bims-metlip Biomed News
on Methods and protocols in metabolomics and lipidomics
Issue of 2020‒01‒19
forty-two papers selected by
Sofia Costa
Cold Spring Harbor Laboratory


  1. J Inherit Metab Dis. 2020 Jan 13.
    Klinke G, Richter S, Monostori P, Schmidt-Mader B, García-Cazorla A, Artuch R, Christ S, Opladen T, Hoffmann GF, Blau N, Okun JG.
      BACKGROUND: Laboratory investigations of cerebrospinal fluid (CSF) are essential when suspecting an inborn error of metabolism (IEM) involving neurological features. Available tests are currently performed on different analytical platforms, requiring a large sample volume and long turnaround time, which often delays timely diagnosis. Therefore, it would be preferable to have an "one-instrument" targeted multi-metabolite approach.METHOD: A liquid chromatography-tandem mass spectrometry (LC-MS/MS) platform, based on two different methods for analyzing 38 metabolites using positive and negative electrospray ionization modes, was established. To allow for platform extension, both methods were designed to use the same CSF sample preparation procedure and to be run on the same separation column (ACE C18-PFP).
    RESULTS: Assessment of the LC-MS/MS platform methods was first made by analytical validation, followed by the establishment of literature-based CSF cut-off values and reference ranges, and by the measurement of available samples obtained from patients with confirmed diagnoses of aromatic L-amino acid decarboxylase deficiency, guanidinoacetate methyltransferase deficiency, ornithine aminotransferase deficiency, cerebral folate deficiency and methylenetetrahydrofolate reductase deficiency.
    CONCLUSION: An extendable targeted LC-MS/MS platform was developed for the analysis of multiple metabolites in CSF, thereby distinguishing samples from patients with IEM from non-IEM samples. Reference concentrations for several biomarkers in CSF are provided for the first time. By measurement on a single analytical platform, less sample volume is required (200 μl), diagnostic results are obtained faster, and preanalytical issues are reduced.
    SYNOPSIS: LC-MS/MS platform for CSF analysis consisting of two differentially designed methods This article is protected by copyright. All rights reserved.
    Keywords:  cerebrospinal fluid; inborn errors of metabolism; inherited metabolic diseases; liquid chromatography coupled to tandem mass spectrometry; reference ranges; targeted metabolomics
    DOI:  https://doi.org/10.1002/jimd.12213
  2. Bioinformatics. 2020 Jan 17. pii: btaa037. [Epub ahead of print]
    Wu CT, Wang Y, Wang Y, Ebbels T, Karaman I, Graça G, Pinto R, Herrington DM, Wang Y, Yu G.
      MOTIVATION: Liquid chromatography - mass spectrometry (LC-MS) is a standard method for proteomics and metabolomics analysis of biological samples. Unfortunately, it suffers from various changes in the retention times (RT) of the same compound in different samples, and these must be subsequently corrected (aligned) during data processing. Classic alignment methods such as in the popular XCMS package often assume a single time-warping function for each sample. Thus, the potentially varying RT drift for compounds with different masses in a sample is neglected in these methods. Moreover, the systematic change in RT drift across run order is often not considered by alignment algorithms. Therefore, these methods cannot effectively correct all misalignments. For a large-scale experiment involving many samples, the existence of misalignment becomes inevitable and concerning.RESULTS: Here we describe an integrated reference-free profile alignment method, neighbor-wise compound-specific Graphical Time Warping (ncGTW), that can detect misaligned features and align profiles by leveraging expected RT drift structures and compound-specific warping functions. Specifically, ncGTW uses individualized warping functions for different compounds and assigns constraint edges on warping functions of neighboring samples. Validated with both realistic synthetic data and internal quality control samples, ncGTW applied to two large-scale metabolomics LC-MS datasets identifies many misaligned features and successfully realigns them. These features would otherwise be discarded or uncorrected using existing methods. The ncGTW software tool is developed currently as a plug-in to detect and realign misaligned features present in standard XCMS output.
    AVAILABILITY AND IMPLEMENTATION: An R package of ncGTW is freely available at Bioconductor and https://github.com/ChiungTingWu/ncGTW. A detailed user's manual and a vignette are provided within the package.
    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    DOI:  https://doi.org/10.1093/bioinformatics/btaa037
  3. Methods Mol Biol. 2020 ;2104 61-97
    Pathmasiri W, Kay K, McRitchie S, Sumner S.
      In this chapter, we summarize data preprocessing and data analysis strategies used for analysis of NMR data for metabolomics studies. Metabolomics consists of the analysis of the low molecular weight compounds in cells, tissues, or biological fluids, and has been used to reveal biomarkers for early disease detection and diagnosis, to monitor interventions, and to provide information on pathway perturbations to inform mechanisms and identifying targets. Metabolic profiling (also termed metabotyping) involves the analysis of hundreds to thousands of molecules using mainly state-of-the-art mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy technologies. While NMR is less sensitive than mass spectrometry, NMR does provide a wealth of complex and information rich metabolite data. NMR data together with the use of conventional statistics, modeling methods, and bioinformatics tools reveals biomarker and mechanistic information. A typical NMR spectrum, with up to 64k data points, of a complex biological fluid or an extract of cells and tissues consists of thousands of sharp signals that are mainly derived from small molecules. In addition, a number of advanced NMR spectroscopic methods are available for extracting information on high molecular weight compounds such as lipids or lipoproteins. There are numerous data preprocessing, data reduction, and analysis methods developed and evolving in the field of NMR metabolomics. Our goal is to provide an extensive summary of NMR data preprocessing and analysis strategies by providing examples and open source and commercially available analysis software and bioinformatics tools.
    Keywords:  Metabolomics; Metabotyping; Multivariate data analysis; NMR; Preprocessing; Quality control
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_5
  4. Methods Mol Biol. 2020 ;2104 419-445
    Srivastava A, Creek DJ.
      Rapid advancements in metabolomics technologies have allowed for application of liquid chromatography mass spectrometry (LCMS)-based metabolomics to investigate a wide range of biological questions. In addition to an important role in studies of cellular biochemistry and biomarker discovery, an exciting application of metabolomics is the elucidation of mechanisms of drug action (Creek et al., Antimicrob Agents Chemother 60:6650-6663, 2016; Allman et al., Antimicrob Agents Chemother 60:6635-6649, 2016). Although it is a very useful technique, challenges in raw data processing, extracting useful information out of large noisy datasets, and identifying metabolites with confidence, have meant that metabolomics is still perceived as a highly specialized technology. As a result, metabolomics has not yet achieved the anticipated extent of uptake in laboratories around the world as genomics or transcriptomics. With a view to bring metabolomics within reach of a nonspecialist scientist, here we describe a routine workflow with IDEOM, which is a graphical user interface within Microsoft Excel, which almost all researchers are familiar with. IDEOM consists of custom built algorithms that allow LCMS data processing, automatic noise filtering and identification of metabolite features (Creek et al., Bioinformatics 28:1048-1049, 2012). Its automated interface incorporates advanced LCMS data processing tools, mzMatch and XCMS, and requires R for complete functionality. IDEOM is freely available for all researchers and this chapter will focus on describing the IDEOM workflow for the nonspecialist researcher in the context of studies designed to elucidate mechanisms of drug action.
    Keywords:  Data processing; Drug mechanism; IDEOM; LCMS; Metabolomics; Microsoft Excel; Mode of action
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_21
  5. Bioinformatics. 2020 Jan 13. pii: btaa012. [Epub ahead of print]
    Kouřil Š, de Sousa J, Václavík J, Friedecký D, Adam T.
      SUMMARY: Untargeted liquid chromatography-high-resolution mass spectrometry analysis produces a large number of features which correspond to the potential compounds in the sample that is analyzed. During the data processing, it is necessary to merge features associated with one compound to prevent multiplicities in the data and possible misidentification. The processing tools that are currently employed use complex algorithms to detect abundances, such as adducts or isotopes. However, most of them are not able to deal with unpredictable adducts and in-source fragments. We introduce a simple opensource R-script CROP based on Pearson pairwise correlations and retention time together with a graphical representation of the correlation network to remove these redundant features.AVAILABILITY: The CROP R-script is available online at www.github.com/rendju/CROP under GNU GPL.
    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    DOI:  https://doi.org/10.1093/bioinformatics/btaa012
  6. Rapid Commun Mass Spectrom. 2020 Jan 17. e8730
    Fu H, Zhang QL, Huang XW, Ma ZH, Zheng XL, Li SL, Duan HN, Sun XC, Lin FF, Zhao LJ, Teng GS, Liu J.
      RATIONALE: Short-chain fatty acids (SCFAs) are associated with intestinal microbiota and diseases in humans. SCFAs have a low response in mass spectrometry and in order to increase sensitivity, reduce sample consumption, shorten analysis time, and simplify sample preparation steps, a derivatization method was developed.METHODS: We converted seven SCFAs into amide derivatives with 4-aminomethylquinoline. The reaction took 20 min at room temperature. Analytes were separated on a reversed-phase C18 column and quantitated in positive ion electrospray ionization mode using multiple reaction monitoring. Acetic acid-d4 was used as the stable isotope-labelled surrogate analyte for acetic acid in the working solutions, while the other stable isotope-labelled standards were used as internal standards (ISs).
    RESULTS: Method validation showed that the intra-day and inter-days precision of quantitation for the seven SCFAs over the whole concentration range was ≤ 3.8% (n = 6). The quantitation accuracy ranged from 85.5% to 104.3% (n = 6). Importantly, the collected feces need to be vortexed immediately with ethanol.
    CONCLUSIONS: This study provides a new derivatization method for precise, accurate, and rapid quantification of SCFAs in human feces using ultra-performance liquid chromatography-tandem mass spectrometry. This method successfully determined the concentration of SCFAs in human feces and could assist in the exploration of intestinal microbiota and disease.
    DOI:  https://doi.org/10.1002/rcm.8730
  7. Methods Mol Biol. 2020 ;2104 447-467
    Cai Y, Rosen Vollmar AK, Johnson CH.
      The exposome is the cumulative measure of environmental influences and associated biological responses across the life span, with critical relevance for understanding how exposures can impact human health. Metabolomics analysis of biological samples offers unique advantages for examining the exposome. Simultaneous analysis of external exposures, biological responses, and host susceptibility at a systems level can help establish links between external exposures and health outcomes. As metabolomics technologies continue to evolve for the study of the exposome, metabolomics ultimately will help provide valuable insights for exposure risk assessment, and disease prevention and management. Here, we discuss recent advances in metabolomics, and describe data processing protocols that can enable analysis of the exposome. This chapter focuses on using liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics for analysis of the exposome, including (1) preprocessing of untargeted metabolomics data, (2) identification of exposure chemicals and their metabolites, and (3) methods to establish associations between exposures and diseases.
    Keywords:  Data processing; Exposome; Human health; Untargeted metabolomics
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_22
  8. Rapid Commun Mass Spectrom. 2020 Jan 13. e8725
    Vargas F, Weldon KC, Sikora N, Wang M, Zhang Z, Gentry EC, Panitchpakdi MW, Caraballo-Rodríguez AM, Dorrestein PC, Jarmusch AK.
      RATIONALE: A major hurdle in identifying chemicals in mass spectrometry experiments is the availability of MS/MS reference spectra in public databases. Currently, scientists purchase databases or use public databases such as Global Natural Product Social Molecular Networking (GNPS). The MSMS-Chooser workflow is an open-source protocol for the creation of MS/MS reference spectra directly in the GNPS infrastructure.METHODS: An MSMS-Chooser Sample Template is provided and completed manually. The MSMS-Chooser Submission File and Sequence Table for data acquisition were programmatically generated. Standards from the Mass Spectrometry Metabolite Library (MSMLS) suspended in a methanol-water (1:1) solution were analyzed. Flow injection on an LC/MS/MS system was used to generate negative and positive mode data using data-dependent acquisition. The MS/MS spectra and Submission File were uploaded to MSMS-Chooser workflow in GNPS for automatic selection of MS/MS spectra.
    RESULTS: Data acquisition and processing required ~2 hours and ~2 min, respectively, per 96-well plate using MSMS-Chooser. Analysis of the MSMLS, over 600 small molecules, using MSMS-Chooser added 889 spectra (including multiple adducts) to the public library in GNPS. Manual validation of one plate indicated accurate selection of MS/MS scans (true positive rate of 0.96 and a true negative rate of 0.99). The MSMS-Chooser output includes a table formatted for inclusion in the GNPS library as well as the ability to directly launch searches via MASST.
    CONCLUSIONS: MSMS-Chooser enables rapid data acquisition, data analysis (selection of MS/MS spectra), and a formatted table for inspection and upload to GNPS. Open file-format data (.mzML or.mzXML) from most mass spectrometry platforms containing MS/MS can be processed using MSMS-Chooser. MSMS-Chooser democratizes the creation of MS/MS reference spectra in GNPS which will improve annotation and strengthen the tools which use the annotation information.
    DOI:  https://doi.org/10.1002/rcm.8725
  9. Metabolites. 2020 Jan 08. pii: E28. [Epub ahead of print]10(1):
    Fernández-Ochoa Á, Quirantes-Piné R, Borrás-Linares I, Cádiz-Gurrea ML, Precisesads Clinical Consortium , Alarcón Riquelme ME, Brunius C, Segura-Carretero A.
      Data pre-processing of the LC-MS data is a critical step in untargeted metabolomics studies in order to achieve correct biological interpretations. Several tools have been developed for pre-processing, and these can be classified into either commercial or open source software. This case report aims to compare two specific methodologies, Agilent Profinder vs. R pipeline, for a metabolomic study with a large number of samples. Specifically, 369 plasma samples were analyzed by HPLC-ESI-QTOF-MS. The collected data were pre-processed by both methodologies and later evaluated by several parameters (number of peaks, degree of missingness, quality of the peaks, degree of misalignments, and robustness in multivariate models). The vendor software was characterized by ease of use, friendly interface and good quality of the graphs. The open source methodology could more effectively correct the drifts due to between and within batch effects. In addition, the evaluated statistical methods achieved better classification results with higher parsimony for the open source methodology, indicating higher data quality. Although both methodologies have strengths and weaknesses, the open source methodology seems to be more appropriate for studies with a large number of samples mainly due to its higher capacity and versatility that allows combining different packages, functions, and methods in a single environment.
    Keywords:  R packages; data pre-processing; liquid chromatography; mass spectrometry; metabolomics; vendor software
    DOI:  https://doi.org/10.3390/metabo10010028
  10. Methods Mol Biol. 2020 ;2104 209-225
    Naake T, Gaquerel E, Fernie AR.
      High-throughput mass spectrometry (MS) metabolomics profiling of highly complex samples allows the comprehensive detection of hundreds to thousands of metabolites under a given condition and point in time and produces information-rich data sets on known and unknown metabolites. One of the main challenges is the identification and annotation of metabolites from these complex data sets since the number of authentic standards available for specialized metabolites is far lower than an account for the number of mass spectral features. Previously, we reported two novel tools, MetNet and MetCirc, for putative annotation and structural prediction on unknown metabolites using known metabolites as baits. MetNet employs differences between m/z values of MS1 features, which correspond to metabolic transformations, and statistical associations, while MetCirc uses MS/MS features as input and calculates similarity scores of aligned spectra between features to guide the annotation of metabolites. Here, we showcase the use of MetNet and MetCirc to putatively annotate metabolites and provide detailed instructions as to how those can be used. While our case studies are from plants, the tools find equal utility in studies on bacterial, fungal, or mammalian xenobiotic samples.
    Keywords:  Annotation; Metabolic modification; Molecular networking; Plant metabolite; Specialized metabolite; Unknown metabolite
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_12
  11. Bioinformatics. 2020 Jan 13. pii: btaa022. [Epub ahead of print]
    Madrid-Gambin F, Oller-Moreno S, Fernandez L, Bartova S, Giner MP, Joyce C, Ferraro F, Montoliu I, Moco S, Marco S.
      RESULTS: NMR-based metabolomics is widely used to obtain metabolic fingerprints of biological systems. While targeted workflows require previous knowledge of metabolites, prior to statistical analysis, untargeted approaches remain a challenge. Computational tools dealing with fully untargeted NMR-based metabolomics are still scarce or not user-friendly. Therefore, we developed AlpsNMR (Automated spectraL Processing System for NMR), an R package that provides automated and efficient signal processing for untargeted NMR metabolomics. AlpsNMR includes spectra loading, metadata handling, automated outlier detection, spectra alignment and peak-picking, integration, and normalization. The resulting output can be used for further statistical analysis. AlpsNMR proved effective in detecting metabolite changes in a test case. The tool allows less experienced users to easily implement this workflow from spectra to a ready-to-use dataset in their routines.AVAILABILITY: The AlpsNMR R package and tutorial is freely available to download from http://github.com/sipss/AlpsNMR under the MIT license.
    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    DOI:  https://doi.org/10.1093/bioinformatics/btaa022
  12. Methods Mol Biol. 2020 ;2104 313-336
    Ghosh T, Zhang W, Ghosh D, Kechris K.
      In recent years, mass spectrometry (MS)-based metabolomics has been extensively applied to characterize biochemical mechanisms, and study physiological processes and phenotypic changes associated with disease. Metabolomics has also been important for identifying biomarkers of interest suitable for clinical diagnosis. For the purpose of predictive modeling, in this chapter, we will review various supervised learning algorithms such as random forest (RF), support vector machine (SVM), and partial least squares-discriminant analysis (PLS-DA). In addition, we will also review feature selection methods for identifying the best combination of metabolites for an accurate predictive model. We conclude with best practices for reproducibility by including internal and external replication, reporting metrics to assess performance, and providing guidelines to avoid overfitting and to deal with imbalanced classes. An analysis of an example data will illustrate the use of different machine learning methods and performance metrics.
    Keywords:  Mass spectrometry; Metabolomics; Performance Metrics; Predictive Modeling; Supervised learning
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_16
  13. Methods Mol Biol. 2020 ;2104 227-243
    Phelan VV.
      The Global Natural Product Social Molecular Networking (GNPS) platform leverages tandem mass spectrometry (MS/MS) data for annotation of compounds. Molecular networks aid in the visualization of the chemical space within a metabolomics experiment. Recently, molecular networking has been combined with feature detection methods to yield Feature-Based Molecular Networking (FBMN). FBMN allows for the discrimination of isomers within the molecular network, incorporation of quantitative information generated by the feature detection tools into visualization of the molecular network, and compatibility with forthcoming in silico annotation tools. This chapter provides step-by-step methods for generating a molecular network to annotate microbial natural products using the Global Natural Product Social Molecular Networking (GNPS) Feature-Based Molecular Networking (FBMN) workflow.
    Keywords:  Feature annotation; GNPS; Molecular networking; Natural Products; Secondary metabolism; Specialized metabolites
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_13
  14. Methods Mol Biol. 2020 ;2104 25-48
    Du X, Smirnov A, Pluskal T, Jia W, Sumner S.
      The informatics pipeline for making sense of untargeted LC-MS or GC-MS data starts with preprocessing the raw data. Results from data preprocessing undergo statistical analysis and subsequently mapped to metabolic pathways for placing untargeted metabolomics data in the biological context. ADAP is a suite of computational algorithms that has been developed specifically for preprocessing LC-MS and GC-MS data. It consists of two separate computational workflows that extract compound-relevant information from raw LC-MS and GC-MS data, respectively. Computational steps include construction of extracted ion chromatograms, detection of chromatographic peaks, spectral deconvolution, and alignment. The two workflows have been incorporated into the cross-platform and graphical MZmine 2 framework and ADAP-specific graphical user interfaces have been developed for using ADAP with ease. This chapter summarizes the algorithmic principles underlying key steps in the two workflows and illustrates how to apply ADAP to preprocess LC-MS and GC-MS data.
    Keywords:  ADAP; Alignment; Data preprocessing; GC–MS; LC–MS; MZmine 2; Metabolomics; Peak picking; Spectral deconvolution; Visualization
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_3
  15. Mol Metab. 2019 Dec 19. pii: S2212-8778(19)30953-6. [Epub ahead of print]
    Bayram S, Fürst S, Forbes M, Kempa S.
      BACKGROUND: Cancer cell metabolism can be characterised by adaptive metabolic alterations, which support abnormal proliferative cell growth with high energetic demand. De novo nucleotide biosynthesis is essential for providing nucleotides for RNA and DNA synthesis, and drugs targeting this biosynthetic pathway have proven to be effective anticancer therapeutics. Nevertheless, cancers are often able to circumvent chemotherapeutic interventions and become therapy resistant. Our understanding of the changing metabolic profile of the cancer cell and the mode of action of therapeutics is dependent on technological advances in biochemical analysis.SCOPE OF REVIEW: This review begins with information about carbon- and nitrogen-donating pathways to build purine and pyrimidine moieties in the course of nucleotide biosynthesis. We discuss the application of stable isotope resolved metabolomics to investigate the dynamics of cancer cell metabolism and outline the benefits of high-resolution accurate mass spectrometry, which enables multiple tracer studies.
    CONCLUSION: With the technological advances in mass spectrometry that allow for the analysis of the metabolome in high resolution, the application of stable isotope resolved metabolomics has become an important technique in the investigation of biological processes. The literature in the area of isotope labelling is dominated by 13C tracer studies. Metabolic pathways have to be considered as complex interconnected networks and should be investigated as such. Moving forward to simultaneous tracing of different stable isotopes will help elucidate the interplay between carbon and nitrogen flow and the dynamics of de novo nucleotide biosynthesis within the cell.
    Keywords:  Cancer metabolism; Flux analysis; Isotope resolved metabolomics
    DOI:  https://doi.org/10.1016/j.molmet.2019.12.002
  16. J Chromatogr B Analyt Technol Biomed Life Sci. 2020 Feb 01. pii: S1570-0232(19)31021-9. [Epub ahead of print]1138 121963
    Lin CC, Sengee A, Mjøs SA.
      Fatty acids from 100 randomly selected human serum samples were esterified to fatty acid methyl esters and analyzed by gas chromatography with flame ionization detector. A subset of the 20 samples that spans the variation in the original set of 100 samples were thereafter analyzed by gas chromatography-mass spectrometry (GC-MS). The GC-MS data were acquired using capillary columns with two different stationary phases, BP20 (polyethylene glycol) and BPX70 (cyanopropyl polysilphenylene-siloxane). Equivalent chain lengths on the two columns are reported for 69 compounds that constituted more than 0.1% of the chromatographic area in at least one sample. Of these, 39 compounds were identified as regular fatty acid methyl esters. The remaining 30 compounds were decomposition products from cholesterol, dimethylacetals, three compounds that have been linked to poor kidney function, and 13 compounds that are currently unidentified. The retention index patterns showed that on both columns there were 16 compounds that were separated by less than 0.05 equivalent chain length units from the nearest neighbor, meaning that they were overlapping or poorly resolved. The relationship between the peak threshold level and the number of peaks found above the level predicts a dramatic increase in the number of peaks that have to be resolved if the threshold is lowered below 0.1%.
    Keywords:  Cyanopropyl column; Equivalent chain lengths, interferents; Fatty acid methyl esters; Gas chromatography–mass spectrometry; Polyethylene glycol column; Serum fatty acids
    DOI:  https://doi.org/10.1016/j.jchromb.2019.121963
  17. Methods Mol Biol. 2020 ;2104 121-137
    Kyle JE.
      Lipidomics data generated using untargeted mass spectrometry techniques can offer great biological insight to metabolic status and disease diagnoses. As the community's ability to conduct large-scale studies with deep coverage of the lipidome expands, approaches to analyzing untargeted data and extracting biological insight are needed. Currently, the function of most individual lipids are not known; however, meaningful biological information can be extracted. Here, I will describe a step-by-step approach to identify patterns and trends in untargeted mass spectrometry lipidomics data to assist users in extracting information leading to a greater understanding of biological systems.
    Keywords:  Blood plasma; LipidMaps; Lipidome; Lipidomics; Mass spectrometry; Untargeted
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_7
  18. Methods Mol Biol. 2020 ;2104 11-24
    Domingo-Almenara X, Siuzdak G.
      XCMS is one of the most used software for liquid chromatography-mass spectrometry (LC-MS) data processing and it exists both as an R package and as a cloud-based platform known as XCMS Online. In this chapter, we first overview the nature of LC-MS data to contextualize the need for data processing software. Next, we describe the algorithms used by XCMS and the role that the different user-defined parameters play in the data processing. Finally, we describe the extended capabilities of XCMS Online.
    Keywords:  Data processing; Liquid chromatography; Mass spectrometry; Metabolomics; XCMS
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_2
  19. Methods Mol Biol. 2020 ;2104 185-207
    Ludwig M, Fleischauer M, Dührkop K, Hoffmann MA, Böcker S.
      SIRIUS 4 is the best-in-class computational tool for metabolite identification from high-resolution tandem mass spectrometry data. It offers de novo molecular formula annotation with outstanding accuracy. When searching fragmentation spectra in a structure database, it reaches over 70% correct identifications. A predicted fingerprint, which indicates the presence or absence of thousands of molecular properties, helps to deduce information about the compound of interest even if it is not contained in any structure database. Here, we present best practices and describe how to leverage the full potential of SIRIUS 4, how to incorporate it into your own workflow, and how it adds value to the analysis of mass spectrometry data beyond spectral library search.
    Keywords:  Annotation; LC–MS/MS; Metabolite identification; Metabolomics; Molecular formula; SIRIUS; Structure prediction
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_11
  20. Methods Mol Biol. 2020 ;2104 139-148
    Yi Z, Zhu ZJ.
      Liquid chromatography-mass spectrometry (LC-MS) is one of the most popular technologies in metabolomics. The large-scale and unambiguous identification of metabolite structures remains a challenging task in LC-MS based metabolomics. Tandem mass spectral databases provide experimental and in silico MS/MS spectra to facilitate the identification of both known and unknown metabolites, which has become a gold standard method in metabolomics. In addition, metabolite knowledge databases offer valuable biological and pathway information of metabolites. In this chapter, we have briefly reviewed the most common and important tandem mass spectral and metabolite databases, and illustrated how they could be used for metabolite identification.
    Keywords:  Metabolite database; Metabolite identification; Metabolomics; Tandem mass spectrum
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_8
  21. Rapid Commun Mass Spectrom. 2020 Jan 17. e8729
    Kuwata K, Itou K, Kotani M, Ohmura T, Naito Y.
      RATIONALE: A recently developed matrix-free laser desorption/ionization method, DIUTHAME (Desorption Ionization Using Through Hole Alumina Membrane), was examined for the feasibility of mass spectrometry imaging (MSI) applied to frozen tissue sections. The permeation behavior of DIUTHAME is potentially useful for MSI as positional information may not be distorted during the extraction of analytes from a sample.METHODS: The through hole porous alumina membranes used in the DIUTHAME chips were fabricated by wet anodization and have 5 μm thickness, 200 nm through-hole diameter and 50% open aperture ratio in typical values. Mouse brain frozen tissue sections on ITO slides were covered by the DIUTHAME chips and were subjected to MSI experiments in commercial time-of-flight mass spectrometers equipped with solid-state UV lasers after thawing and drying without matrix application.
    RESULTS: Mass spectra and mass images were successfully obtained from the frozen tissue sections by means of DIUTHAME as the ionization method. The mass spectra contained rich peaks in phospholipid mass range free from chemical background owing to there being no matrix-derived peaks. DIUTHAME-MSI delivered high-quality mass images which reflected the brain tissue anatomy.
    CONCLUSION: Analytes can be extracted from frozen tissue by capillary action of the through holes in DIUTHAME and moisture contained in the tissue without distortion of positional information of the analytes. The sample preparation for frozen tissue sections in DIUTHAME-MSI is simple, requiring none of the skills or dedicated apparatus needed for matrix application. DIUTHAME can facilitate MSI at low mass, as there is no interference from matrix-derived peaks, and should provide good quality, reproducible mass images more easily than MALDI-MSI.
    DOI:  https://doi.org/10.1002/rcm.8729
  22. Anal Bioanal Chem. 2020 Jan 17.
    Rodríguez-Ramos R, Socas-Rodríguez B, Santana-Mayor Á, Rodríguez-Delgado MÁ.
      In this work, the development of a simple, fast and reliable method for the evaluation of a group of twelve plastic migrants in alcoholic and non-alcoholic beverages widely consumed by the population has been carried out. For that, a modified QuEChERS method for the extraction and preconcentration of the target compounds has been used prior to their separation and quantification by gas chromatography coupled to triple quadrupole tandem mass spectrometry. The whole methodology was validated for beer, cider and grape juice matrices, using dibutyl phthalate-3,4,5,6-d4 as surrogate. Recovery ranged from 75 to 120% for all matrices with relative standard deviation values lower than 20%, and the limits of quantification of the method were achieved in the range 0.034-1.415 μg/L. Finally, the analysis of different beer, cider and grape juice samples commercialised in different supermarkets of Tenerife was carried out, finding the presence of four of the evaluated phthalates in the range 0.14-1.1 μg/L in some of the evaluated beers, six of them in several cider samples, in the range 0.3-2.1 μg/L, and one in the range 1.2-1.5 μg/L in three of the analysed grape juices.
    Keywords:  Beer; Cider; Gas chromatography; Grape juice; Mass spectrometry; Phthalates; QuEChERS
    DOI:  https://doi.org/10.1007/s00216-019-02382-0
  23. J Am Soc Mass Spectrom. 2018 Aug 01. 29(8): 1745-1756
    Jora M, Burns AP, Ross RL, Lobue PA, Zhao R, Palumbo CM, Beal PA, Addepalli B, Limbach PA.
      The analytical identification of positional isomers (e.g., 3-, N4-, 5-methylcytidine) within the > 160 different post-transcriptional modifications found in RNA can be challenging. Conventional liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) approaches rely on chromatographic separation for accurate identification because the collision-induced dissociation (CID) mass spectra of these isomers nearly exclusively yield identical nucleobase ions (BH2+) from the same molecular ion (MH+). Here, we have explored higher-energy collisional dissociation (HCD) as an alternative fragmentation technique to generate more informative product ions that can be used to differentiate positional isomers. LC-MS/MS of modified nucleosides characterized using HCD led to the creation of structure- and HCD energy-specific fragmentation patterns that generated unique fingerprints, which can be used to identify individual positional isomers even when they cannot be separated chromatographically. While particularly useful for identifying positional isomers, the fingerprinting capabilities enabled by HCD also offer the potential to generate HPLC-independent spectral libraries for the rapid analysis of modified ribonucleosides.
    Keywords:  HCD fragmentation; LC-MS/MS; Nucleoside analysis; Positional isomers; RNA modification
  24. Anal Chem. 2020 Jan 13.
    Rubio VY, Cagmat JG, Wang GP, Yost RA, Garrett TJ.
      Current targeted metabolomic workflows are limited by design and thus sacrifice crucial information from a profiling stand-point that could lead to a more fundamental understanding of the metabolic processes of interest. One drawback to per-forming targeted analysis on ion trapping instruments is the potential for increased variability in analysis when analytes and standards are isolated and trapped individually for fragmentation. In addition, this sequential isolation process increases the duty cycle of the mass spectrometer and reduces the number of points collected across a chromatographic peak. To ad-dress this, the use of a wide isolation window (12 Da) to encompass the target analyte and the isotope standard within a sin-gle fragmentation window ensures that fragmentation is consistent when quantitation relies on the ratio of the target to the internal standard. Additionally, the preservation of a faster scan rate ensures that optimal representation of chromato-graphic peaks are preserved for the purposes of both quantitative and qualitative analyses that require peak integration for statistical analysis. The use of this flexible method is promising in the investigation of pathways that require multiple targets and are highly integrated within the system. Here, we demonstrate the application of this method in a fast ultra-high-performance liquid chromatography (UHPLC) analysis to integrate wide-isolation quantitative strategies for high-resolution mass spectrometry (HRMS) combined with profiling qualitative metabolomics for the analysis of tryptophan degradation metabolites in mouse serum. Analysis of tryptophan deficient states as compared to control chow in both germ-free or E. coli gut microbiota states were used to quantitate pathway-specific metabolites as well as obtain full profiling information. The quantitative and qualitative results revealed the preservation of the primary pathways of degradation in the kynurenine pathway to potentially produce primary products such as nicotinamide during stress-induced dietary states.
    DOI:  https://doi.org/10.1021/acs.analchem.9b04210
  25. Methods Mol Biol. 2020 ;2104 49-60
    Rurik M, Alka O, Aicheler F, Kohlbacher O.
      This chapter describes the open-source tool suite OpenMS. OpenMS contains more than 180 tools which can be combined to build complex and flexible data-processing workflows. The broad range of functionality and the interoperability of these tools enable complex, complete, and reproducible data analysis workflows in computational proteomics and metabolomics. We introduce the key concepts of OpenMS and illustrate its capabilities with a complete workflow for the analysis of untargeted metabolomics data, including metabolite quantification and identification.
    Keywords:  Data analysis; Metabolomics; OpenMS; Reproducible science; Workflows
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_4
  26. Methods Mol Biol. 2020 ;2104 387-400
    Karnovsky A, Li S.
      Recent advances in analytical techniques, particularly LC-MS, generate increasingly large and complex metabolomics datasets. Pathway analysis tools help place the experimental observations into relevant biological or disease context. This chapter provides an overview of the general concepts and common tools for pathway analysis, including Mummichog for untargeted metabolomics. Examples of pathway mapping, MetScape, and Mummichog are explained. This serves as both a practical tutorial and a timely survey of pathway analysis for label-free metabolomics data.
    Keywords:  MetScape; Metabolic network; Metabolomics; Mummichog; Pathway analysis; Untargeted metabolomics
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_19
  27. Anal Bioanal Chem. 2020 Jan 16.
    Schulze B, Bader T, Seitz W, Winzenbacher R.
      To close the "analytical gap" in the liquid chromatographic (LC) analysis of highly polar substances, two techniques which have been suggested earlier were tested in terms of retention factors and detection limits: hydrophilic interaction liquid chromatography (HILIC) and mixed-mode chromatography (MMC). A substance mix of 55 analytes ranging from logD - 8.2 to 3.4 and 17 different LC columns, also comprising additional reversed-phase columns were used. Contrary to most reversed-phase columns, column bleed has been identified as an important factor, which may cause serious restrictions during high-resolution mass spectrometric detection (HRMS). We found that highly abundant background masses continuously eluting from the columns heavily influence ion transmission to the detector. As a result, the linear dynamic range as well as the sensitivity decreases and thus limits the HRMS applicability of some columns. We therefore recommend a thorough investigation of ion transmission during HRMS method development. This will help to maintain the high potential of HRMS in terms of qualitative and quantitative screening analysis.
    Keywords:  HILIC; Ion transmission; MMC; Mass spectrometry; PMOC; PMT
    DOI:  https://doi.org/10.1007/s00216-020-02387-0
  28. J Am Soc Mass Spectrom. 2019 Oct 01. 30(10): 2037-2040
    Rister AL, Dodds ED.
      Estradiol is an estrogenic steroid that can undergo glucuronidation at two different sites, which results in two estradiol glucuronide regioisomers. These isomers can be produced by different enzymes and can have different biological activities before being eliminated from the body. Although there have been previous methods that can distinguish the two isomers, these methods often have long acquisition times or high cost per analysis. In this study, traveling wave ion mobility spectrometry (TWIMS) coupled with mass spectrometry (MS) was employed to separate estradiol glucuronides using alkali metal adduction in positive ion mode, where the sodiated dimer adduct provided adequate separation both in single-component standards and in two-component mixtures. Additionally, in negative ion mode, tandem mass spectrometry (MS/MS) was used to quantitatively determine the relative composition of the two isomers. This was possible due to differences in the energetic requirements for loss of the glucuronic acid, which was characterized by energy-resolved collision-induced dissociation (CID). This work demonstrated that the intensity of the glucuronic acid neutral loss product as compared with the intensity of the intact precursor ion can be used to determine the percentage of each isomer present in a mixture. Overall, TWIMS successfully separated estradiol glucuronide isomers in positive ion mode and MS/MS via CID enables relative quantitation of each isomer in negative ion mode.
    Keywords:  Metal ion adduction; Steroid glucuronide isomers; Tandem mass spectrometry; Traveling wave ion mobility spectrometry
  29. Molecules. 2020 Jan 15. pii: E349. [Epub ahead of print]25(2):
    Liakh I, Pakiet A, Sledzinski T, Mika A.
      Oxylipins are derivatives of polyunsaturated fatty acids and due to their important and diverse functions in the body, they have become a popular subject of studies. The main challenge for researchers is their low stability and often very low concentration in samples. Therefore, in recent years there have been developments in the extraction and analysis methods of oxylipins. New approaches in extraction methods were described in our previous review. In turn, the old analysis methods have been replaced by new approaches based on mass spectrometry (MS) coupled with liquid chromatography (LC) and gas chromatography (GC), and the best of these methods allow hundreds of oxylipins to be quantitatively identified. This review presents comparative and comprehensive information on the progress of various methods used by various authors to achieve the best results in the analysis of oxylipins in biological samples.
    Keywords:  GC–MS; HPLC; LC–MS; UHPLC; biological samples; oxylipins
    DOI:  https://doi.org/10.3390/molecules25020349
  30. Metabolites. 2020 Jan 10. pii: E30. [Epub ahead of print]10(1):
    Cocuron JC, Ross Z, Alonso AP.
      Subcellular compartmentation has been challenging in plant 13C-metabolic flux analysis. Indeed, plant cells are highly compartmented: they contain vacuoles and plastids in addition to the regular organelles found in other eukaryotes. The distinction of reactions between compartments is possible when metabolites are synthesized in a particular compartment or by a unique pathway. Sucrose is an example of such a metabolite: it is specifically produced in the cytosol from glucose 6-phosphate (G6P) and fructose 6-phosphate (F6P). Therefore, determining the 13C-labeling in the fructosyl and glucosyl moieties of sucrose directly informs about the labeling of cytosolic F6P and G6P, respectively. To date, the most commonly used method to monitor sucrose labeling is by nuclear magnetic resonance, which requires substantial amounts of biological sample. This study describes a new methodology that accurately measures the labeling in free sugars using liquid chromatography tandem mass spectrometry (LC-MS/MS). For this purpose, maize embryos were pulsed with [U-13C]-fructose, intracellular sugars were extracted, and their time-course labeling was analyzed by LC-MS/MS. Additionally, extracts were enzymatically treated with hexokinase to remove the soluble hexoses, and then invertase to cleave sucrose into fructose and glucose. Finally, the labeling in the glucosyl and fructosyl moieties of sucrose was determined by LC-MS/MS.
    Keywords:  13C-labeling; 13C-metabolic flux analysis; LC-MS/MS; fructose 6-phosphate; glucose 6-phosphate; hexokinase; invertase; subcellular compartmentation; sucrose
    DOI:  https://doi.org/10.3390/metabo10010030
  31. Clin Chem Lab Med. 2020 Jan 13. pii: /j/cclm.ahead-of-print/cclm-2019-0959/cclm-2019-0959.xml. [Epub ahead of print]
    Hawley JM, Adaway JE, Owen LJ, Keevil BG.
      Background Classically, serum testosterone (T) and androstenedione (A4) have been the mainstay for the biochemical assessment of hyperandrogenism. However, recent evidence suggests 11β-hydroxyandrostenedione (11OHA4) and 11-ketotestosterone (11KT) may also be important. Here, we describe the development of a liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay for quantitation of total serum T, A4, 17-hydroxyprogesterone (17OHP), 11OHA4 and 11KT. In addition, we applied the method to assess pre-analytical stability. Methods An isotopically labelled internal standard was added to samples prior to supported liquid extraction (SLE). Extracts were analysed using LC-MS/MS to detect T/A4/17OHP/11OHA4 and 11KT along with their corresponding internal standards. Samples (n = 7) were collected from healthy volunteers (n = 14) and left incubated at 20 °C for up to 72 h. Tubes were retrieved at select time points, centrifuged, separated and frozen prior to analysis. Results The total run time was 4 min. For all analytes, intra- and inter-assay imprecision did not exceed 7.9% and 5.3%, respectively; matrix effects were negligible and mean recoveries ranged from 95.3 to 111.6%. The limits of quantitation (LOQs) were 0.25 nmol/L for T, A4 and 11OHA4, 0.50 nmol/L for 17OHP, and 0.24 nmol/L for 11KT. No significant change was observed in pre-centrifugation A4 or female T concentrations over 72 h. Significant increases (p < 0.01) in concentrations of 11KT, 17OHP, 11OHA4 and male T were observed after 2, 8, 12 and 24 h, respectively. Conclusions We developed a robust LC-MS/MS assay for the quantitation of total serum T/A4/17OHP/11OHA4 and 11KT. Applying the method to determine pre-analytical stability suggests samples requiring 11KT need separating from the cells within 2 h.
    Keywords:  11-ketotestosterone; 11β-hydroxyandrostenedione; androgens; mass spectrometry; stability
    DOI:  https://doi.org/10.1515/cclm-2019-0959
  32. Int J Epidemiol. 2020 Jan 16. pii: dyz244. [Epub ahead of print]
    Ekholm J, Ohukainen P, Kangas AJ, Kettunen J, Wang Q, Karsikas M, Khan AA, Kingwell BA, Kähönen M, Lehtimäki T, Raitakari OT, Järvelin MR, Meikle PJ, Ala-Korpela M.
      MOTIVATION: An intuitive graphical interface that allows statistical analyses and visualizations of extensive data without any knowledge of dedicated statistical software or programming.IMPLEMENTATION: EpiMetal is a single-page web application written in JavaScript, to be used via a modern desktop web browser.
    GENERAL FEATURES: Standard epidemiological analyses and self-organizing maps for data-driven metabolic profiling are included. Multiple extensive datasets with an arbitrary number of continuous and category variables can be integrated with the software. Any snapshot of the analyses can be saved and shared with others via a www-link. We demonstrate the usage of EpiMetal using pilot data with over 500 quantitative molecular measures for each sample as well as in two large-scale epidemiological cohorts (N >10 000).
    AVAILABILITY: The software usage exemplar and the pilot data are open access online at [http://EpiMetal.computationalmedicine.fi]. MIT licensed source code is available at the Github repository at [https://github.com/amergin/epimetal].
    DOI:  https://doi.org/10.1093/ije/dyz244
  33. Methods Mol Biol. 2020 ;2104 165-184
    Wishart DS.
      The Human Metabolome Database (HMDB) is a comprehensive, online, digital database designed to support the analysis and interpretation of metabolomic data acquired from human and/or mammalian metabolomic studies. This chapter covers three methods or protocols pertinent to using the HMDB: (1) understanding the general layout of the HMDB; (2) exploring the contents of a typical HMDB "MetaboCard"; and (3) an example of how HMDB can be used in a metabolomics study on human glioblastoma.
    Keywords:  Data analysis; Database; Disease; Human; Metabolomics
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_10
  34. J Am Soc Mass Spectrom. 2019 Oct 01. 30(10): 2031-2036
    Baker ES, Patti GJ.
      In November 2018, the American Society for Mass Spectrometry hosted the Annual Fall Workshop on informatic methods in metabolomics. The Workshop included sixteen lectures presented by twelve invited speakers. The focus of the talks was untargeted metabolomics performed with liquid chromatography/mass spectrometry. In this review, we highlight five recurring topics that were covered by multiple presenters: (i) data sharing, (ii) artifacts and contaminants, (iii) feature degeneracy, (iv) database organization, and (v) requirements for metabolite identification. Our objective here is to present viewpoints that were widely shared among participants, as well as those in which varying opinions were articulated. We note that most of the presenting speakers employed different data processing software, which underscores the diversity of informatic programs currently being used in metabolomics. We conclude with our thoughts on the potential role of reference datasets as a step towards standardizing data processing methods in metabolomics.
    Keywords:  ASMS Fall Workshop; Informatics; Metabolism; Metabolomics
  35. Methods Mol Biol. 2020 ;2104 361-386
    Hattwell JPN, Hastings J, Casanueva O, Schirra HJ, Witting M.
      Interpretation of metabolomics data in the context of biological pathways is important to gain knowledge about underlying metabolic processes. In this chapter we present methods to analyze genome-scale models (GSMs) and metabolomics data together. This includes reading and mining of GSMs using the SBTab format to retrieve information on genes, reactions, and metabolites. Furthermore, the chapter showcases the generation of metabolic pathway maps using the Escher tool, which can be used for data visualization. Lastly, approaches to constrain flux balance analysis (FBA) by metabolomics data are presented.
    Keywords:  Analysis; Integration; Metabolic networks; Metabolomics; Visualization
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_18
  36. J Am Soc Mass Spectrom. 2019 Nov 01. 30(11): 2369-2379
    Powers JB, Campagna SR.
      Various separation and mass spectrometric (MS) techniques have furthered our ability to study complex mixtures, and the desire to measure every analyte in a system is of continual interest. For many complex mixtures, such as the total molecular content of a cell, it is becoming apparent that no one single separation technique or analysis is likely to achieve this goal. Therefore, having a variety of tools to measure the complexity of these mixtures is prudent. Orbitrap MSs are broadly used in systems biology studies due to their unique performance characteristics. However, GC-Orbitraps have only recently become available, and instruments that can use gas chromatography (GC) cannot use liquid chromatography (LC) and vice versa. This limits small molecule analyses, such as those that would be employed for metabolomics, lipidomics, or toxicological studies. Thus, a simple, temporary interface was designed for a GC and Thermo Scientific™ Ion Max housing unit. This interface enables either GC or LC separation to be used on the same MS, an Exactive™ Plus Orbitrap, and utilizes an atmospheric pressure chemical ionization (APCI) source. The GC-APCI interface was tested against a commercially available atmospheric pressure photoionization (APPI) interface for three types of analytes that span the breadth of typical GC analyses: fatty acid methyl esters (FAMEs), polyaromatic hydrocarbons (PAHs), and saturated hydrocarbons. The GC-APCI-Orbitrap had similar or improved performance to the APPI and other reported methods in that it had a lower limit of quantitation, better signal to noise, and lower tendency to fragment analytes.
    Keywords:  Atmospheric pressure chemical ionization (APCI); Atmospheric pressure chemical ionization (APPI); Fatty acid methyl esters (FAMEs); GC-MS; Gas chromatography; Interface; Orbitrap; Polyaromatic hydrocarbons (PAHs); Saturated hydrocarbons
  37. Methods Mol Biol. 2020 ;2104 1-10
    Barnes S.
      Metabolomics has become a powerful tool in biological and clinical investigations. This chapter reviews the technological basis of metabolomics and the considerations in answering biomedical questions. The workflow of metabolomics is explained in the sequence of data processing, quality control, metabolite annotation, statistical analysis, pathway analysis, and multi-omics integration. Reproducibility in both sample analysis and data analysis is key to the scientific progress, and the recommendation is made on reporting standards in publications. This chapter explains the technical aspects of metabolomics in the context of systems biology and applications to human health.
    Keywords:  Annotation; GC-MS; LC-MS; Metabolomics; NMR; Precision medicine; Recommendation; Systems medicine
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_1
  38. J Pharm Biomed Anal. 2020 Jan 07. pii: S0731-7085(19)32098-9. [Epub ahead of print]181 113097
    Rodrigues MVN, Rodrigues-Silva C, Boaventura S, Oliveira ASS, Rath S, Cass QB.
      The screening of compounds is the initial step in research for the development of new drugs. For this reason, the availability of fast and reliable tools for the screening of a large number of compounds becomes essential. Among the therapeutic targets, the enzyme xanthine oxidase (XO) is of great interest for its importance as a biological source of superoxide radicals, which contribute to the oxidative stress on organisms and are involved in many pathological processes. In the present study, we validated a new method using an immobilized capillary enzyme reactor in an LC system directly coupled to triple quadrupole mass spectrometry to screen for XO ligands. The use of mass spectrometry provided selectivity and speed to the system, eliminating the analytical separation step. The Michaelis-Menten constant (KM) value determined for the immobilized enzyme was 14.5 ± 0.4 μmol L-1, which is consistent with the value previously reported for the XO-ICER with UV detection in a 2D LC method. The on-line approach was successfully applied to assay the XO inhibitory activities of thirty isolated compounds from different classes of natural products and provided greater productivity (288 analysis/day) than 2D LC method (84 analysis/day) of screened samples.
    Keywords:  Bioaffinity chromatography; Bioreactor; High-Throughput screening; Uric acid
    DOI:  https://doi.org/10.1016/j.jpba.2020.113097
  39. J Chromatogr A. 2019 Dec 24. pii: S0021-9673(19)31294-4. [Epub ahead of print] 460823
    Zisi C, Pappa-Louisi A, Nikitas P.
      A complete package of functions in the R-language has been written for professional separation optimization of complex mixtures of ionized and/or non-ionized solutes. The package includes functions for (a) base-line correction of experimentally recorded chromatograms, (b) modeling of chromatographic peak shapes and retention data, (c) prediction of the retention time of the test analytes and/or their chromatograms, and (d) separation optimization under either isocratic or single and/or double gradient elution conditions by changing the organic modifier(s) content and/or eluent pH. The optimization functions presented in this study offer two different modes for selection of optimal separation conditions: automatic and manual mode. In the automatic mode, the optimal separation conditions are determined by maximizing the resolution within separation time preset by the analyst. In the manual mode, the optimal separation conditions are selected via scatter or contour plots. The foreknowledge of the precise dependence of resolution and separation time upon one or two retention parameters of interest, provided by the proposed computer-assisted separation optimization method, gives chromatographers a feel of confidence for the selection of the optimal conditions for a desired separation. An illustrative video given in the Supplementary material may encourage a novice practitioner in R (software) programming language to follow the proposed separation optimization procedure in a real HPLC analysis.
    Keywords:  Computer assisted separation optimization and simulation; HPLC analysis; Isocratic and gradient elution; R programming language
    DOI:  https://doi.org/10.1016/j.chroma.2019.460823
  40. J Am Soc Mass Spectrom. 2019 Nov 01. 30(11): 2185-2195
    Dodds JN, Baker ES.
      Ion mobility spectrometry (IMS) is a rapid separation technique that has experienced exponential growth as a field of study. Interfacing IMS with mass spectrometry (IMS-MS) provides additional analytical power as complementary separations from each technique enable multidimensional characterization of detected analytes. IMS separations occur on a millisecond timescale, and therefore can be readily nested into traditional GC and LC/MS workflows. However, the continual development of novel IMS methods has generated some level of confusion regarding the advantages and disadvantages of each. In this critical insight, we aim to clarify some common misconceptions for new users in the community pertaining to the fundamental concepts of the various IMS instrumental platforms (i.e., DTIMS, TWIMS, TIMS, FAIMS, and DMA), while addressing the strengths and shortcomings associated with each. Common IMS-MS applications are also discussed in this review, such as separating isomeric species, performing signal filtering for MS, and incorporating collision cross-section (CCS) values into both targeted and untargeted omics-based workflows as additional ion descriptors for chemical annotation. Although many challenges must be addressed by the IMS community before mobility information is collected in a routine fashion, the future is bright with possibilities.
    Keywords:  IMS; Ion mobility spectrometry; Mass spectrometry; Untargeted metabolomics
  41. Methods Mol Biol. 2020 ;2104 337-360
    Chong J, Xia J.
      MetaboAnalyst ( www.metaboanalyst.ca ) is an easy-to-use, comprehensive web-based tool, freely available for metabolomics data processing, statistical analysis, functional interpretation, as well as integration with other omics data. This chapter first provides an introductory overview to the current MetaboAnalyst (version 4.0) with regards to its underlying design concepts and user interface structure. Subsequent sections describe three common metabolomics data analysis workflows covering targeted metabolomics, untargeted metabolomics, and multi-omics data integration.
    Keywords:  Enrichment analysis; Metabolic pathway analysis; Multi-omics integration; Multivariate statistics; Web server
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_17