Dataset Information

A comparison of human serum and plasma metabolites using untargeted 1H NMR spectroscopy and UPLC-MS.

ABSTRACT: INTRODUCTION:Differences in the metabolite profiles between serum and plasma are incompletely understood. OBJECTIVES:To evaluate metabolic profile differences between serum and plasma and among plasma sample subtypes. METHODS:We analyzed serum, platelet rich plasma (PRP), platelet poor plasma (PPP), and platelet free plasma (PFP), collected from 8 non-fasting apparently healthy women, using untargeted standard 1D and CPMG 1H NMR and reverse phase and hydrophilic (HILIC) UPLC-MS. Differences between metabolic profiles were evaluated using validated principal component and orthogonal partial least squares discriminant analysis. RESULTS:Explorative analysis showed the main source of variation among samples was due to inter-individual differences with no grouping by sample type. After correcting for inter-individual differences, lipoproteins, lipids in VLDL/LDL, lactate, glutamine, and glucose were found to discriminate serum from plasma in NMR analyses. In UPLC-MS analyses, lysophosphatidylethanolamine (lysoPE)(18:0) and lysophosphatidic acid(20:0) were higher in serum, and phosphatidylcholines (PC)(16:1/18:2, 20:3/18:0, O-20:0/22:4), lysoPC(16:0), PE(O-18:2/20:4), sphingomyelin(18:0/22:0), and linoleic acid were lower. In plasma subtype analyses, isoleucine, leucine, valine, phenylalanine, glutamate, and pyruvate were higher among PRP samples compared with PPP and PFP by NMR while lipids in VLDL/LDL, citrate, and glutamine were lower. By UPLC-MS, PE(18:0/18:2) and PC(P-16:0/20:4) were higher in PRP compared with PFP samples. CONCLUSIONS:Correction for inter-individual variation was required to detect metabolite differences between serum and plasma. Our results suggest the potential importance of inter-individual effects and sample type on the results from serum and plasma metabolic phenotyping studies.

SUBMITTER: Kaluarachchi M

PROVIDER: S-EPMC7122646 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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A comparison of human serum and plasma metabolites using untargeted <sup>1</sup>H NMR spectroscopy and UPLC-MS.

Kaluarachchi Manuja M Boulangé Claire L CL Karaman Ibrahim I Lindon John C JC Ebbels Timothy M D TMD Elliott Paul P Tracy Russell P RP Olson Nels C NC

Metabolomics : Official journal of the Metabolomic Society 20180213 3

<h4>Introduction</h4>Differences in the metabolite profiles between serum and plasma are incompletely understood.<h4>Objectives</h4>To evaluate metabolic profile differences between serum and plasma and among plasma sample subtypes.<h4>Methods</h4>We analyzed serum, platelet rich plasma (PRP), platelet poor plasma (PPP), and platelet free plasma (PFP), collected from 8 non-fasting apparently healthy women, using untargeted standard 1D and CPMG <sup>1</sup>H NMR and reverse phase and hydrophilic ...[more]

PMID: 30830335

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Project description:Hyperuricemia (HUA) as a metabolic disease is closely associated with metabolic disorders. The etiology and pathogenesis of HUA are not fully understood, so there is no radical cure so far. Metabolomics, a specialized study of endogenous small molecule substances, has become a powerful tool for metabolic pathway analysis of selected differential metabolites, which is helpful for initially revealing possible development mechanisms of various human diseases. Twenty HUA patients and 20 healthy individuals participated in the experiment, and ultrahigh performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UPLC-Q-TOF/MS) was employed to investigate serum samples to find differential metabolites. The statistical techniques used were principal component analysis and orthogonal partial least-squares discriminant analysis. The differences in metabolomics results of samples after pretreatment with different solvents were compared, 38, 20, 26, 28, 33, 50, and 40 potential differential metabolites were found, respectively, in HUA patient samples, and each group involved different metabolic pathways. Repetitive metabolites were removed, 138 differential metabolites in HUA serum were integrated for analysis, and the human body was affected by 7 metabolic pathways of glycerophospholipid metabolism, sphingolipid metabolism, arachidonic acid metabolism, linoleic acid metabolism, phenylalanine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, and α-linolenic acid metabolism. In this work, the metabolomics approach based on UPLC-Q-TOF/MS was employed to investigate serum metabolic changes in HUA patients, 138 potential differential metabolites related to HUA were identified, which provided associations of lipids, amino acids, fatty acids, organic acids, and nucleosides profiles of HUA individuals. Metabolic pathways involved in glycerophospholipid metabolism, sphingolipid metabolism, arachidonic acid metabolism, linoleic acid metabolism, phenylalanine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, and a-linolenic acid metabolism shed light on the understanding of the etiology and pathogenesis process of HUA.

Project description:INTRODUCTION: Differences in the metabolite profiles between serum and plasma are incompletely understood. </br> OBJECTIVES: To evaluate metabolic profile differences between serum and plasma and among plasma sample subtypes. </br> METHODS: We analyzed serum, platelet rich plasma (PRP), platelet poor plasma (PPP), and platelet free plasma (PFP), collected from 8 non-fasting apparently healthy women, using untargeted standard 1D and CPMG 1H NMR and reverse phase and hydrophilic (HILIC) UPLC-MS. Differences between metabolic profiles were evaluated using validated principal component and orthogonal partial least squares discriminant analysis. </br> RESULTS: Explorative analysis showed the main source of variation among samples was due to inter-individual differences with no grouping by sample type. After correcting for inter-individual differences, lipoproteins, lipids in VLDL/LDL, lactate, glutamine, and glucose were found to discriminate serum from plasma in NMR analyses. In UPLC-MS analyses, lysophosphatidylethanolamine (lysoPE)(18:0) and lysophosphatidic acid(20:0) were higher in serum, and phosphatidylcholines (PC)(16:1/18:2, 20:3/18:0, O-20:0/22:4), lysoPC(16:0), PE(O-18:2/20:4), sphingomyelin(18:0/22:0), and linoleic acid were lower. In plasma subtype analyses, isoleucine, leucine, valine, phenylalanine, glutamate, and pyruvate were higher among PRP samples compared with PPP and PFP by NMR while lipids in VLDL/LDL, citrate, and glutamine were lower. By UPLC-MS, PE(18:0/18:2) and PC(P-16:0/20:4) were higher in PRP compared with PFP samples. </br> CONCLUSIONS: Correction for inter-individual variation was required to detect metabolite differences between serum and plasma. Our results suggest the potential importance of inter-individual effects and sample type on the results from serum and plasma metabolic phenotyping studies. </br></br> UPLC-MS protocols and data are reported in the current study MTBLS465. </br> NMR protocols and data associated to this study are reported in MTBLS470. </br><br/> Linked Studies: <a href='https://www.ebi.ac.uk/metabolights/MTBLS470' target='_blank'><span class='label label-success'>MTBLS470</span></a>

Project description:INTRODUCTION: Differences in the metabolite profiles between serum and plasma are incompletely understood. </br> OBJECTIVES: To evaluate metabolic profile differences between serum and plasma and among plasma sample subtypes. </br> METHODS: We analyzed serum, platelet rich plasma (PRP), platelet poor plasma (PPP), and platelet free plasma (PFP), collected from 8 non-fasting apparently healthy women, using untargeted standard 1D and CPMG 1H NMR and reverse phase and hydrophilic (HILIC) UPLC-MS. Differences between metabolic profiles were evaluated using validated principal component and orthogonal partial least squares discriminant analysis. </br> RESULTS: Explorative analysis showed the main source of variation among samples was due to inter-individual differences with no grouping by sample type. After correcting for inter-individual differences, lipoproteins, lipids in VLDL/LDL, lactate, glutamine, and glucose were found to discriminate serum from plasma in NMR analyses. In UPLC-MS analyses, lysophosphatidylethanolamine (lysoPE)(18:0) and lysophosphatidic acid(20:0) were higher in serum, and phosphatidylcholines (PC)(16:1/18:2, 20:3/18:0, O-20:0/22:4), lysoPC(16:0), PE(O-18:2/20:4), sphingomyelin(18:0/22:0), and linoleic acid were lower. In plasma subtype analyses, isoleucine, leucine, valine, phenylalanine, glutamate, and pyruvate were higher among PRP samples compared with PPP and PFP by NMR while lipids in VLDL/LDL, citrate, and glutamine were lower. By UPLC-MS, PE(18:0/18:2) and PC(P-16:0/20:4) were higher in PRP compared with PFP samples. </br> CONCLUSIONS: Correction for inter-individual variation was required to detect metabolite differences between serum and plasma. Our results suggest the potential importance of inter-individual effects and sample type on the results from serum and plasma metabolic phenotyping studies. </br></br> NMR protocols and data are reported in the current study MTBLS470. </br> UPLC-MS protocols and data associated to this study are reported in MTBLS465. </br><br/> Linked Studies: <a href='https://www.ebi.ac.uk/metabolights/MTBLS465' target='_blank'><span class='label label-success'>MTBLS465</span></a>

Project description:INTRODUCTION:When analyzing the human plasma metabolome with Nuclear Magnetic Resonance (NMR) spectroscopy, the Carr-Purcell-Meiboom-Gill (CPMG) experiment is commonly employed for large studies. However, this process can lead to compromised statistical analyses due to residual macromolecule signals. In addition, the utilization of Trimethylsilylpropanoic acid (TSP) as an internal standard often leads to quantification issues, and binning, as a spectral summarization step, can result in features not clearly assignable to metabolites. OBJECTIVES:Our aim was to establish a new complete protocol for large plasma cohorts collected with the purpose of describing the comparative metabolic profile of groups of samples. METHODS:We compared the conventional CPMG approach to a novel procedure that involves diffusion NMR, using the Longitudinal Eddy-Current Delay (LED) experiment, maleic acid (MA) as the quantification reference and peak picking for spectral reduction. This comparison was carried out using the ultrafiltration method as a gold standard in a simple sample classification experiment, with Partial Least Squares-Discriminant Analysis (PLS-DA) and the resulting metabolic signatures for multivariate data analysis. In addition, the quantification capabilities of the method were evaluated. RESULTS:We found that the LED method applied was able to detect more metabolites than CPMG and suppress macromolecule signals more efficiently. The complete protocol was able to yield PLS-DA models with enhanced classification accuracy as well as a more reliable set of important features than the conventional CPMG approach. Assessment of the quantitative capabilities of the method resulted in good linearity, recovery and agreement with an established amino acid assay for the majority of the metabolites tested. Regarding repeatability,?~?85% of all peaks had an adequately low coefficient of variation (<?30%) in replicate samples. CONCLUSION:Overall, our comparison yielded a high-throughput untargeted plasma NMR protocol for optimized data acquisition and processing that is expected to be a valuable contribution in the field of metabolic biomarker discovery.

Dataset Information

A comparison of human serum and plasma metabolites using untargeted 1H NMR spectroscopy and UPLC-MS.

Publications

A comparison of human serum and plasma metabolites using untargeted <sup>1</sup>H NMR spectroscopy and UPLC-MS.

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