidomic approaches that use direct infusion, including novel fragmentation strategies, identification of multiple new informative dimensions for mass spectrometric interrogation, and the development of new bioinformatic approaches for enhanced identification and quantitation of the individual molecular constituents that comprise each cell's lipidome. Concurrently, advances in liquid chromatography-based platforms and novel strategies for quantitative matrix-assisted laser desorption/ionization mass spectrometry for lipidomic analyses have been developed. Through the synergistic use of this repertoire of new mass spectrometric approaches, the power and scope of lipidomics has been greatly expanded to accelerate progress toward the comprehensive understanding of the pleiotropic roles of lipids in biological systems.     

Chemical derivatization strategy for mass spectrometry-based lipidomics

Lipids, serving as the structural components of cellular membranes, energy storage, and signaling molecules, play the essential and multiple roles in biological functions of mammals. Mass spectrometry (MS) is widely accepted as the first choice for lipid analysis, offering good performance in sensitivity, accuracy, and structural characterization. However, the untargeted qualitative profiling and absolute quantitation of lipids are still challenged by great structural diversity and high structural similarity. In recent decade, chemical derivatization mainly targeting carboxyl group and carbon-carbon double bond of lipids have been developed for lipidomic analysis with diverse advantages: (i) offering more characteristic structural information; (ii) improving the analytical performance, including chromatographic separation and MS sensitivity; (iii) providing one-to-one chemical isotope labeling internal standards based on the isotope derivatization regent in quantitative analysis. Moreover, the chemical derivatization strategy has shown great potential in combination with ion mobility mass spectrometry and ambient mass spectrometry. Herein, we summarized the current states and advances in chemical derivatization-assisted MS techniques for lipidomic analysis, and their strengths and challenges are also given. In summary, the chemical derivatization-based lipidomic approach has become a promising and reliable technique for the analysis of lipidome in complex biological samples.     

Mass spectrometry‐based metabolomics towards understanding of gene functions with a diversity of biological contexts

Currently, mass spectrometry‐based metabolomics studies extend beyond conventional chemical categorization and metabolic phenotype analysis to understanding gene function in various biological contexts (e.g., mammalian, plant, and microbial). These novel utilities have led to many innovative discoveries in the following areas: disease pathogenesis, therapeutic pathway or target identification, the biochemistry of animal and plant physiological and pathological activities in response to diverse stimuli, and molecular signatures of host–pathogen interactions during microbial infection. In this review, we critically evaluate the representative applications of mass spectrometry‐based metabolomics to better understand gene function in diverse biological contexts, with special emphasis on working principles, study protocols, and possible future development of this technique. Collectively, this review raises awareness within the biomedical community of the scientific value and applicability of mass spectrometry‐based metabolomics strategies to better understand gene function, thus advancing this application's utility in a broad range of biological fields. © 2012 Wiley Periodicals, Inc., Mass Spec Rev 32:118–128, 2013     

Eight key rules for successful data-dependent acquisition in mass spectrometry-based metabolomics

In recent years, metabolomics has emerged as a pivotal approach for the holistic analysis of metabolites in biological systems. The rapid progress in analytical equipment, coupled to the rise of powerful data processing tools, now provides unprecedented opportunities to deepen our understanding of the relationships between biochemical processes and physiological or phenotypic conditions in living organisms. However, to obtain unbiased data coverage of hundreds or thousands of metabolites remains a challenging task. Among the panel of available analytical methods, targeted and untargeted mass spectrometry approaches are among the most commonly used. While targeted metabolomics usually relies on multiple-reaction monitoring acquisition, untargeted metabolomics use either data-independent acquisition (DIA) or data-dependent acquisition (DDA) methods. Unlike DIA, DDA offers the possibility to get real, selective MS/MS spectra and thus to improve metabolite assignment when performing untargeted metabolomics. Yet, DDA settings are more complex to establish than DIA settings, and as a result, DDA is more prone to errors in method development and application. Here, we present a tutorial which provides guidelines on how to optimize the technical parameters essential for proper DDA experiments in metabolomics applications. This tutorial is organized as a series of rules describing the impact of the different parameters on data acquisition and data quality. It is primarily intended to metabolomics users and mass spectrometrists that wish to acquire both theoretical background and practical tips for developing effective DDA methods.     

Evaluating and minimizing batch effects in metabolomics

Determining metabolomic differences among samples of different phenotypes is a critical component of metabolomics research. With the rapid advances in analytical tools such as ultrahigh-resolution chromatography and mass spectrometry, an increasing number of metabolites can now be profiled with high quantification accuracy. The increased detectability and accuracy raise the level of stringiness required to reduce or control any experimental artifacts that can interfere with the measurement of phenotype-related metabolome changes. One of the artifacts is the batch effect that can be caused by multiple sources. In this review, we discuss the origins of batch effects, approaches to detect interbatch variations, and methods to correct unwanted data variability due to batch effects. We recognize that minimizing batch effects is currently an active research area, yet a very challenging task from both experimental and data processing perspectives. Thus, we try to be critical in describing the performance of a reported method with the hope of stimulating further studies for improving existing methods or developing new methods.     

Mass spectrometry-based proteomics approaches applied in cataract research

Cataract, the opacification of the eye lens, is the leading cause of blindness worldwide--it accounts for approximately 42% of all cases. The lens fibers have the highest protein content within the body, more than 35% of their wet weight. Given the eye lens pure composition of highly abundant structural proteins crystallins (up to 90%), it seems to be an ideal proteomic entity to study and might be also hypothesized to model the other protein conformational diseases. Crystallins are extremely long-lived, and there is virtually no protein turnover. This provides great opportunities for post-translational modifications (PTM) to occur and to predispose lens to the cataract formation. Despite recent progress in proteomics, the human lens proteome remains largely unknown. Mass spectrometry hold great promise to determine which crystallin modifications lead to a cataract. Quantitative analysis of PTMs at the peptide level with proteomics is a powerful bioanalytical tool for lens-tissue samples, and provides more comprehensive results. New mass spectrometry-based approaches that are being applied to lens research will be highlighted. Finally, the future directions of proteomics cataract research will be outlined.     

Mass spectrometry-based "omics" technologies in cancer diagnostics

Many "omics" techniques have been developed for one goal: biomarker discovery and early diagnosis of human cancers. A comprehensive review of mass spectrometry-based "omics" approaches performed on various biological samples for molecular diagnosis of human cancers is presented in this article. Furthermore, the existing and potential problems/solutions (both de facto experimental and bioinformatic challenges), and future prospects have been extensively discussed. Although the use of present omic methods as diagnostic tools are still in their infant stage and consequently not ready for immediate clinical use, it can be envisaged that the "omics"-based cancer diagnostics will gradually enter into the clinic in next 10 years as an important supplement to current clinical diagnostics.     

静电场轨道阱质谱的进展

静电场轨道阱是近年来新兴的一种质谱质量分析器,它利用离子在特定静电场中运动频率的不同对阱内离子进行质量分析,由于其具有较高的分辨率和质量准确度,被广泛应用于化学、生物学、医学等领域。本工作对静电场轨道阱质谱的形成过程和基本原理进行了详细介绍;对轨道阱质谱在蛋白质组学、代谢组学等方面的应用做了简要综述;对轨道阱质谱与常压电离技术联用的最新进展进行了评述,并指出电喷雾萃取电离、低温等离子体探针等常压质谱技术与轨道阱质谱的联用将在蛋白质分析、化学合成、化学反应机理研究等诸多领域发挥重要作用,旨在推动我国相关质谱仪器的国产化进程。     

基于质谱技术的代谢组学在体外药物肝毒性评价中的研究进展

药物肝毒性是药物安全性评价的重要内容之一,研发早期对药物及其代谢产物潜在的肝毒性进行准确预测和评价,可以提高药物研发的成功率。将代谢组学技术与体外细胞模型相结合,以细胞代谢表型的变化为指标直接反映药物的毒性效应及毒性机制,能够改善临床前药物肝毒性的预测准确性,在药物肝毒性筛选研究中极具应用价值和发展潜力。本文综述了目前肝毒性研究中的细胞模型与培养方法,介绍了三维细胞培养模型在体外研究中的优势,并总结了基于质谱技术的代谢组学研究在体外细胞模型中的分析策略及其在药物肝毒性评价中的应用,其中基于质谱成像技术的空间分辨代谢组学方法在体外细胞模型研究中具有独特优势,有望发展成为体外肝毒性研究的有力工具。     

Mass spectrometry-based methods for the advanced characterization and structural analysis of lignin: A review

Lignin is currently one of the most promising biologically derived resources, due to its abundance and application in biofuels, materials and conversion to value aromatic chemicals. The need to better characterize and understand this complex biopolymer has led to the development of many different analytical approaches, several of which involve mass spectrometry and subsequent data analysis. This review surveys the most important analytical methods for lignin involving mass spectrometry, first looking at methods involving gas chromatography, liquid chromatography and then continuing with more contemporary methods such as matrix assisted laser desorption ionization and time-of-flight-secondary ion mass spectrometry. Following that will be techniques that directly ionize lignin mixtures—without chromatographic separation—using softer atmospheric ionization techniques that leave the lignin oligomers intact. Finally, ultra-high resolution mass analyzers such as FT-ICR have enabled lignin analysis without major sample preparation and chromatography steps. Concurrent with an increase in the resolution of mass spectrometers, there have been a wealth of complementary data analyses and visualization methods that have allowed researchers to probe deeper into the “lignome” than ever before. These approaches extract trends such as compound series and even important analytical information about lignin substructures without performing lignin degradation either chemically or during MS analysis. These innovative methods are paving the way for a more comprehensive understanding of this important biopolymer, as we seek more sustainable solutions for our human species’ energy and materials needs.     

生物样品中醇类代谢物的化学衍生化质谱分析方法研究进展

生物样品的代谢组学是近年来质谱领域的研究热点.醇类代谢物是生物样品非常重要的一类代谢物,主要包括脂肪醇、糖类、酚类、甘油酯类以及甾醇等.这些代谢物在体内承担着各种重要的生理学功能.然而,由于大多数醇类代谢物极性较低,缺乏易于离子化的基团,其在质谱领域的研究比胺类、酸类等代谢物少.化学衍生化技术通过设计靶向某官能团的有机...     

基于高分辨质谱的代谢组学分析技术研究进展

代谢组学研究的目标是对生物体系中所有内源小分子代谢物进行全面的定性和定量表征.由于代谢物组成复杂、种类繁多、理化性质各异、且浓度差异大,给分析工作带来了极大的挑战.高分辨质谱因具有高灵敏度、高质量分辨率和质量精度、宽动态范围等优势,已成为代谢组学研究的主流分析工具.本文综述了近5年来基于高分辨质谱的代谢组学分析技术和方...     

Mass spectrometry-based clinical proteomics: head-and-neck cancer biomarkers and drug-targets discovery

Mass spectrometry (MS)-based proteomics is a rapidly developing technology for both qualitative and quantitative analyses of proteins, and investigations into protein posttranslational modifications, subcellular localization, and interactions. Recent advancements in MS have made tremendous impact on the throughput and comprehensiveness of cancer proteomics, paving the way to unraveling deregulated cellular pathway networks in human malignancies. In turn, this knowledge is rapidly being translated into the discovery of novel potential cancer markers (PCMs) and targets for molecular therapeutics. Head-and-neck cancer is one of the most morbid human malignancies with an overall poor prognosis and severely compromised quality of life. Early detection and novel therapeutic strategies are urgently needed for more effective disease management. The characterizations of protein profiles of head-and-neck cancers and non-malignant tissues, with unprecedented sensitivity and precision, are providing technology platforms for identification of novel PCMs and drug targets. Importantly, low-abundance proteins are being identified and characterized, not only from the tumor tissues, but also from bodily fluids (plasma, saliva, and urine) in a high-throughput and unbiased manner. This review is a critical appraisal of recent advances in MS-based proteomic technologies and platforms for facilitating the discovery of biomarkers and novel drug targets in head-and-neck cancer. A major challenge in the discovery and verification of these cancer biomarkers is the typically limited availability of well-characterized and adequately stored clinical samples in tumor and sera banks, collected using recommended procedures, and with detailed information on clinical, pathological parameters, and follow-up. Most biomarker discovery studies use limited number of clinical samples and verification of cancer markers in large number of samples is beyond the scope of a single laboratory. The validation of these potential markers in large sample cohorts in multicentric studies is needed for their translation from the bench to the bedside.     

Mass spectrometry‐based holistic analytical approaches for metabolite profiling in systems biology studies

Metabonomics and metabolomics represent one of the three major platforms in systems biology. To perform metabolomics it is necessary to generate comprehensive “global” metabolite profiles from complex samples, for example, biological fluids or tissue extracts. Analytical technologies based on mass spectrometry (MS), and in particular on liquid chromatography–MS (LC–MS), have become a major tool providing a significant source of global metabolite profiling data. In the present review we describe and compare the utility of the different analytical strategies and technologies used for MS‐based metabolomics with a particular focus on LC–MS. Both the advantages offered by the technology and also the challenges and limitations that need to be addressed for the successful application of LC–MS in metabolite analysis are described. Data treatment and approaches resulting in the detection and identification of biomarkers are considered. Special emphasis is given to validation issues, instrument stability, and QA/quality control (QC) procedures. © 2011 Wiley Periodicals, Inc., Mass Spec Rev 30:884–906, 2011