代谢组学分析技术及其在几类重大疾病研究中的应用

代谢组学正成为生物医学研究领域的新热点。各种代谢组学分析技术各有优缺点，配有低温探头的核磁共振、混合型串联傅里叶变换质谱以及多种联用技术将成为代谢组学研究的关键技术。目前，大量多维代谢组学数据的分析方法和专用软件急待开发完善。代谢组学在肿瘤，老年痴呆症、心血管疾病、肝肾脏类疾病等研究中的应用取得一定进展，疾病代谢组学具有良好的发展前景。     

基于质谱技术的代谢组学研究及其在中国的发展

代谢组学是关于生物系统代谢物组成及变化规律的科学,是系统生物学的重要组成部分.质谱技术是目前代谢组学研究中最主要的分析手段之一,广泛应用于代谢组学各个领域.本文阐述了基于质谱技术的代谢组学方法及其应用,重点介绍和评论了近年来我国在该领域取得的进步和成果,并对基于质谱技术的代谢组学研究目前存在的问题及未来的发展进行了分析与展望.     

代谢组学法研究三聚氰胺对儿童尿液代谢的影响

运用代谢组学方法研究了三聚氰胺对儿童尿液代谢的影响.通过超高效液相色谱-飞行时间质谱(UPLC/TOF-MS)法分析儿童尿样的代谢指纹图谱,质谱数据采用MarkerLynx软件处理,然后使用主成分分析和偏最小二乘判别分析法分析病例组和正常对照组之间的代谢物谱差异,并通过变量重要性投影(VIP)选取潜在的生物标志物,结合质谱同位素分析和数据库检索对潜在的生物标志物进行鉴定.结果表明,三聚氰胺通过肾结石导致的物理性损伤干扰了柠檬酸代谢.代谢组学法能够应用于三聚氰胺导致的代谢异常的研究及三聚氰胺导致肾损伤的无创检测.     

脂组学及其研究进展

脂质的生物功能具有多样性,其代谢与多种疾病的发生、发展密切相关.脂质的分析量化对研究疾病发生机理和诊断治疗,以及医药研发有非常重要的生物学意义.分析技术的快速发展,特别是质谱及其联用技术的运用,促使脂质分析不断完善.脂组学就是对生物样本中脂质的整体分析,是代谢组学的重要组成部分,能够促进代谢组学的发展.本文就脂质的生物功能、脂质分析以及脂组学的研究现状作简要评述.     

应用于天然产物研究与开发的代谢组学数据库

代谢组学在对生物体系的小分子代谢物进行定性和定量研究中产生了大量的数据,成为天然产物研究和开发的重要工具。一系列公开、方便且注释良好的代谢组学数据库和全功能软件的开发促进了天然产物大数据的集成、处理和解释,这不仅有利于数据的存储、管理和分析,还可作为信息共享联动的平台。基于质谱的代谢组学数据库的建立对于天然产物已知成分筛选、未知成分鉴定起着十分重要的作用。对国内外常用的代谢组学数据库及其特征进行综述,强调它们的内容、功能和应用,为数据库的选择提供理论依据,并展望代谢组学数据库的开发趋势。     

基于气相色谱－质谱的尿液代谢组学技术结合化学计量学用于戊二酸血症Ⅰ型早期检测研究

采用气相色谱－质谱联用技术结合化学计量学,针对高维小样本的疾病代谢组学图谱建立高性能的戊二酸血症Ⅰ型（GA－Ⅰ）早期检测模型。基于偏最小二乘判别分析（PLS－DA）的共线性处理和数据解释优势,自助抽样法（Bootstrap）通过数据扰动方式集成多个模型的变量选择能力,挑选出能够持续被筛选的变量实现稳健特征筛选（BS－PLSDA）。对于GA－Ⅰ的尿液代谢组学图谱,在两种逐步增大训练集之间样本差异的比例划分（7∶3和6∶4）下,载荷（LW）、变量投影重要性（VIP）、显著性多元相关（sMC）3种信息向量对应的BS－PLSDA均优于其单独PLS－DA建模的特征变量筛选稳健性。在样本划分比例为7∶3时,BS－VIP－PLSDA的Kuncheva指数高达0.807 5。筛选出的稳健特征变量与文献报道的诊断指标一致,不仅真正解释组别间的差异与GA－Ⅰ的代谢机理密切相关,且BS－LW－PLSDA、BS－VIP－PLSDA和BS－sMC－PLSDA展示了良好的预测性能,受试者工作特征曲线下面积均值分别为0.773 9、0.854 8和0.847 1,马修斯相关系数均值分别为0.671 9、0.783 8和0.801 3。与支持向量机递归特征消除法（SVM－RFE）相比,在采用相同的集成特征选择策略下,尽管非线性径向基核函数对应的BS－RBF－SVMRFE可获得高建模性能,但数据解释能力较低。该研究提出的BS－PLSDA可兼顾建模性能和模型解释能力,符合实际临床需求,对GA－Ⅰ早期检测、辅助诊断和疾病机理研究具有很好的指导意义。     

核磁共振代谢组学数据的尺度归一化新方法

在核磁共振代谢组学数据预处理中,尺度归一化主要目的是提高特征代谢物信息的权重,减小噪声及无关代谢物信息的影响,从而降低后续模式识别分析的难度. 本文提出一种新的尺度归一化方法,该方法不强调各变量在尺度上的归一,而是在原始数据的基础上,通过提高那些稳定性高、且在不同类别样本中具有显著差异性的变量的权重,以增强与特征代谢物相关的信息. 文中分别采用模拟数据和真实代谢组学数据对新归一化方法的性能进行评估,并与单位方差法(Unit Variance)、变量稳定性(Variable Stability)和尺度缩放法(Level Scaling)等常用的尺度归一化方法做比较. 研究结果表明：新归一化方法能够提高多变量统计模型的预测能力,较好地保留核磁共振谱的分子信息,有助于特征代谢物的识别,并使后续的数据分析结果具有更好的可解释性.     

色谱-质谱联用技术在代谢组学中的应用

代谢组学是对生物体受外部刺激所产生的小分子代谢产物的变化或其随时间的变化进行研究的一门学科,以实现对体液、细胞以及组织提取物等复杂的生物样本中所有代谢产物的定性和定量分析为研究目标。色谱-质谱联用技术在代谢组学的研究中已显示出极大的发展潜力。本文主要综述近年来代谢组学研究中涉及的色谱-质谱联用技术及其数据处理方法,重点介绍各种分离技术的特点及其在应用中的关键问题,并对其在代谢组学应用中的未来发展给予展望。     

一种基于液相色谱-质谱技术进行血清代谢组学研究的方法：从代谢指纹到潜在标志物

本文描述了一种基于液相色谱-质谱技术（LC-MS）的代谢组学发现疾病潜在标志物的方法.该方法利用LC-MS获得代谢指纹图谱,并通过多种统计分析方法对产生的海量数据进行分析,最终筛选出潜在标志物.数据分析过程包括：通过归一化、修正80%规则、数据集分割和数据缩放等方法对数据集进行预处理 通过正交校正的偏最小二乘（OPLS）模式识别方法对样品进行分型 根据模型的变量重要性因子（VIP值）、非参数检验结果和z值筛选潜在标志物.以宫颈癌血清样本为例,应用上述方法,15个变量被确认为潜在标志物,操作者接受曲线（ROC）下的面积为0.667～0.956.经过相关性分析和结构鉴定,发现这15个变量来自9个化合物.其中7个化合物被鉴定为色氨酸、硬脂酸、花生四烯酸、溶血磷脂酰胆碱（0：0/16：0,16：0/0：0,18：1/0：0和18：0/0：0）,说明在宫颈癌中花生四烯酸和溶血磷脂酰胆碱的代谢发生异常.     

化学计量学方法用于蛋白质组学质谱数据的特征筛选

提出了一种基于偏最小二乘判别分析和F-score的特征筛选方法,并将其用于蛋白质组学质谱数据分析。方法主要包含3个步骤:(1)用LIMPIC算法对原始数据进行预处理;(2)计算每个变量的F-score值并将所有变量按F-score值降底的顺序排列;(3)采用偏最小二乘判别分析交互检验按前向选择法选择最佳变量子集。用本方法对一组卵巢癌数据进行分析,最终从原始的15154个质荷比变量中选择了12个特征变量作为潜在生物标记物,它们在训练集上交叉检验的特异性和灵敏度分别为98.36%和98.15%,在独立测试集上的特异性和灵敏度分别为96.67%和100%。用筛选出的变量作PCA所得的结果显示这些变量能够较好地将样本分类,说明能够反映出样本的类别信息。所提出的方法可用于蛋白质组学质谱数据的特征筛选及样本分类。     

Metabolite profiling and beyond: approaches for the rapid processing and annotation of human blood serum mass spectrometry data

In this paper, we describe data processing and metabolite identification approaches which lead to a rapid and semi-automated interpretation of metabolomics experiments. Data from metabolite fingerprinting using LC-ESI-Q-TOF/MS were processed with several open-source software packages, including XCMS and CAMERA to detect features and group features into compound spectra. Next, we describe the automatic scheduling of tandem mass spectrometry (MS) acquisitions to acquire a large number of MS/MS spectra, and the subsequent processing and computer-assisted annotation towards identification using the R packages MetShot, Rdisop, and the MetFusion application. We also implement a simple retention time prediction model using predicted lipophilicity logD, which predicts retention times within 42 s (6 min gradient) for most compounds in our setup. We putatively identified 44 common metabolites including several amino acids and phospholipids at metabolomics standards initiative (MSI) levels two and three and confirmed the majority of them by comparison with authentic standards at MSI level one. To aid both data integration within and data sharing between laboratories, we integrated data from two labs and mapped retention times between the chromatographic systems. Despite the different MS instrumentation and different chromatographic gradient programs, the mapped retention times agree within 26 s (20 min gradient) for 90 % of the mapped features.

基于质谱分析的代谢组学研究进展

质谱分析技术是代谢组学研究的重要技术之一。该文通过近5年来的文献分析,对基于质谱分析的代谢组学研究方法的新进展,包括样品前处理方法、分析检测方法、数据处理方法等,以及近年来代谢组学在疾病诊断、药物研发、营养学、毒理学、运动医学等领域的应用进展,进行了较全面的综述,并对未来的发展趋势进行了展望。     

Feature selection of gas chromatography/mass spectrometry chemical profiles of basil plants using a bootstrapped fuzzy rule-building expert system

A bootstrapped fuzzy rule-building expert system (FuRES) and a bootstrapped t-statistical weight feature selection method were individually used to select informative features from gas chromatography/mass spectrometry (GC/MS) chemical profiles of basil plants cultivated by organic and conventional farming practices. Feature subsets were selected from two-way GC/MS data objects, total ion chromatograms, and total mass spectra, separately. Four economic classifiers based on the bootstrapped FuRES approach, i.e., fuzzy optimal associative memory (e-FOAM), e-FuRES, partial least-squares–discriminant analysis (e-PLS-DA), and soft independent modeling by class analogy (e-SIMCA), and four economic classifiers based on the bootstrapped t-weight approach, i.e., e-PLS-DA-t, e-FOAM-t, e-FuRES-t, and e-SIMCA-t, were constructed thereafter to be compared with full-size classifiers obtained from the entire GC/MS data objects (i.e., FOAM, FuRES, PLS-DA, and SIMCA). By using three features selected from two-way data objects, the average classification rates with e-FOAM, e-FuRES, e-PLS-DA, and e-SIMCA were 95.3?±?0.5 %, 100 %, 100 %, and 91.8?±?0.2 %, respectively. The established economic classifiers were used to classify a new validation set collected 2.5 months later with no parametric change to experimental procedure. Classification rates with e-FOAM, e-FuRES, e-PLS-DA, and e-SIMCA were 96.7 %, 100 %, 100 %, and 96.7 %, respectively. Characteristic components in basil extracts corresponding to highest-ranked useful features were putatively identified. The feature subset may prove valuable as a rapid approach for organic basil authentication.     

Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC–MS-Based Untargeted Metabolomics

Untargeted metabolomics based on liquid chromatography coupled with mass spectrometry (LC–MS) can detect thousands of features in samples and produce highly complex datasets. The accurate extraction of meaningful features and the building of discriminant models are two crucial steps in the data analysis pipeline of untargeted metabolomics. In this study, pure ion chromatograms were extracted from a liquor dataset and left-sided colon cancer (LCC) dataset by K-means-clustering-based Pure Ion Chromatogram extraction method version 2.0 (KPIC2). Then, the nonlinear low-dimensional embedding by uniform manifold approximation and projection (UMAP) showed the separation of samples from different groups in reduced dimensions. The discriminant models were established by extreme gradient boosting (XGBoost) based on the features extracted by KPIC2. Results showed that features extracted by KPIC2 achieved 100% classification accuracy on the test sets of the liquor dataset and the LCC dataset, which demonstrated the rationality of the XGBoost model based on KPIC2 compared with the results of XCMS (92% and 96% for liquor and LCC datasets respectively). Finally, XGBoost can achieve better performance than the linear method and traditional nonlinear modeling methods on these datasets. UMAP and XGBoost are integrated into KPIC2 package to extend its performance in complex situations, which are not only able to effectively process nonlinear dataset but also can greatly improve the accuracy of data analysis in non-target metabolomics.     

Volatile compounds profiling by using proton transfer reaction‐time of flight‐mass spectrometry (PTR‐ToF‐MS). The case study of dark chocolates organoleptic differences

Direct‐injection mass spectrometry (DIMS) techniques have evolved into powerful methods to analyse volatile organic compounds (VOCs) without the need of chromatographic separation. Combined to chemometrics, they have been used in many domains to solve sample categorization issues based on volatilome determination. In this paper, different DIMS methods that have largely outperformed conventional electronic noses (e‐noses) in classification tasks are briefly reviewed, with an emphasis on food‐related applications. A particular attention is paid to proton transfer reaction mass spectrometry (PTR‐MS), and many results obtained using the powerful PTR‐time of flight‐MS (PTR‐ToF‐MS) instrument are reviewed. Data analysis and feature selection issues are also summarized and discussed. As a case study, a challenging problem of classification of dark chocolates that has been previously assessed by sensory evaluation in four distinct categories is presented. The VOC profiles of a set of 206 chocolate samples classified in the four sensory categories were analysed by PTR‐ToF‐MS. A supervised multivariate data analysis based on partial least squares regression‐discriminant analysis allowed the construction of a classification model that showed excellent prediction capability: 97% of a test set of 62 samples were correctly predicted in the sensory categories. Tentative identification of ions aided characterisation of chocolate classes. Variable selection using dedicated methods pinpointed some volatile compounds important for the discrimination of the chocolates. Among them, the CovSel method was used for the first time on PTR‐MS data resulting in a selection of 10 features that allowed a good prediction to be achieved. Finally, challenges and future needs in the field are discussed.     

基于液相色谱-质谱联用技术的代谢组学分析方法研究进展

代谢组学是研究小分子代谢物的有用工具,能够直接反映生命体终端和表型信息,在精准医学和转化医学中发挥着重要作用。色谱-质谱联用技术具有灵敏度高、选择性好、动态范围宽、信息丰富等优点,已成为代谢组学研究的主要技术平台。代谢组学分析方法的创新与进展是代谢组学在各领域广泛应用的重要前提。该文综述了近5年来基于液相色谱-质谱联用技术的代谢组学分析方法取得的成果,并对目前存在的问题及发展前景给予展望。综述引用文献81篇。     

Analytical techniques for single-cell metabolomics: state of the art and trends

Single-cell metabolomics is an emerging field that addresses fundamental biological questions and allows one to observe metabolic phenomena in heterogeneous populations of single cells. In this review, we assess the suitability of different detection techniques and present considerations on sample preparation for single-cell metabolomics. Although targeted analysis of single cells can readily be conducted using fluorescent probes and optical instruments (microscopes, fluorescence detectors), a comprehensive metabolomic approach requires a powerful label-free method, such as mass spectrometry (MS). Mass-spectrometric techniques applied to study small molecules in single cells include electrospray MS, matrix-assisted laser desorption/ionization MS, and secondary ion MS. Sample preparation is an important aspect to be taken into account during further development of methods for single-cell metabolomics.     

CE–MS for anionic metabolic profiling: An overview of methodological developments

The efficient profiling of highly polar and charged metabolites in biological samples remains a huge analytical challenge in metabolomics. Over the last decade, new analytical techniques have been developed for the selective and sensitive analysis of polar ionogenic compounds in various matrices. Still, the analysis of such compounds, notably for acidic ionogenic metabolites, remains a challenging endeavor, even more when the available sample size becomes an issue for the total analytical workflow. In this paper, we give an overview of the possibilities of capillary electrophoresis‐mass spectrometry (CE–MS) for anionic metabolic profiling by focusing on main methodological developments. Attention is paid to the development of improved separation conditions and new interfacing designs in CE–MS for anionic metabolic profiling. A complete overview of all CE–MS‐based methods developed for this purpose is provided in table format (Table 1) which includes information on sample type, separation conditions, mass analyzer and limits of detection (LODs). Selected applications are discussed to show the utility of CE–MS for anionic metabolic profiling, especially for small‐volume biological samples. On the basis of the examination of the reported literature in this specific field, we conclude that there is still room for the design of a highly sensitive and reliable CE–MS method for anionic metabolic profiling. A rigorous validation and the availability of standard operating procedures would be highly favorable in order to make CE–MS an alternative, viable analytical technique for metabolomics.     

New tools and resources in metabolomics: 2016–2017

Rapid advances in mass spectrometry (MS) and nuclear magnetic resonance (NMR)‐based platforms for metabolomics have led to an upsurge of data every single year. Newer high‐throughput platforms, hyphenated technologies, miniaturization, and tool kits in data acquisition efforts in metabolomics have led to additional challenges in metabolomics data pre‐processing, analysis, interpretation, and integration. Thanks to the informatics, statistics, and computational community, new resources continue to develop for metabolomics researchers. The purpose of this review is to provide a summary of the metabolomics tools, software, and databases that were developed or improved during 2016–2017, thus, enabling readers, developers, and researchers access to a succinct but thorough list of resources for further improvisation, implementation, and application in due course of time.     

A tutorial review: Metabolomics and partial least squares-discriminant analysis – a marriage of convenience or a shotgun wedding

The predominance of partial least squares-discriminant analysis (PLS-DA) used to analyze metabolomics datasets (indeed, it is the most well-known tool to perform classification and regression in metabolomics), can be said to have led to the point that not all researchers are fully aware of alternative multivariate classification algorithms. This may in part be due to the widespread availability of PLS-DA in most of the well-known statistical software packages, where its implementation is very easy if the default settings are used. In addition, one of the perceived advantages of PLS-DA is that it has the ability to analyze highly collinear and noisy data. Furthermore, the calibration model is known to provide a variety of useful statistics, such as prediction accuracy as well as scores and loadings plots. However, this method may provide misleading results, largely due to a lack of suitable statistical validation, when used by non-experts who are not aware of its potential limitations when used in conjunction with metabolomics. This tutorial review aims to provide an introductory overview to several straightforward statistical methods such as principal component-discriminant function analysis (PC-DFA), support vector machines (SVM) and random forests (RF), which could very easily be used either to augment PLS or as alternative supervised learning methods to PLS-DA. These methods can be said to be particularly appropriate for the analysis of large, highly-complex data sets which are common output(s) in metabolomics studies where the numbers of variables often far exceed the number of samples. In addition, these alternative techniques may be useful tools for generating parsimonious models through feature selection and data reduction, as well as providing more propitious results. We sincerely hope that the general reader is left with little doubt that there are several promising and readily available alternatives to PLS-DA, to analyze large and highly complex data sets.