An improved pseudotargeted metabolomics approach using multiple ion monitoring with time-staggered ion lists based on ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry

Pseudotargeted metabolomics is a novel strategy integrating the advantages of both untargeted and targeted methods. The conventional pseudotargeted metabolomics required two MS instruments, i.e., ultra-high performance liquid chromatography/quadrupole-time- of-flight mass spectrometry (UHPLC/Q-TOF MS) and UHPLC/triple quadrupole mass spectrometry (UHPLC/QQQ-MS), which makes method transformation inevitable. Furthermore, the picking of ion pairs from thousands of candidates and the swapping of the data between two instruments are the most labor-intensive steps, which greatly limit its application in metabolomic analysis. In the present study, we proposed an improved pseudotargeted metabolomics method that could be achieved on an UHPLC/Q-TOF/MS instrument operated in the multiple ion monitoring (MIM) mode with time-staggered ion lists (tsMIM). Full scan-based untargeted analysis was applied to extract the target ions. After peak alignment and ion fusion, a stepwise ion picking procedure was used to generate the ion lists for subsequent single MIM and tsMIM. The UHPLC/Q-TOF tsMIM MS-based pseudotargeted approach exhibited better repeatability and a wider linear range than the UHPLC/Q-TOF MS-based untargeted metabolomics method. Compared to the single MIM mode, the tsMIM significantly increased the coverage of the metabolites detected. The newly developed method was successfully applied to discover plasma biomarkers for alcohol-induced liver injury in mice, which indicated its practicability and great potential in future metabolomics studies.     

Stepped collisional energy MSAll: an analytical approach for optimal MS/MS acquisition of complex mixture with diverse physicochemical properties

The analysis of complex mixtures is becoming increasingly important in various fields, such as nutrition, medicinal plants and metabolomics. The components contained in such complex mixtures are always characterized with diverse physiochemical properties that pose a major challenge during the optimization of various parameters using liquid chromatography‐mass spectrometer (LC‐MS). The parameter ‘CE energy’ that is normally set at a fixed value with a moderate range of CE spread during data‐dependent acquisition (DDA) analysis, a prevalent approach for untargeted identification, often fails to generate sufficient MS/MS fragment ions for untargeted identification of components from complex mixtures. Here we developed a simple and generally applicable acquisition method named stepped MS^All (sMS^All) in this study, aiming to obtain optimal MS/MS spectra for identification of chemically diverse compounds from complex mixtures. sMS^All collects serial MS^All scans acquired at low CE to gradually ramped‐up high CE values in a cycle that conventional DDA scans cannot afford. The resultant MS/MS spectra of each compound were compared and evaluated among serial MS^All scans, and the optimal spectra were used for identification. An untargeted data analysis strategy was then employed to analyze these optimal MS/MS spectra by searching common diagnostic ions and connecting the diagnostic ion families into a network via bridging components. This sMS^All‐based route enables identification of 71 natural products from a herbal preparation, whereas only 53 out of 71 compounds were identified using the classical DDA approach. Therefore, the sMS^All‐based approach is expected to find its wide applications for characterization of vastly diverse compounds with no priori knowledge from various complex mixtures. Copyright © 2016 John Wiley & Sons, Ltd.     

Quantitative and qualitative precision improvements by effective mobility-scale data transformation in capillary electrophoresis analysis

By transforming the time-based x-axis of electropherograms in capillary zone electrophoresis (CZE) into the corresponding effective mobility-scale, we propose a simple and robust data representation for a better qualitative and quantitative capillary electrophoresis (CE) analysis. The time scale of the raw electrophoretic data (detection signal versus time) is transformed into an effective electrophoretic mobility scale (mu eff-scale) with account of the electroosmotic flow (EOF) peak or of an internal standard of known effective mobility. With the new scaling (detection signals versus effective mobility), the obtained electropherograms are more representative of the velocity-based electrophoretic separation and the comparison of complete electropherograms is directly possible. This is of importance when tracking peaks in real samples where alteration in EOF stability can occur or when comparing electrophoretic runs from different experimental setups (independence in column length and voltage). Beside the qualitative possibilities, a quantitative improvement is achieved in the mu eff-scale with significant better peak area reproducibility and equal to more precision in quantitative analysis than with the primary time-scale integration.     

Metabolic fingerprinting of Schistosoma mansoni infection in mice urine with capillary electrophoresis

Schistosoma mansoni infection in mice has been fingerprinted using CE to study the capabilities of this technique as a diagnostic tool for this parasitic disease. Two modes of separation were used in generating the electrophoretic data, with each untreated urine sample the following methods were applied: (i) a fused-silica capillary, operating with an applied potential of 18 kV, in micellar EKC (MEKC) and (ii) a polyacrylamide-coated capillary, operating with an applied potential of -20 kV under zonal CZE conditions. By combining normal and reverse polarities in the data treatment we have extracted more information from the samples, which is a better approach for CE metabolomics. The traditional problems associated with variability in electrophoretic peak migration times for analytes were countered by using a dynamic programming algorithm for the electropherograms alignment. Principal component analyses of these aligned electropherograms and partial least square discriminant analysis (PLS-DA) data are shown to provide a valuable means of rapid and sample classification. This approach may become an important tool for the identification of biomarkers, diagnosis and disease surveillance.     

CE‐MS for metabolomics: Developments and applications in the period 2016–2018

In the field of metabolomics, CE‐MS is now recognized as a strong analytical technique for the analysis of (highly) polar and charged metabolites in a wide range of biological samples. Over the past few years, significant attention has been paid to the design and improvement of CE‐MS approaches for (large‐scale) metabolic profiling studies and for establishing protocols in order to further expand the role of CE‐MS in metabolomics. In this paper, which is a follow‐up of a previous review paper covering the years 2014–2016 (Electrophoresis 2017, 38, 190–202), main advances in CE‐MS approaches for metabolomics studies are outlined covering the literature from July 2016 to June 2018. Aspects like developments in interfacing designs and data analysis tools for improving the performance of CE‐MS for metabolomics are discussed. Representative examples highlight the utility of CE‐MS in the fields of biomedical, clinical, microbial, and plant metabolomics. A complete overview of recent CE‐MS‐based metabolomics studies is given in a table, which provides information on sample type and pretreatment, capillary coatings and MS detection mode. Finally, some general conclusions and perspectives are given.     

Semi‐quantitative and structural metabolic phenotyping by direct infusion ion trap mass spectrometry and its application in genetical metabolomics

The identification of quantitative trait loci (QTL) for plant metabolites requires the quantitation of these metabolites across a large range of progeny. We developed a rapid metabolic profiling method using both untargeted and targeted direct infusion tandem mass spectrometry (DIMSMS) with a linear ion trap mass spectrometer yielding sufficient precision and accuracy for the quantification of a large number of metabolites in a high‐throughput environment. The untargeted DIMSMS method uses top‐down data‐dependent fragmentation yielding MS² and MS³ spectra. We have developed software tools to assess the structural homogeneity of the MS² and MS³ spectra hence their utility for phenotyping and genetical metabolomics. In addition we used a targeted DIMS(MS) method for rapid quantitation of specific compounds. This method was compared with targeted LC/MS/MS methods for these compounds. The DIMSMS methods showed sufficient precision and accuracy for QTL discovery. We phenotyped 200 individual Lolium perenne genotypes from a mapping population harvested in two consecutive years. Computational and statistical analyses identified 246 nominal m/z bins with sufficient precision and homogeneity for QTL discovery. Comparison of the data for specific metabolites obtained by DIMSMS with the results from targeted LC/MS/MS analysis showed that quantitation by this metabolic profiling method is reasonably accurate. Of the top 100 MS¹ bins, 22 ions gave one or more reproducible QTL across the 2 years. Copyright © 2009 John Wiley & Sons, Ltd.     

MASSyPup — an ‘Out of the Box’ solution for the analysis of mass spectrometry data

Mass spectrometry has evolved to a key technology in the areas of metabolomics and proteomics. Centralized facilities generate vast amount of data, which frequently need to be processed off‐site. Therefore, the distribution of data and software, as well as the training of personnel in the analysis of mass spectrometry data, becomes increasingly important. Thus, we created a comprehensive collection of mass spectrometry software which can be run directly from different media such as DVD or USB without local installation. MASSyPup is based on a Linux Live distribution and was complemented with programs for conversion, visualization and analysis of mass spectrometry (MS) data. A special emphasis was put on protein analysis and proteomics, encompassing the measurement of complete proteins, the identification of proteins based on Peptide Mass Fingerprints (PMF) or LC‐MS/MS data, and de novo sequencing. Another focus was directed to the study of metabolites and metabolomics, covering the detection, identification and quantification of compounds, as well as subsequent statistical analyses. Additionally, we added software for Mass Spectrometry Imaging (MSI), including hardware support for self‐made MSI devices. MASSyPup represents a ‘ready to work’ system for teaching or MS data analysis, but also represents an ideal platform for the distribution of MS data and the development of related software. The current Live DVD version can be downloaded free of charge from http://www.bioprocess.org/massypup . Copyright © 2014 John Wiley & Sons, Ltd.     

Comprehensive Analysis of LC/MS Data Using Pseudocolor Plots

We have developed new applications of the pseudocolor plot for the analysis of LC/MS data. These applications include spectral averaging, analysis of variance, differential comparison of spectra, and qualitative filtering by compound class. These applications have been motivated by the need to better understand LC/MS data generated from analysis of human biofluids. The examples presented use data generated to profile steroid hormones in urine extracts from a Cushing’s disease patient relative to a healthy control, but are general to any discovery-based scanning mass spectrometry technique. In addition to new visualization techniques, we introduce a new metric of variance: the relative maximum difference from the mean. We also introduce the concept of substructure-dependent analysis of steroid hormones using precursor ion scans. These new analytical techniques provide an alternative approach to traditional untargeted metabolomics workflow. We present an approach to discovery using MS that essentially eliminates alignment or preprocessing of spectra. Moreover, we demonstrate the concept that untargeted metabolomics can be achieved using low mass resolution instrumentation.      

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.     

Increase the accessibility and scale of targeted metabolomics: Construction of a human urinary metabolome-wide multiple reaction monitoring library using directly-coupled reversed-phase and hydrophilic interaction chromatography

Multiple reaction monitoring (MRM) is wildly employed to research drug absorption, distribution, metabolism, excretion and pharmacokinetics in pharmaceutical and clinical laboratories. Recently, scientists in these areas have shown great interest in utilization of metabolomics to evaluate drug efficacy and toxicity. MRM-based targeted metabolomics is intrinsically more sensitive and selective than MS based untargeted metabolomics in complex biological samples. MRM also minimizes data complexity for fast and focused analysis of core metabolites. Nevertheless, to mitigate the intrinsic targeted nature of MRM and promote it as a discovery toolbox for metabolomics, larger scale MRM assays providing more comprehensive biological information are highly desirable. Here, we employed data-dependent and data-independent strategies to perform extensive MS/MS mapping of human urinary metabolome with the assistance of a directly-coupled reversed-phase liquid chromatography and hydrophilic interaction chromatography (RPLC-HILIC) for simultaneous profiling of hydrophilic and hydrophobic metabolites. RPLC-HILIC enables to save time, limit sample consumption and facilitate data interpretation by removing data redundancy occurring between separate RPLC and HILIC methods. Major product ions in the raw MS/MS spectra were used to build a human urinary metabolome-wide MRM library which contains 749 refined MRM tags in negative ion mode with 198 of them being unambiguously or tentatively assigned for particular metabolites. The library relieves researchers from the most time-consuming setup of massive MRM transitions and making an important step toward large-scale targeted urinary metabolomics.     

MS-IAS:集成的质谱代谢组学数据分析系统

针对代谢组学研究中的数据处理问题,本研究建立了基于质谱的数据分析系统MS-IAS(Mass spectrometry based integrated analysis system).此系统集成了特征选择、聚类、分类等多种方法,用以处理质谱数据,具有多种统计分析方法能对所选的特征变量进行比较,以发现与所研究问题相关的潜在生物标志物.MS-IAS支持数据与多种算法结果可图形化显示,有助于对数据的解释与分析.以肝病患者的质谱代谢组数据为例,展示MS-IAS的功能,两种特征选择算法从数据集中筛选出了40个对肝病具有区分能力的特征变量,展示了MS-IAS成为代谢组学研究中的通用质谱数据分析系统的潜力.     

Characterising and correcting batch variation in an automated direct infusion mass spectrometry (DIMS) metabolomics workflow

Direct infusion mass spectrometry (DIMS)-based untargeted metabolomics measures many hundreds of metabolites in a single experiment. While every effort is made to reduce within-experiment analytical variation in untargeted metabolomics, unavoidable sources of measurement error are introduced. This is particularly true for large-scale multi-batch experiments, necessitating the development of robust workflows that minimise batch-to-batch variation. Here, we conducted a purpose-designed, eight-batch DIMS metabolomics study using nanoelectrospray (nESI) Fourier transform ion cyclotron resonance mass spectrometric analyses of mammalian heart extracts. First, we characterised the intrinsic analytical variation of this approach to determine whether our existing workflows are fit for purpose when applied to a multi-batch investigation. Batch-to-batch variation was readily observed across the 7-day experiment, both in terms of its absolute measurement using quality control (QC) and biological replicate samples, as well as its adverse impact on our ability to discover significant metabolic information within the data. Subsequently, we developed and implemented a computational workflow that includes total-ion-current filtering, QC-robust spline batch correction and spectral cleaning, and provide conclusive evidence that this workflow reduces analytical variation and increases the proportion of significant peaks. We report an overall analytical precision of 15.9 %, measured as the median relative standard deviation (RSD) for the technical replicates of the biological samples, across eight batches and 7 days of measurements. When compared against the FDA guidelines for biomarker studies, which specify an RSD of <20 % as an acceptable level of precision, we conclude that our new workflows are fit for purpose for large-scale, high-throughput nESI DIMS metabolomics studies.     

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.

Assessing the suitability of capillary electrophoresis‐mass spectrometry for biomarker discovery in plasma‐based metabolomics

The actual utility of capillary electrophoresis‐mass spectrometry (CE‐MS) for biomarker discovery using metabolomics still needs to be assessed. Therefore, a simulated comparative metabolic profiling study for biomarker discovery by CE‐MS was performed, using pooled human plasma samples with spiked biomarkers. Two studies have been carried out in this work. Focus of study I was on comparing two sets of plasma samples, in which one set (class I) was spiked with five isotope‐labeled compounds, whereas another set (class II) was spiked with six different isotope‐labeled compounds. In study II, focus was also on comparing two sets of plasma samples, however, the isotope‐labeled compounds were spiked to both class I and class II samples but with concentrations which differ by a factor two between both classes (with one compound absent in each class). The aim was to determine whether CEMS‐based metabolomics could reveal the spiked biomarkers as the main classifiers, applying two different data analysis software tools (MetaboAnalyst and Matlab). Unsupervised analysis of the recorded metabolic profiles revealed a clear distinction between class I and class II plasma samples in both studies. This classification was mainly attributed to the spiked isotope‐labeled compounds, thereby emphasizing the utility of CE‐MS for biomarker discovery.     

基于UPLC-Q-Orbitrap MS/MS技术研究酸枣仁发酵过程中的化学成分转化

利用超高效液相色谱-四极杆-静电场轨道阱串联质谱（UPLC-Q-Orbitrap MS/MS）研究了酸枣仁经茯苓发酵前后的主要化学成分变化，初步探讨了茯苓对酸枣仁发酵的转化规律.鉴定了发酵产物中24种化学成分，整合多元统计方法对酸枣仁发酵0，7和14 d后的差异性化学成分进行研究，采用正交偏最小二乘分析找到7个差异成分.采用t-检验对24种化学成分的相对含量进行分析；并分析了酸枣仁中黄酮苷、皂苷类成分的代谢反应途径.通过茯苓发酵，酸枣仁中化学成分主要发生了脱酰基和脱糖基等水解反应.为酸枣仁发酵产物的化学成分检测提供了快速、直观且准确的方法，为酸枣仁-茯苓共发酵产物的深入开发与利用提供了科学依据.     

Chemometrics for the Identification of Nitrogen and Acid Compounds in Milk-Whey as By-Products from Crescenza and Grana Padano Type Cheese-Making

Proteomics and metabolomics are analytic tools used in combination with bioinformatics to study proteins and metabolites which contribute to describing complex biological systems. The growing interest in research concerning the resolution of these systems has stimulated the development of sophisticated procedures and new applications. This paper introduces the evolution of statistical techniques for the treatment of data, suggesting the possibility to successfully characterize the milk-whey syneresis process by applying two-dimensional correlation analysis (2DCOR) to a series of CE electropherograms referring to milk-whey samples collected during cheese manufacturing. Two cheese-making processes to produce hard cheese (Grana type) and fresh cheese (Crescenza) were taken as models. The applied chemometric tools were shown to be useful for the treatment of data acquired in a systematically perturbed chemical system as a function of time.     

Capillary electrophoresis and ultra-high-performance liquid chromatography methods in clinical monitoring of creatinine in human urine: A comparative study

Creatinine is an important diagnostic marker and is also used as a standardization tool for the quantitative evaluation of exogenous/endogenous substances in urine. This study aimed at evaluating and comparing three analytical approaches, based on hyphenations of different separation [two-dimensional capillary isotachophoresis (CITP–CITP), capillary zone electrophoresis (CZE), ultra-high-performance liquid chromatography (UHPLC)] and detection [conductivity (CD), ultraviolet (UV), tandem mass spectrometry (MS/MS)] techniques, for their ability to provide reliable clinical data along with their suitability for the routine clinical use (cost, simplicity, sample throughput). The developed UHPLC–MS/MS, CITP–CITP–CD, and CZE–UV methods were characterized by favorable performance parameters, such as linearity (r ˃ 0.99), precision (relative standard deviation, 0.22–2.97% for the creatinine position in analytical profiles), and recovery (87.1–115.1%). Clinical data, obtained from the analysis of 24 human urine samples by a reference enzymatic method, were comparable with those obtained by the tested methods (Passing–Bablok regression and Bland–Altman analysis), approving their usefulness for the routine clinical use. In this context, the UHPLC–MS/MS method provides benefits of enhanced orthogonality/accuracy and high sample throughput (threefold shorter total analysis times than the CE methods), whereas advantages of the CE methods for routine labs are simplicity and low cost of both the instrumentation and measurements.     

Analysis of substances to be used as internal standards in MEKC

The use of internal standards (ISs) improves the quantitative performance of CE. However, ISs chosen for use in CZE often cannot be used for micellar EKC (MEKC). Therefore 22 substances were investigated as potential ISs in MEKC. These substances were selected based upon a literature search. The substances were investigated using a method similar to the standard operating conditions for MEKC as recommended by S. Terabe. Furthermore, the migration time and the corrected migration time (k(S)) were determined for each substance to establish the migration position relative to other peaks in the electropherograms. A combination of eight substances, selected according to the obtained results (t(m) = 4 up to 12 min), was tested for practical benefit and applicability. The peak area precision was in the range of 0.8 and 1.2% (n = 60), and the peaks were well shaped for all the investigated substances. The selected substances covered a wide migration time window and therefore they can be regarded as suitable for future analysis at any required migration position.     

Pattern recognition in capillary electrophoresis data using dynamic programming in the wavelet domain

A novel approach for CE data analysis based on pattern recognition techniques in the wavelet domain is presented. Low-resolution, denoised electropherograms are obtained by applying several preprocessing algorithms including denoising, baseline correction, and detection of the region of interest in the wavelet domain. The resultant signals are mapped into character sequences using first derivative information and multilevel peak height quantization. Next, a local alignment algorithm is applied on the coded sequences for peak pattern recognition. We also propose 2-D and 3-D representations of the found patterns for fast visual evaluation of the variability of chemical substances concentration in the analyzed samples. The proposed approach is tested on the analysis of intracerebral microdialysate data obtained by CE and LIF detection, achieving a correct detection rate of about 85% with a processing time of less than 0.3 s per 25,000-point electropherogram. Using a local alignment algorithm on low-resolution denoised electropherograms might have a great impact on high-throughput CE since the proposed methodology will substitute automatic fast pattern recognition analysis for slow, human based time-consuming visual pattern recognition methods.