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A Metabonomic Comparison of Human Serum and Plasma Subtypes Using Untargeted NMR Spectroscopy and UPLC-MS (UPLC-MS assays)

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.

UPLC-MS protocols and data are reported in the current study MTBLS465.
NMR protocols and data associated to this study are reported in MTBLS470.

Linked Studies: MTBLS470

INSTRUMENT(S): Xevo G2 QTOF (Waters)

SUBMITTER: Manuja Kaluarachchi

PROVIDER: MTBLS465 | MetaboLights | 2018-07-25

REPOSITORIES: MetaboLights

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Project description: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. NMR protocols and data are reported in the current study MTBLS470. UPLC-MS protocols and data associated to this study are reported in MTBLS465. Linked Studies: <a href='https://www.ebi.ac.uk/metabolights/MTBLS465' target='_blank'>MTBLS465</a>

Project description:Results: MiRNA expression profiling of 1281 human miRNAs revealed relevant expression of 221 miRNAs consistently expressed in all samples at all time points. Correlation of platelet miRNA ranks was highly significant with data from other studies. Global distribution of miRNA expression was relatively similar in all subjects. No miRNA exhibited a significant effect of time at level 0.05. This finding was supported by exploratory analysis of predicted time effects from the mixed effects models. Furthermore, no significant effect of ASA could be found. Concerning the functional implication of the 20 most abundantly expressed miRNAs, we found 6 functional themes: nuclear import, cell cycle, transmembrane receptor protein serine/threonine kinase signaling pathway, regulation of kinase activity, pathways in cancer, and leukocyte differentiation. Conclusion: Our study shows that the platelet miRNA profile is remarkably stable in healthy subjects and is not affected by single-dose ASA treatment. For this reason, single-point analysis of platelet miRNA profile is reasonable when inter-individual differences are studied. The functional annotation network points toward additional extra-platelet effects of platelet miRNAs. We assessed the platelet miRNA profile blood in 5 volunteers at five time points over a time course of 10 days; 24 h prior to the last blood sampling, all subjects took 500 mg acetylsalicylic acid (ASA) once. Platelet miRNA was isolated from platelet-rich plasma which was leucocyte-depleted by negative bead separation. Then miRNA array analysis was performed on the isolated platelet miRNA. Temporal patterns and the effect of ASA were explored by a linear mixed effects model for each miRNA. For the 20 most abundantly expressed platelet miRNAs, a target gene search was performed by using miRWalk for validated and predicted targets. To visualize which molecular functions arerelated to those miRNAs, an annotation network was created by ClueGO, a Cytoscape plug-in.

Project description:The GOLDN study was initiated to assess how genetic factors interact with environmental (diet and drug) interventions to influence blood levels of triglycerides and other atherogenic lipid species and inflammation markers (registered at <a href="http://clinicaltrials.gov/ct2/show/NCT00083369">clinicaltrails.gov</a>, number NCT00083369). The study recruited Caucasian participants primarily from three-generational pedigrees from two NHLBI Family Heart Study (FHS) field centers (Minneapolis, MN and Salt Lake City, UT). Only families with at least two siblings were recruited and only participants who did not take lipid-lowering agents (pharmaceuticals or nutraceuticals) for at least 4 weeks prior to the initial visit were included. A total of 1048 GOLDN participants were included in the diet intervention. The diet intervention followed the protocol of Patsch et al. (<a href="http://www.ncbi.nlm.nih.gov/pubmed/1420093">1992</a>). The whipping cream (83% fat) meal had 700 Calories/m2 body surface area (2.93 MJ/m2 body surface area): 3% of calories were derived from protein (instant nonfat dry milk) and 14% from carbohydrate (sugar). The ratio of polyunsaturated to saturated fat was 0.06 and the cholesterol content of the average meal was 240 mg. The mixture was blended with ice and flavorings. Blood samples were drawn immediately before (fasting) and at 3.5 and 6 hours after consuming the high-fat meal. For the GOLDN lipidomics study, sterols and fatty acids were measured from stored plasma (-80 degrees Celsius) collected at fasting and 3.5 hours after the diet intervention using TrueMass Panels from Lipomics (West Sacramento, CA). A total of 11 sterols were quantified in nmols/gram of sample including total cholesterol, 7-dehydrocholesterol, desmosterol, lanosterol, lathasterol, cholestanol, coprostanol, beta-sitosterol, campesterol, stigmasterol, and 7alpha-hydroxycholesterol. A total of 35 fatty acids were quantified in nmols/gram of sample inlcuding myristic acid (14:0); pentadecanoic acid (15:0); palmitic acid (16:0); stearic acid (18:0); arachidic acid (20:0); behenic acid (22:0); lignoceric acid (24:0); myristoleic acid (14:1n5); palmitoleic acid (16:1n7); palmitelaidic acid (t16:1n7); oleic acid (18:1n9); elaidic acid (t18:1n9); vaccenic acid (18:1n7); linoleic acid (18:2n6); gamma-linolenic acid (18:3n6); alpha-linolenic acid (18:3n3); stearidonic acid (18:4n3); eicosenoic acid (20:1n9); eicosadienoic acid (20:2n6); mead acid (20:3n9); di-homo-gamma-linolenic acid (20:3n6); arachidonic acid (20:4n6); eicsoatetraenoic acid (20:4n3); eicosapentaenoic acid (20:5n3); erucic acid (22:1n9); docosadienoic acid (22:2n6); adrenic acid (22:4n6); docosapentaenoic acid (22:5n6); docosapentaenoic acid (22:5n3); docosahexaenoic acid (22:6n3); nervonic acid (24:1n9); and plasmalogen derivatives of 16:0, 18:0, 18:1n9, and 18:1n7.

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A Metabonomic Comparison of Human Serum and Plasma Subtypes Using Untargeted NMR Spectroscopy and UPLC-MS (UPLC-MS assays)

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