基于离子淌度质谱的代谢物碰撞截面积测量方法和数据库研究进展

代谢组学旨在全面系统地分析复杂生物样本中的代谢物。近年来,离子淌度质谱（IM-MS）技术快速发展,为代谢组学分析提供了强大的技术支撑。离子淌度质谱根据代谢物的化学结构进行气相分离,其衍生的碰撞截面积（CCS）可作为一种新的物理化学性质,辅助用于鉴定已知和未知代谢物的化学结构。碰撞截面积在代谢组学中的应用需要确保对其准确测量,同时需要构建高覆盖、高准确的碰撞截面积数据库。本文旨在介绍常见的不同类型商业化离子淌度质谱及其对小分子代谢物碰撞截面积测量和校正的原理,归纳目前可用于代谢组学应用的碰撞截面积数据库,并展望碰撞截面积在代谢组学中的应用。     

小胖威利综合征和天使综合征细胞株培养液的代谢组学差异分析

小胖威利综合征和天使综合征是两种在15号染色体长臂(15q11-q13)发生基因变异的遗传性疾病。本研究对这两种细胞株的培养液开展基于高效液相色谱-质谱联用的代谢物组学数据收集,并利用XCMS在线分析平台对代谢产物组进行全局对比分析。通过Metlin数据库筛查代谢物组差异分析所命中的代谢产物,进一步依据Bio Cyc Pathway数据库绘制出包含这些显著差异代谢物结点的代谢网络通路。数据分析共发现了70种显著差异的代谢物和36个被高度覆盖的代谢通路,在此基础上探究了首次发现的、在代谢差异分析中最为显著的吗啡生物合成和尼古丁降解通路与疾病表型的潜在关联性,为遗传疾病的无损分子诊断提供了代谢组学基础和生物标志物候选靶标。     

结合核磁共振技术与液质联用技术的代谢组学数据采集、处理和分析

代谢组学是近年来新兴起来的一门"组学"学科,主要研究不同状态下,如正常与应激、健康与疾病、野生型与基因突变型,生物系统内所有小分子代谢物(M.W.1500 Da)的变化,对其进行定性鉴定和定量分析,目的是发现和鉴别差异代谢物,揭示差异代谢物在不同状态下生物系统中的产生途径及其作用。代谢组学研究通常包括样品制备、数据采集、数据(预)处理和多变量分析等步骤,正确的数据采集、处理和分析是获得具有统计意义结果的前提。核磁共振技术(nuclear magnetic resonance,NMR)和液质联用技术(liquid chromatography-mass spectroscopy,LC-MS)是代谢组学研究的两种最主要分析手段。本文将主要针对结合NMR和LC-MS两种技术进行代谢组学研究的数据采集、处理、分析进行综述,并展望NMR和LC-MS两种技术在代谢组学领域的发展前景。     

候选药物T-VA的质谱裂解规律及其在大鼠体内的代谢产物研究

采用液相色谱-线性离子阱-静电场轨道阱高分辨质谱(LC/LTQ-Orbitrap MS)技术研究候选药物T-VA的裂解规律及体内代谢,建立了大鼠体内T-VA及其代谢产物的LC/MSⁿ分析方法,分析讨论了各自的主要碎片离子峰、质谱特征与结构信息,发现了血浆中主要代谢物M1。借鉴药物化学的方法,合成得到了代谢产物实体M1-1,经¹HNMR、¹³CNMR、HRMS确认其结构,根据其色谱、质谱特征进行验证,结果表明,合成得到的M1-1即为血浆主要代谢物M1。借助PC12细胞模型验证了代谢产物M1的神经保护活性,该结果可为进一步研究其生物转化过程与前药修饰提供重要信息。     

基于GC-MS的黄曲霉毒素致犬肝损伤的代谢组学研究

运用代谢组学方法研究了黄曲霉毒素(AF)对犬肝脏的损伤作用。通过气相色谱-质谱技术分析AF中毒犬血浆中的代谢物谱,使用主成分分析法研究犬AF中毒后血浆内源性代谢物谱的变化情况。结果表明,犬AF中毒后,血浆内源性代谢物谱随中毒时间的推移发生了相应的变化。血浆中的部分氨基酸(如L-异亮氨酸、甘氨酸、L-苏氨酸等)含量升高,葡萄糖、胆固醇以及部分游离脂肪酸(如软脂酸、硬脂酸等)含量下降。这些代谢物的变化与肝脏的损伤程度存在着一定的关系,而且其变化趋势与血液生化指标检测结果一致。由此可见,基于GC-MS的代谢组学技术能较全面地反应生物体的代谢状态,再结合主成分分析法对血浆代谢物的变化进行模式识别,有助于AF中毒的早期诊断。     

液相色谱-质谱用于代谢标志物的发现和鉴定

代谢组学是测量相对分子质量1 000以下的内源性代谢物的科学,目前已在疾病研究、药物研发及植物和微生物等领域均得到重视,是研究小分子的一个十分有用的工具。它以组织、体液或细胞为研究对象,最常用的技术是质谱和NMR。一般来说,代谢组学的研究包括样品采集、预处理、代谢组数据采集、多变量数据分析、标志物发现和识别及最终的生物解释。由于缺乏数据库及标样,代谢标志物的识别是研究的一个瓶颈。 在这里,我们将报告一个基于色谱-质谱联用技术的集成识别策略,包括多变量数据分析、精确分子量测定、质谱碎片裂解规律、色谱保留规律、馏分微制备、亲和色谱、酶解等。 药物作用机理和疾病标志物的代谢组学研究将作为例子阐明我们的策略。     

奋乃静在人胆汁中的代谢物分析

建立了超高效液相色谱 四极杆串联飞行时间质谱（UPLC/Q-TOF MS）检测并表征奋乃静在人胆汁中代谢物的方法。T管收集一名精神病患者服用奋乃静后的胆汁样品,经乙腈沉淀蛋白预处理后,采用UPLC/Q-TOF MS进行分析。根据高分辨质谱给出的准确分子质量信息推测可能的分子式,结合MS E功能采集的前体离子和产物离子信息,利用质量缺损过滤（MDF）和generic dealkylation等代谢物鉴定软件筛选代谢物。通过对比原形药物和代谢物的质谱裂解途径,推测可能的代谢物结构。在服药（4 mg,b.i.d）后的人胆汁中,共检测到29种奋乃静代谢物,包括I相代谢物16种,II相代谢物13种,其中16种为首次报道的新颖代谢物。奋乃静在人体内的主要代谢途径包括羟基化、脱氢、N-去烷基化、甲基化、硫酸及葡萄糖醛酸结合等,该结果进一步完善了奋乃静在人体内的代谢途径。     

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...     

高效液相色谱-质谱联用技术在口腔癌代谢组学分析中的应用

目的:探讨超高效液相色谱-四级杆飞行时间高分辨质谱联用技术(UPLC/Q-TOF-MS)在口腔癌代谢组学分析的应用效果,并分析其优势特点.方法:选择我院2020年1月至2020年12月口腔癌患者21例为观察组,同期体检人群21例为对照组,利用UPLC/Q-TOF-MS技术对两组的血浆、尿液和唾液进行代谢组学分析,然后采...     

血浆代谢组同时定性定量分析方法研究

数据非依赖的质谱采集是近年来发展的一种新型多级质谱分析方法,只需一次进样便可同时快速获取所有母离子及其子离子信息。本研究以肺癌血浆样本为研究对象,采用数据非依赖的SWATH(sequential windowed acquisition of all theoretical fragment ions)采集技术,建立了液相色谱-串联质谱法(LC-MS/MS)同时定性定量分析血浆代谢组。基于代谢物数据库识别比对,成功识别了93个代谢物,实现了代谢物的定性分析。对成功识别的代谢物建立定量分析方法并进行方法学考察。结果表明,其中90个代谢物的线性相关系数大于0.99,定量限在1.25～12 000μg/L之间;其中有86个代谢物可以满足生物样本定量分析的要求,其连续4天的日内精密度和日间精密度均小于20%;在4℃下,96 h内的放置稳定性在0.62%～19.35%之间。该方法的覆盖率高,能同时快速准确地对肺癌血浆中癌症相关代谢物进行定性定量分析,也适用于其他生物样品,可以为定量代谢组学分析提供重要的方法学平台。     

Development of LC/MS Techniques for Comprehensive Metabolome Analysis

Metabolomics is a rapidly evolving field for studying biological systems and discovering potential disease biomarkers. For any metabolomics application, metabolome analysis with adequate sensitivity and specificity is essential in defining the metabolome. Ideally, all metabolites present in a biological system are qualitatively and quantitatively profiled. Unfortunately, due to technical limitations, only a fraction of metabolites are currently analyzed by using techniques such as NMR and mass spectrometry (MS). Due to limited metabolome coverage, many important metabolome networks and some subdue changes in the metabolome may not be revealed with current techniques. In this presentation, several technical issues related to the development of LC/MS for enabling metabolome analysis will be discussed. Because of great diversity of chemical and physical properties of metabolites, we have been developing an isotope labeling LC/MS workflow with a goal of improving the metabolome coverage in analyzing biological samples such as human biofluids and tissue samples. Several labeling chemistries will be described to provide isotope tags to the metabolites for sensitive detection and accurate quantification. LC methods including multi-dimensional separation to separate the labeled metabolites with high efficiency will be discussed. New protocols for MS analysis, metabolite identification and quantitative data processing will be presented.     

Mass spectrometric based approaches in urine metabolomics and biomarker discovery

Urine metabolomics has recently emerged as a prominent field for the discovery of non‐invasive biomarkers that can detect subtle metabolic discrepancies in response to a specific disease or therapeutic intervention. Urine, compared to other biofluids, is characterized by its ease of collection, richness in metabolites and its ability to reflect imbalances of all biochemical pathways within the body. Following urine collection for metabolomic analysis, samples must be immediately frozen to quench any biogenic and/or non‐biogenic chemical reactions. According to the aim of the experiment; sample preparation can vary from simple procedures such as filtration to more specific extraction protocols such as liquid‐liquid extraction. Due to the lack of comprehensive studies on urine metabolome stability, higher storage temperatures (i.e. 4°C) and repetitive freeze‐thaw cycles should be avoided. To date, among all analytical techniques, mass spectrometry (MS) provides the best sensitivity, selectivity and identification capabilities to analyze the majority of the metabolite composition in the urine. Combined with the qualitative and quantitative capabilities of MS, and due to the continuous improvements in its related technologies (i.e. ultra high‐performance liquid chromatography [UPLC] and hydrophilic interaction liquid chromatography [HILIC]), liquid chromatography (LC)‐MS is unequivocally the most utilized and the most informative analytical tool employed in urine metabolomics. Furthermore, differential isotope tagging techniques has provided a solution to ion suppression from urine matrix thus allowing for quantitative analysis. In addition to LC‐MS, other MS‐based technologies have been utilized in urine metabolomics. These include direct injection (infusion)‐MS, capillary electrophoresis‐MS and gas chromatography‐MS. In this article, the current progresses of different MS‐based techniques in exploring the urine metabolome as well as the recent findings in providing potentially diagnostic urinary biomarkers are discussed. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:115–134, 2017.     

Mass spectrometry-based metabolomics

This review presents an overview of the dynamically developing field of mass spectrometry-based metabolomics. Metabolomics aims at the comprehensive and quantitative analysis of wide arrays of metabolites in biological samples. These numerous analytes have very diverse physico-chemical properties and occur at different abundance levels. Consequently, comprehensive metabolomics investigations are primarily a challenge for analytical chemistry and specifically mass spectrometry has vast potential as a tool for this type of investigation. Metabolomics require special approaches for sample preparation, separation, and mass spectrometric analysis. Current examples of those approaches are described in this review. It primarily focuses on metabolic fingerprinting, a technique that analyzes all detectable analytes in a given sample with subsequent classification of samples and identification of differentially expressed metabolites, which define the sample classes. To perform this complex task, data analysis tools, metabolite libraries, and databases are required. Therefore, recent advances in metabolomics bioinformatics are also discussed.     

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 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     

Recent advancements in LC-MS based analysis of biotoxins: Present and future challenges

There has been a rising concern regarding the harmful impact of biotoxins, source of origin, and the determination of the specific type of toxin. With numerous reports on their extensive spread, biotoxins pose a critical challenge to figure out their parent groups, metabolites, and concentration. In that aspect, liquid chromatography-mass spectrometry (LC-MS) based analysis paves the way for its accurate identification and quantification. The biotoxins are ideally categorized as phytotoxins, mycotoxins, shellfish-toxins, ciguatoxins, cyanotoxins, and bacterial toxins such as tetrodotoxins. Considering the diverse nature of biotoxins, both low-resolution mass spectrometry (LRMS) and high-resolution mass spectrometry (HRMS) methods have been implemented for their detection. The sample preparation strategy for complex matrix usually includes “QuEChERS” extraction or solid-phase extraction coupled with homogenization and centrifugation. For targeted analysis of biotoxins, the LRMS consisting of a tandem mass spectrometer operating in multiple reaction monitoring mode has been widely implemented. With the help of the reference standard, most of the toxins were accurately quantified. At the same time, the suspect screening and nontarget screening approach are facilitated by the HRMS platforms during the absence of reference standards. Significant progress has also been made in sampling device employment, utilizing novel sample preparation strategies, synthesizing toxin standards, employing hybrid MS platforms, and the associated data interpretation. This critical review attempts to elucidate the progress in LC-MS based analysis in the determination of biotoxins while pointing out major challenges and suggestions for future development.