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Keywords {🔍}

metabolites, methods, data, analysis, metabolomics, rplcposdmrm, hilicnegdmrm, article, abundance, method, measurements, average, metabolite, scholar, samples, google, overlinecvk, replicate, groups, correlation, values, cas, biological, acids, supplementary, top, hilic, fig, targeted, shows, metabolism, table, distribution, amino, experiments, min, measured, clustering, casecontrol, heatmap, reliable, study, cancer, sample, normalized, figure, pairs, rplc, lcmsms, hierarchical,

Topics {✒️}

lc–ms/ms-based targeted metabolomics hilic-ms/ms multi-targeted method lc–ms/ms targeted metabolomics lc-ms-based targeted metabolomics lc–ms/ms metabolomics analysis lc–ms/ms method development a_{ij}^{} = a_{ij}^{\prime}/\left liquid chromatography-mass spectrometry lc–ms/ms analysis lc–ms/ms platform lc–ms/ms methods mass spectrometry reviews ms-based metabolomics data lc-ms metabolomics methods reversed-phase liquid chromatography ^{\prime}}} {a_{ik}^{\prime}}} rplc-pos-dmrm measurements show isotope labeling-assisted evaluation lc–ms/ms cell culture plates/preparations van den berg article download pdf comprehensive global meta-analysis lc-ms metabolomics a_{ij}^{\prime} \left targeted metabolomics analysis case–control paired analysis targeted metabolomics data dehydro-l-ascorbic acid hilic-neg-dmrm data ho-joon lee ho‑joon lee ms-based metabolomics tested case–control pairs lc–ms system pathway-level metabolic regulation hilic-lc-ms agilent masshunter optimizer lc–ms analysis hilic-neg-dmrm method average fold-change magnitudes hydrophobic lc–ms hilic-neg-dmrm measurements hilic-neg-dmrm methods positive/negative ion-switching rplc-hilic correlation coefficients case–control paired samples full size image rplc-pos-dmrm method methionine/cysteine/folate metabolism

Schema {🗺️}

WebPage:
      mainEntity:
         headline:A large-scale analysis of targeted metabolomics data from heterogeneous biological samples provides insights into metabolite dynamics
         description:We previously developed a tandem mass spectrometry-based label-free targeted metabolomics analysis framework coupled to two distinct chromatographic methods, reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC), with dynamic multiple reaction monitoring (dMRM) for simultaneous detection of over 200 metabolites to study core metabolic pathways. We aim to analyze a large-scale heterogeneous data compendium generated from our LC–MS/MS platform with both RPLC and HILIC methods to systematically assess measurement quality in biological replicate groups and to investigate metabolite abundance changes and patterns across different biological conditions. Our metabolomics framework was applied in a wide range of experimental systems including cancer cell lines, tumors, extracellular media, primary cells, immune cells, organoids, organs (e.g. pancreata), tissues, and sera from human and mice. We also developed computational and statistical analysis pipelines, which include hierarchical clustering, replicate-group CV analysis, correlation analysis, and case–control paired analysis. We generated a compendium of 42 heterogeneous deidentified datasets with 635 samples using both RPLC and HILIC methods. There exist metabolite signatures that correspond to various phenotypes of the heterogeneous datasets, involved in several metabolic pathways. The RPLC method shows overall better reproducibility than the HILIC method for most metabolites including polar amino acids. Correlation analysis reveals high confidence metabolites irrespective of experimental systems such as methionine, phenylalanine, and taurine. We also identify homocystine, reduced glutathione, and phosphoenolpyruvic acid as highly dynamic metabolites across all case–control paired samples. Our study is expected to serve as a resource and a reference point for a systematic analysis of label-free LC–MS/MS targeted metabolomics data in both RPLC and HILIC methods with dMRM.
         datePublished:2019-07-09T00:00:00Z
         dateModified:2019-07-09T00:00:00Z
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         license:http://creativecommons.org/licenses/by/4.0/
         sameAs:https://doi.org/10.1007/s11306-019-1564-8
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            Targeted metabolomics
            LC–MS/MS
            RPLC
            HILIC
            Measurement reliability
            Amino acids
            Metabolite dynamics
            Biochemistry
            general
            Molecular Medicine
            Cell Biology
            Developmental Biology
            Biomedicine
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                     address:
                        name:Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, USA
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                        name:Michigan Institute for Data Science, University of Michigan, Ann Arbor, USA
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                        name:Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, USA
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               name:Li Zhang
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                     address:
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               name:Costas A. Lyssiotis
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                     address:
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                        name:Rogel Cancer Center, University of Michigan, Ann Arbor, USA
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      headline:A large-scale analysis of targeted metabolomics data from heterogeneous biological samples provides insights into metabolite dynamics
      description:We previously developed a tandem mass spectrometry-based label-free targeted metabolomics analysis framework coupled to two distinct chromatographic methods, reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC), with dynamic multiple reaction monitoring (dMRM) for simultaneous detection of over 200 metabolites to study core metabolic pathways. We aim to analyze a large-scale heterogeneous data compendium generated from our LC–MS/MS platform with both RPLC and HILIC methods to systematically assess measurement quality in biological replicate groups and to investigate metabolite abundance changes and patterns across different biological conditions. Our metabolomics framework was applied in a wide range of experimental systems including cancer cell lines, tumors, extracellular media, primary cells, immune cells, organoids, organs (e.g. pancreata), tissues, and sera from human and mice. We also developed computational and statistical analysis pipelines, which include hierarchical clustering, replicate-group CV analysis, correlation analysis, and case–control paired analysis. We generated a compendium of 42 heterogeneous deidentified datasets with 635 samples using both RPLC and HILIC methods. There exist metabolite signatures that correspond to various phenotypes of the heterogeneous datasets, involved in several metabolic pathways. The RPLC method shows overall better reproducibility than the HILIC method for most metabolites including polar amino acids. Correlation analysis reveals high confidence metabolites irrespective of experimental systems such as methionine, phenylalanine, and taurine. We also identify homocystine, reduced glutathione, and phosphoenolpyruvic acid as highly dynamic metabolites across all case–control paired samples. Our study is expected to serve as a resource and a reference point for a systematic analysis of label-free LC–MS/MS targeted metabolomics data in both RPLC and HILIC methods with dMRM.
      datePublished:2019-07-09T00:00:00Z
      dateModified:2019-07-09T00:00:00Z
      pageStart:1
      pageEnd:13
      license:http://creativecommons.org/licenses/by/4.0/
      sameAs:https://doi.org/10.1007/s11306-019-1564-8
      keywords:
         Targeted metabolomics
         LC–MS/MS
         RPLC
         HILIC
         Measurement reliability
         Amino acids
         Metabolite dynamics
         Biochemistry
         general
         Molecular Medicine
         Cell Biology
         Developmental Biology
         Biomedicine
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            url:http://orcid.org/0000-0003-3616-5387
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                  address:
                     name:Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, USA
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            name:Costas A. Lyssiotis
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               name:Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, USA
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      name:Li Zhang
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            address:
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            name:University of Michigan
            address:
               name:Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan, Ann Arbor, USA
               type:PostalAddress
            type:Organization
      name:Costas A. Lyssiotis
      url:http://orcid.org/0000-0001-9309-6141
      affiliation:
            name:University of Michigan Medical School
            address:
               name:Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, USA
               type:PostalAddress
            type:Organization
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            address:
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            address:
               name:Department of Internal Medicine, Division of Gastroenterology and Hepatology, University of Michigan Medical School, Ann Arbor, USA
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      name:Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, USA
      name:Michigan Institute for Data Science, University of Michigan, Ann Arbor, USA
      name:Rogel Cancer Center, University of Michigan, Ann Arbor, USA
      name:Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, USA
      name:Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, USA
      name:Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, USA
      name:Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan, Ann Arbor, USA
      name:Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, USA
      name:Rogel Cancer Center, University of Michigan, Ann Arbor, USA
      name:Department of Internal Medicine, Division of Gastroenterology and Hepatology, University of Michigan Medical School, Ann Arbor, USA

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