bims-metlip 2020-08-09 papers

bims-metlip

Biomed News

on Methods and protocols in metabolomics and lipidomics

Issue of 2020–08–09
seven papers selected by
Sofia Costa, Cold Spring Harbor Laboratory

IsoSearch: An Untargeted and Unbiased Metabolite and Lipid Isotopomer Tracing Strategy from HR-LC-MS/MS Datasets.
Assessment of human plasma and urine sample preparation for reproducible and high-throughput UHPLC-MS clinical metabolic phenotyping.
Evaluation and optimization of common lipid extraction methods in cerebrospinal fluid samples.
Evaluation of ion mobility in capillary electrophoresis coupled to mass spectrometry for the identification in metabolomics.
Evaluating line-broadening factors on a reference spectrum as a bucketing method for NMR based metabolomics.
HPLC methods for quantifying anticancer drugs in human samples: A systematic review.
MetaboMAPS: Pathway sharing and multi-omics data visualization in metabolic context.

Methods Protoc. 2020 Jul 30. pii: E54. [Epub ahead of print]3(3):

IsoSearch: An Untargeted and Unbiased Metabolite and Lipid Isotopomer Tracing Strategy from HR-LC-MS/MS Datasets.

He Huang, Min Yuan, Phillip Seitzer, Susan Ludwigsen, John M Asara.

  Stable isotopic tracer analysis is a technique used to determine carbon or nitrogen atom incorporation into biological systems. A number of mass spectrometry based approaches have been developed for this purpose, including high-resolution tandem mass spectrometry (HR-LC-MS/MS), selected reaction monitoring (SRM) and parallel reaction monitoring (PRM). We have developed an approach for analyzing untargeted metabolomic and lipidomic datasets using high-resolution mass spectrometry with polarity switching and implemented our approach in the open-source R script IsoSearch and in Scaffold Elements software. Using our strategy, which requires an unlabeled reference dataset and isotope labeled datasets across various biological conditions, we traced metabolic isotopomer alterations in breast cancer cells (MCF-7) treated with the metabolic drugs 2-deoxy-glucose, 6-aminonicotinamide, compound 968, and rapamycin. Metabolites and lipids were first identified by the commercial software Scaffold Elements and LipidSearch, then IsoSearch successfully profiled the 13C-isotopomers extracted metabolites and lipids from 13C-glucose labeled MCF-7 cells. The results interpreted known models, such as glycolysis and pentose phosphate pathway inhibition, but also helped to discover new metabolic/lipid flux patterns, including a reactive oxygen species (ROS) defense mechanism induced by 6AN and triglyceride accumulation in rapamycin treated cells. The results suggest the IsoSearch/Scaffold Elements platform is effective for studying metabolic tracer analysis in diseases, drug metabolism, and metabolic engineering for both polar metabolites and non-polar lipids.

Keywords:  13C; 15N; LC-MS/MS; cancer; cell culture; flux; glucose; glutamine; high resolution; isotopic tracer analysis; lipidomics; liquid chromatography; mass spectrometry; metabolism; metabolomics; polarity switching; stable isotope labeling

DOI:  https://doi.org/10.3390/mps3030054
Analyst. 2020 Aug 06.

Assessment of human plasma and urine sample preparation for reproducible and high-throughput UHPLC-MS clinical metabolic phenotyping.

Andrew D Southam, Liam D Haglington, Lukáš Najdekr, Andris Jankevics, Ralf J M Weber, Warwick B Dunn.

Clinical metabolic phenotyping employs metabolomics and lipidomics to detect and measure hundreds to thousands of metabolites and lipids within human samples. This approach aims to identify metabolite and lipid changes between phenotypes (e.g. disease status) that aid understanding of biochemical mechanisms driving the phenotype. Sample preparation is a critical step in clinical metabolic phenotyping: it must be reproducible and give a high extraction yield of metabolites and lipids, and in high-throughput studies it needs to be rapid. Here, we assessed the extraction of polar metabolites from human urine and polar metabolites and lipids from human plasma for analysis by ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) metabolomics and lipidomics. We evaluated several monophasic (urine and plasma) and biphasic (plasma) extractions, and we also tested alterations to (a) solvent-biofluid incubation time and temperature during monophasic extraction, and (b) phase partitioning time during biphasic extraction. Extracts were analysed by three UHPLC-MS assays: (i) hydrophilic interaction chromatography (HILIC) for urine and plasma, (ii) C18 aqueous reversed phase for urine, and (iii) C18 reversed phase for plasma lipids, and the yield and reproducibility of each method was assessed. We measured UHPLC-MS injection reproducibility as well as sample preparation reproducibility to assess sample solvent composition compatibility with UHPLC-MS and to pinpoint the origin of variance within the methods. For HILIC UHPLC-MS plasma and urine analysis, monophasic 50 : 50 methanol : acetonitrile had the most detected putatively-identified polar metabolites with high method reproducibility. This method had the highest lipid yield for plasma extracts analysed by the HILIC method. If lipid removal from the plasma polar HILIC extract is required, then the biphasic methanol/chloroform/water method is recommended. For C18 (aqueous) UHPLC-MS urine analysis, 50 : 50 methanol : water had high reproducibility and yield. For C18 UHPLC-MS plasma lipidomics, monophasic 100% isopropanol had the highest detection response of all annotated lipid classes with high reproducibility. Increasing monophasic incubation time and temperature had little benefit on metabolite and lipid yield and reproducibility for all methods.

DOI: https://doi.org/10.1039/d0an01319f
J Chromatogr B Analyt Technol Biomed Life Sci. 2020 Jul 16. pii: S1570-0232(20)30568-7. [Epub ahead of print]1153 122271

Evaluation and optimization of common lipid extraction methods in cerebrospinal fluid samples.

Bernd Reichl, Nicole Eichelberg, Matilda Freytag, Johannes Gojo, Andreas Peyrl, Wolfgang Buchberger.

  A typical lipidomics approach aims at the simultaneous analysis of a multitude of lipid species from different lipid classes with highest possible sensitivity for all target lipids. Efficient extraction of lipids from the biological matrix is a crucial step in the analytical workflow. Whereas numerous applications of classical and more recently published extraction methods have been reported for blood serum or plasma samples, very little is known about the applicability of these methods for cerebrospinal fluid (CSF). CSF though represents a highly interesting biofluid for the investigation of neurological disorders, such as Alzheimer's disease, Parkinson's disease, multiple sclerosis, amyotrophic lateral sclerosis, or brain cancer. Since CSF comprises substantially lower endogenous lipid concentrations compared to serum or plasma, the use of highly efficient extraction methods is of utmost importance. In addition, literature on lipid extraction methods is often inconsistent in terms of methodological parameters like temperature, mixing times, or the number of repeated extraction cycles. In this study, four liquid-liquid extraction methods (Folch, Bligh & Dyer, MTBE and BUME) and one protein precipitation method (MMC method) were evaluated using a pooled CSF sample, followed by the investigation of key process parameters (temperature and mixing times) and modifications of the most promising methods, in order to achieve a broad coverage of lipid classes as well as high recoveries and repeatabilities. A modified Folch method turned out as most suitable for the efficient extraction of a broad range of lipid classes from CSF including glycerophospholipids, glycerolipids and sphingolipids. In addition, using cooled solvents and equipment was shown to significantly improve lipid extraction efficiencies. Mixing times should be thoroughly optimized for the lipid classes of interest in order to achieve high recoveries without lipid degradation due to unnecessarily long mixing. Finally, acidification led to improved extraction efficiency for acidic glycerophospholipids.

Keywords:  Cerebrospinal fluid; Design of experiments; High performance liquid chromatography; Lipid extraction; Lipidomics; Mass spectrometry

DOI:  https://doi.org/10.1016/j.jchromb.2020.122271
Electrophoresis. 2020 Aug 03.

Evaluation of ion mobility in capillary electrophoresis coupled to mass spectrometry for the identification in metabolomics.

Nicolas Drouin, Andreas Mielcarek, Christian Wenz, Serge Rudaz.

  Currently, feature annotation remains one of the main challenges in untargeted metabolomics. In this context, the information provided by high-resolution mass spectrometry (HRMS) in addition to accurate mass can improve the quality of metabolite annotation, and MS/MS fragmentation patterns are widely used. Accurate mass and a separation index, such as retention time or effective mobility (μeff ), in chromatographic and electrophoretic approaches, respectively, must be used for unequivocal metabolite identification. The possibility of measuring collision cross section (CCS) values by using ion mobility (IM) is becoming increasingly popular in metabolomic studies thanks to the new generation of IM mass spectrometers. Based on their similar separation mechanisms involving electric field and the size of the compounds, the complementarity of DT CCSN2 and μeff needs to be evaluated. In this study, a comparison of DT CCSN2 and μeff was achieved in the context of feature identification ability in untargeted metabolomics by capillary zone electrophoresis (CZE) coupled with HRMS. This study confirms the high correlation of DT CCSN2 with the mass of the studied metabolites as well as the orthogonality between accurate mass and μeff , making this combination particularly interesting for the identification of several endogenous metabolites. The use of IM-MS remains of great interest for facilitating the annotation of neutral metabolites present in the electroosmotic flow (EOF) that are poorly or not separated by CZE. This article is protected by copyright. All rights reserved.

Keywords:  Cross collision section; Effective mobility; Features annotation; Ion mobility; Metabolomics

DOI:  https://doi.org/10.1002/elps.202000120
Anal Biochem. 2020 Jul 29. pii: S0003-2697(20)30404-8. [Epub ahead of print] 113872

Evaluating line-broadening factors on a reference spectrum as a bucketing method for NMR based metabolomics.

Bo Wang, Antoniette M Maldonado-Devincci, Lin Jiang.

  Metabolomics based nuclear magnetic resonance (NMR) is widely used in disease mechanism analysis and drug discovery. One of the most important factors in NMR based metabolomics study is the accuracy of spectra bucketing which plays a critical role in data interpretation. Though various methods have been developed for automatic bucketing, the most popular approach is still the traditional rectangular bucketing method which is mainly due to the requirement of user expertise for the automatic bucketing methods. In this study, we developed a new automatic bucketing method that not only efficiently increases peak bucketing accuracy but also allows the bucketing process to be conveniently visualized and adjusted by the end-users. This method applied the line broadening (lb) factor to the average spectrum for a study set which serves as the reference spectrum, and the peak width of the reference spectrum was then set as the peak bucketing pattern. The approach to pick the bucket boundaries is simple but powerful after the line broadening factor was applied. The line broadening factors from 0 to 2 l b were tested using mouse fecal samples and the 1 l b method showed similar peak patterns and data interpretation results compared with a careful manual bucketing pattern. Besides this, the new method generated bucketing patterns could be easily visualized using the Amix software and revised by general users without excessive data science and NMR instrumentation expertise. In summary, our study showed a powerful and convenient tool in NMR peak auto bucketing with flexible visualization and adjustment ability for metabolomics studies.

Keywords:  Bucketing; Line broadening; Metabolomics; Nuclear magnetic resonance

DOI:  https://doi.org/10.1016/j.ab.2020.113872
Anal Biochem. 2020 Aug 04. pii: S0003-2697(20)30423-1. [Epub ahead of print] 113891

HPLC methods for quantifying anticancer drugs in human samples: A systematic review.

Reyhaneh Sabourian, Seyedeh Zohreh Mirjalili, Negar Namini, Fateme Chavoshy, Mannan Hajimahmoudi, Maliheh Safavi.

  Pharmacokinetic (PK) study of anticancer drugs in cancer patients is highly crucial for dose selection and dosing intervals in clinical applications. Once an anticancer drug is administered, it undergoes various metabolic pathways; to determine these pathways, it is necessary to follow the administered drug in biological samples via different analytical methods. In addition, multi-drug quantification methods in patients undergoing multi-drug regimens of cancer therapy can have several benefits, such as reduced sampling time and analysis costs. In order to collect and categorize these studies, we conducted a systematic review of HPLC methods reported for the analysis of anticancer drugs in biological samples. A systematic search was performed on PubMed Medline, Scopus, and Web of Science databases, and 116 studies were included. In summary of included studies, when the objective of a method was to quantify a single drug, MS, or UV detectors were utilized equivalently. On the other hand, in methods with the aim of quantifying drug and metabolite(s) in a single run, MS detectors were the most utilized. This review can provide a comprehensive insight for researchers prior to developing a quantification method and selecting a detector.

Keywords:  HPLC; Mass; UV; anticancer; metabolite; multi-drugs

DOI:  https://doi.org/10.1016/j.ab.2020.113891
F1000Res. 2020 ;9 288

MetaboMAPS: Pathway sharing and multi-omics data visualization in metabolic context.

Julia Koblitz, Dietmar Schomburg, Meina Neumann-Schaal.

  Metabolic pathways are an important part of systems biology research since they illustrate complex interactions between metabolites, enzymes, and regulators. Pathway maps are drawn to elucidate metabolism or to set data in a metabolic context. We present MetaboMAPS, a web-based platform to visualize numerical data on individual metabolic pathway maps. Metabolic maps can be stored, distributed and downloaded in SVG-format. MetaboMAPS was designed for users without computational background and supports pathway sharing without strict conventions. In addition to existing applications that established standards for well-studied pathways, MetaboMAPS offers a niche for individual, customized pathways beyond common knowledge, supporting ongoing research by creating publication-ready visualizations of experimental data.

Keywords:  Data Visualization; Metabolic Maps; Metabolism; Omics Data; Pathways; SVG; Systems Biology

DOI:  https://doi.org/10.12688/f1000research.23427.1