Cross-Platform Comparison of Untargeted and Targeted Lipidomics Approaches on Aging Mouse Plasma

Scientific Reports

Volumn 8, Issue 1, 2018, Pages

  Contrepois, Kévin ^a   Mahmoudi, Salah ^a   Ubhi, Baljit K ^b   Papsdorf, Katharina ^a   Hornburg, Daniel ^a   Brunet, Anne ^a   Snyder, Michael ^a  

^a STANFORD UNIVERSITY   (United States)

^b MDS SCIEX   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

LIPID; TRIACYLGLYCEROL;

AGING; ANIMAL; BLOOD; C57BL MOUSE; EVALUATION STUDY; LIQUID CHROMATOGRAPHY; MALE; METABOLISM; MOUSE; PLASMA; PROCEDURES; TANDEM MASS SPECTROMETRY;

AGING; ANIMALS; CHROMATOGRAPHY, LIQUID; EVALUATION STUDIES AS TOPIC; LIPIDS; MALE; MICE; MICE, INBRED C57BL; PLASMA; TANDEM MASS SPECTROMETRY; TRIGLYCERIDES;

EID: 85058185289     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/s41598-018-35807-4     Document Type: Article

References (27)

1. Yang, K. & Han, X. Lipidomics: Techniques, Applications, and Outcomes Related to Biomedical Sciences. Trends in biochemical sciences 41, 954–969, 10.1016/j.tibs.2016.08.010 (2016)
2. Shevchenko, A. & Simons, K. Lipidomics: coming to grips with lipid diversity. Nature reviews. Molecular cell biology 11, 593–598, 10.1038/nrm2934 (2010)
3. Johnson, C. H., Ivanisevic, J. & Siuzdak, G. Metabolomics: beyond biomarkers and towards mechanisms. Nature reviews. Molecular cell biology 17, 451–459, 10.1038/nrm.2016.25 (2016)
4. Yin, P. & Xu, G. Current state-of-the-art of nontargeted metabolomics based on liquid chromatography-mass spectrometry with special emphasis in clinical applications. Journal of chromatography. A 1374, 1–13, 10.1016/j.chroma.2014.11.050 (2014)
5. Contrepois, K., Jiang, L. & Snyder, M. Optimized Analytical Procedures for the Untargeted Metabolomic Profiling of Human Urine and Plasma by Combining Hydrophilic Interaction (HILIC) and Reverse-Phase Liquid Chromatography (RPLC)-Mass Spectrometry. Molecular & cellular proteomics: MCP 14, 1684–1695, 10.1074/mcp.M114.046508 (2015)
6. Contrepois, K., Liang, L. & Snyder, M. Can Metabolic Profiles Be Used as a Phenotypic Readout of the Genome to Enhance Precision Medicine? Clinical chemistry 62, 676–678, 10.1373/clinchem.2015.251181 (2016)
7. Cajka, T. & Fiehn, O. T. M. Untargeted and Targeted Methods in Mass Spectrometry-Based Metabolomics and Lipidomics. Analytical chemistry 88, 524–545, 10.1021/acs.analchem.5b04491 (2016)
8. Ubhi, B. K. et al. A Novel Lipid Screening Platform that Provides a Complete Solution for Lipidomics Research. (SCIEX Technical Application Note)
9. Chennamsetty, I. et al. Nat1 Deficiency Is Associated with Mitochondrial Dysfunction and Exercise Intolerance in Mice. Cell reports 17, 527–540, 10.1016/j.celrep.2016.09.005 (2016)
10. Breitkopf, S. B. et al. A relative quantitative positive/negative ion switching method for untargeted lipidomics via high resolution LC-MS/MS from any biological source. Metabolomics 13, 10.1007/s11306-016-1157-8 (2017)
11. Cajka, T. & Fiehn, O. Comprehensive analysis of lipids in biological systems by liquid chromatography-mass spectrometry. Trends Analyt Chem 61, 192–206, 10.1016/j.trac.2014.04.017 (2014)
12. Taguchi, R. & Ishikawa, M. Precise and global identification of phospholipid molecular species by an Orbitrap mass spectrometer and automated search engine Lipid Search. Journal of chromatography. A 1217, 4229–4239, 10.1016/j.chroma.2010.04.034 (2010)
13. Lintonen, T. P. et al. Differential mobility spectrometry-driven shotgun lipidomics. Analytical chemistry 86, 9662–9669, 10.1021/ac5021744 (2014)
14. Schneider, B. B., Covey, T. R., Coy, S. L., Krylov, E. V. & Nazarov, E. G. Planar differential mobility spectrometer as a pre-filter for atmospheric pressure ionization mass spectrometry. International journal of mass spectrometry 298, 45–54, 10.1016/j.ijms.2010.01.006 (2010)
15. Ubhi, B. K. Novel Chemical Standards Kits Enable Facile Lipid Quantitation. (SCIEX Technical Application Note)
16. Murphy, R. C., Fiedler, J. & Hevko, J. Analysis of nonvolatile lipids by mass spectrometry. Chemical reviews 101, 479–526 (2001)
17. Cajka, T., Smilowitz, J. T. & Fiehn, O. Validating Quantitative Untargeted Lipidomics Across Nine Liquid Chromatography-High-Resolution Mass Spectrometry Platforms. Analytical chemistry 89, 12360–12368, 10.1021/acs.analchem.7b03404 (2017)
18. Bowden, J. A. et al. Harmonizing lipidomics: NIST interlaboratory comparison exercise for lipidomics using SRM 1950-Metabolites in Frozen Human Plasma. Journal of lipid research 58, 2275–2288, 10.1194/jlr.M079012 (2017)
19. Quehenberger, O. et al. Lipidomics reveals a remarkable diversity of lipids in human plasma. Journal of lipid research 51, 3299–3305, 10.1194/jlr.M009449 (2010)
20. Leuthold, P. et al. Comprehensive Metabolomic and Lipidomic Profiling of Human Kidney Tissue: A Platform Comparison. Journal of proteome research 16, 933–944, 10.1021/acs.jproteome.6b00875 (2017)
21. Yet, I. et al. Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms. PloS one 11, e0153672, 10.1371/journal.pone.0153672 (2016)
22. Suhre, K. et al. Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PloS one 5, e13953, 10.1371/journal.pone.0013953 (2010)
23. Houtkooper, R. H. et al. The metabolic footprint of aging in mice. Scientific reports 1, 134, 10.1038/srep00134 (2011)
24. Quehenberger, O. & Dennis, E. A. The human plasma lipidome. The New England journal of medicine 365, 1812–1823, 10.1056/NEJMra1104901 (2011)
25. Araki, S., Okazaki, M. & Goto, S. Impaired lipid metabolism in aged mice as revealed by fasting-induced expression of apolipoprotein mRNAs in the liver and changes in serum lipids. Gerontology 50, 206–215, 10.1159/000078349 (2004)
26. Seo, C. et al. Metabolomic study of aging in mouse plasma by gas chromatography-mass spectrometry. Journal of chromatography. B, Analytical technologies in the biomedical and life sciences 1025, 1–6, 10.1016/j.jchromb.2016.04.052 (2016)
27. Miller, K. N. et al. Aging and caloric restriction impact adipose tissue, adiponectin, and circulating lipids. Aging cell 16, 497–507, 10.1111/acel.12575 (2017)