Abstract
Introduction
The absolute quantitation of lipids at the lipidome-wide scale is a challenge but plays an important role in the comprehensive study of lipid metabolism.
Objectives
We aim to develop a high-throughput quantitative lipidomics approach to enable the simultaneous identification and absolute quantification of hundreds of lipids in a single experiment. Then, we will systematically characterize lipidome-wide changes in the aging mouse brain and provide a link between aging and disordered lipid homeostasis.
Methods
We created an in-house lipid spectral library, containing 76,361 lipids and 181,300 MS/MS spectra in total, to support accurate lipid identification. Then, we developed a response factor-based approach for the large-scale absolute quantifications of lipids.
Results
Using the lipidomics approach, we absolutely quantified 1212 and 864 lipids in human cells and mouse brains, respectively. The quantification accuracy was validated using the traditional approach with a median relative error of 12.6%. We further characterized the lipidome-wide changes in aging mouse brains, and dramatic changes were observed in both glycerophospholipids and sphingolipids. Sphingolipids with longer acyl chains tend to accumulate in aging brains. Membrane-esterified fatty acids demonstrated diverse changes with aging, while most polyunsaturated fatty acids consistently decreased.
Conclusion
We developed a high-throughput quantitative lipidomics approach and systematically characterized the lipidome-wide changes in aging mouse brains. The results proved a link between aging and disordered lipid homeostasis.
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References
Aicheler, F., Li, J., Hoene, M., Lehmann, R., Xu, G., & Kohlbacher, O. (2015). Retention time prediction improves identification in nontargeted lipidomics approaches. Analytical Chemistry, 87(15), 7698–7704. https://doi.org/10.1021/acs.analchem.5b01139.
Atherton, H. J., Gulston, M. K., Bailey, N. J., Cheng, K. K., Zhang, W., Clarke, K., et al. (2009). Metabolomics of the interaction between PPAR-alpha and age in the PPAR-alpha-null mouse. Molecular Systems Biology, 5, 259. https://doi.org/10.1038/msb.2009.18.
Broeckling, C. D., Ganna, A., Layer, M., Brown, K., Sutton, B., Ingelsson, E., et al. (2016). Enabling efficient and confident annotation of LC-MS metabolomics data through MS1 spectrum and time prediction. Analytical Chemistry, 88(18), 9226–9234. https://doi.org/10.1021/acs.analchem.6b02479.
Cajka, T., & Fiehn, O. (2014). Comprehensive analysis of lipids in biological systems by liquid chromatography-mass spectrometry. Trends in Analytical Chemistry, 61, 192–206. https://doi.org/10.1016/j.trac.2014.04.017.
Cajka, T., & Fiehn, O. (2017). LC-MS-based lipidomics and automated identification of lipids using the lipidblast in-silico MS/MS library. Methods in Molecular Biology, 1609, 149–170. https://doi.org/10.1007/978-1-4939-6996-8_14.
Cifkova, E., Holcapek, M., Lisa, M., Ovcacikova, M., Lycka, A., Lynen, F., et al. (2012). Nontargeted quantitation of lipid classes using hydrophilic interaction liquid chromatography-electrospray ionization mass spectrometry with single internal standard and response factor approach. Analytical Chemistry, 84(22), 10064–10070. https://doi.org/10.1021/ac3024476.
Cooper, D. E., Young, P. A., Klett, E. L., & Coleman, R. A. (2015). physiological consequences of compartmentalized acyl-CoA metabolism. Journal of Biological Chemistry, 290(33), 20023–20031. https://doi.org/10.1074/jbc.R115.663260.
Cutler, R. G., Kelly, J., Storie, K., Pedersen, W. A., Tammara, A., Hatanpaa, K., et al. (2004). Involvement of oxidative stress-induced abnormalities in ceramide and cholesterol metabolism in brain aging and Alzheimer’s disease. Proceedings of the National Academy of Sciences USA, 101(7), 2070–2075. https://doi.org/10.1073/pnas.0305799101.
Ejsing, C. S., Duchoslav, E., Sampaio, J., Simons, K., Bonner, R., Thiele, C., et al. (2006). Automated identification and quantification of glycerophospholipid molecular species by multiple precursor ion scanning. Analytical Chemistry, 78(17), 6202–6214. https://doi.org/10.1021/ac060545x.
Fahy, E., Subramaniam, S., Murphy, R. C., Nishijima, M., Raetz, C. R., Shimizu, T., et al. (2009). Update of the LIPID MAPS comprehensive classification system for lipids. Journal of Lipid Research, 50(Suppl), 9–14, https://doi.org/10.1194/jlr.R800095-JLR200.
Han, X. (2010). The pathogenic implication of abnormal interaction between apolipoprotein E isoforms, amyloid-beta peptides, and sulfatides in Alzheimer’s disease. Molecular Neurobiology, 41(2–3), 97–106. https://doi.org/10.1007/s12035-009-8092-x.
Han, X. (2016). Lipidomics for studying metabolism. Nature Reviews Endocrinology, 12(11), 668–679. https://doi.org/10.1038/nrendo.2016.98.
Han, X., & Gross, R. W. (2003). Global analyses of cellular lipidomes directly from crude extracts of biological samples by ESI mass spectrometry: A bridge to lipidomics. Journal of Lipid Research, 44(6), 1071–1079. https://doi.org/10.1194/jlr.R300004-JLR200.
Han, X., & Gross, R. W. (2005). Shotgun lipidomics: Electrospray ionization mass spectrometric analysis and quantitation of cellular lipidomes directly from crude extracts of biological samples. Mass Spectrometry Reviews, 24(3), 367–412. https://doi.org/10.1002/mas.20023.
Heymsfield, S. B., Hu, H. H., Shen, W., & Carmichael, O. (2015). Emerging technologies and their applications in lipid compartment measurement. Trends in Endocrinology & Metabolism, 26(12), 688–698. https://doi.org/10.1016/j.tem.2015.10.003.
Ivanisevic, J., Stauch, K. L., Petrascheck, M., Benton, H. P., Epstein, A. A., Fang, M., et al. (2016). Metabolic drift in the aging brain. Aging (Albany NY), 8(5), 1000–1020. https://doi.org/10.18632/aging.100961.
Ivanisevic, J., Zhu, Z. J., Plate, L., Tautenhahn, R., Chen, S., O’Brien, P. J., et al. (2013). Toward ‘omic scale metabolite profiling: A dual separation-mass spectrometry approach for coverage of lipid and central carbon metabolism. Analytical Chemistry, 85(14), 6876–6884. https://doi.org/10.1021/ac401140h.
Kihara, A. (2014). Sphingosine 1-phosphate is a key metabolite linking sphingolipids to glycerophospholipids. Biochimica et Biophysica Acta, 1841(5), 766–772. https://doi.org/10.1016/j.bbalip.2013.08.014.
Kind, T., Liu, K. H., Lee, D. Y., DeFelice, B., Meissen, J. K., & Fiehn, O. (2013). LipidBlast in silico tandem mass spectrometry database for lipid identification. Nature Methods, 10(8), 755–758. https://doi.org/10.1038/nmeth.2551.
Kofeler, H. C., Fauland, A., Rechberger, G. N., & Trotzmuller, M. (2012). Mass spectrometry based lipidomics: An overview of technological platforms. Metabolites, 2(1), 19–38. https://doi.org/10.3390/metabo2010019.
Kuhl, C., Tautenhahn, R., Bottcher, C., Larson, T. R., & Neumann, S. (2012). CAMERA: An integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Analytical Chemistry, 84(1), 283–289. https://doi.org/10.1021/ac202450g.
Lam, S. M., Chua, G. H., Li, X. J., Su, B., & Shui, G. (2016). Biological relevance of fatty acyl heterogeneity to the neural membrane dynamics of rhesus macaques during normative aging. Oncotarget, 7(35), 55970–55989. https://doi.org/10.18632/oncotarget.11190.
Lam, S. M., Tian, H., & Shui, G. (2017). Lipidomics, en route to accurate quantitation. Biochimica et Biophysica Acta, 1862(8), 752–761. https://doi.org/10.1016/j.bbalip.2017.02.008.
Lopez-Otin, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2013). The hallmarks of aging. Cell, 153(6), 1194–1217. https://doi.org/10.1016/j.cell.2013.05.039.
Mapstone, M., Cheema, A. K., Fiandaca, M. S., Zhong, X., Mhyre, T. R., MacArthur, L. H., et al. (2014). Plasma phospholipids identify antecedent memory impairment in older adults. Nature Medicine, 20(4), 415–418. https://doi.org/10.1038/nm.3466.
Matyash, V., Liebisch, G., Kurzchalia, T. V., Shevchenko, A., & Schwudke, D. (2008). Lipid extraction by methyl-tert-butyl ether for high-throughput lipidomics. Journal of Lipid Research, 49(5), 1137–1146. https://doi.org/10.1194/jlr.D700041-JLR200.
Prince, J. T., & Marcotte, E. M. (2006). Chromatographic alignment of ESI-LC-MS proteomics data sets by ordered bijective interpolated warping. Analytical Chemistry, 78(17), 6140–6152. https://doi.org/10.1021/ac0605344.
Rohrig, F., & Schulze, A. (2016). The multifaceted roles of fatty acid synthesis in cancer. Nature Reviews Cancer, 16(11), 732–749. https://doi.org/10.1038/nrc.2016.89.
Salek, R. M., Steinbeck, C., Viant, M. R., Goodacre, R., & Dunn, W. B. (2013). The role of reporting standards for metabolite annotation and identification in metabolomic studies. Gigascience, 2(1), 13. https://doi.org/10.1186/2047-217X-2-13.
Sales, S., Knittelfelder, O., & Shevchenko, A. (2017). Lipidomics of human blood plasma by high-resolution shotgun mass spectrometry. Methods in Molecular Biology, 1619, 203–212. https://doi.org/10.1007/978-1-4939-7057-5_16.
Sandra, K., Ados, P., Vanhoenacker, S., David, G., F., & Sandra, P. (2010). Comprehensive blood plasma lipidomics by liquid chromatography/quadrupole time-of-flight mass spectrometry. Journal of Chromatography A, 1217(25), 4087–4099. https://doi.org/10.1016/j.chroma.2010.02.039.
Schwudke, D., Oegema, J., Burton, L., Entchev, E., Hannich, J. T., Ejsing, C. S., et al. (2006). Lipid profiling by multiple precursor and neutral loss scanning driven by the data-dependent acquisition. Analytical Chemistry, 78(2), 585–595. https://doi.org/10.1021/ac051605m.
Shmookler Reis, R. J., Xu, L., Lee, H., Chae, M., Thaden, J. J., Bharill, P., et al. (2011). Modulation of lipid biosynthesis contributes to stress resistance and longevity of C. elegans mutants. Aging (Albany NY), 3(2), 125–147. https://doi.org/10.18632/aging.100275.
Shui, G., Guan, X. L., Gopalakrishnan, P., Xue, Y., Goh, J. S., Yang, H., et al. (2010). Characterization of substrate preference for Slc1p and Cst26p in Saccharomyces cerevisiae using lipidomic approaches and an LPAAT activity assay. PLoS ONE, 5(8), e11956. https://doi.org/10.1371/journal.pone.0011956.
Snowden, S. G., Ebshiana, A. A., Hye, A., An, Y., Pletnikova, O., O’Brien, R., et al. (2017). Association between fatty acid metabolism in the brain and Alzheimer disease neuropathology and cognitive performance: A nontargeted metabolomic study. PLoS Medicine, 14(3), e1002266. https://doi.org/10.1371/journal.pmed.1002266.
Stein, S. E., & Scott, D. R. (1994). Optimization and testing of mass spectral library search algorithms for compound identification. Journal of the American Society for Mass Spectrometry, 5(9), 859–866. https://doi.org/10.1016/1044-0305(94)87009-8.
Sultana, R., Perluigi, M., & Allan Butterfield, D. (2013). Lipid peroxidation triggers neurodegeneration: A redox proteomics view into the Alzheimer disease brain. Free Radical Biology and Medicine, 62, 157–169. https://doi.org/10.1016/j.freeradbiomed.2012.09.027.
Sumner, L. W., Amberg, A., Barrett, D., Beale, M. H., Beger, R., Daykin, C. A., et al. (2007). Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics, 3(3), 211–221. https://doi.org/10.1007/s11306-007-0082-2.
Tautenhahn, R., Bottcher, C., & Neumann, S. (2008). Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics, 9, 504. https://doi.org/10.1186/1471-2105-9-504.
Touboul, D., & Gaudin, M. (2014). Lipidomics of Alzheimer’s disease. Bioanalysis, 6(4), 541–561. https://doi.org/10.4155/bio.13.346.
van Meer, G. (2005). Cellular lipidomics. The EMBO Journal, 24(18), 3159–3165. https://doi.org/10.1038/sj.emboj.7600798.
Wenk, M. R. (2010). Lipidomics: New tools and applications. Cell, 143(6), 888–895. https://doi.org/10.1016/j.cell.2010.11.033.
Wyss-Coray, T. (2016). Ageing, neurodegeneration and brain rejuvenation. Nature, 539(7628), 180–186. https://doi.org/10.1038/nature20411.
Xiang, Y., Lam, S. M., & Shui, G. (2015). What can lipidomics tell us about the pathogenesis of Alzheimer disease? Biological Chemistry, 396(12), 1281–1291. https://doi.org/10.1515/hsz-2015-0207.
Xu, L., Davis, T. A., & Porter, N. A. (2009). Rate constants for peroxidation of polyunsaturated fatty acids and sterols in solution and in liposomes. Journal of the American Chemical Society, 131(36), 13037–13044. https://doi.org/10.1021/ja9029076.
Yang, K., Cheng, H., Gross, R. W., & Han, X. (2009). Automated lipid identification and quantification by multidimensional mass spectrometry-based shotgun lipidomics. Analytical Chemistry, 81(11), 4356–4368. https://doi.org/10.1021/ac900241u.
Zhang, T., Chen, S., Liang, X., & Zhang, H. (2015). Development of a mass-spectrometry-based lipidomics platform for the profiling of phospholipids and sphingolipids in brain tissues. Analytical and Bioanalytical Chemistry, 407(21), 6543–6555. https://doi.org/10.1007/s00216-015-8822-z.
Zhou, Z., Tu, J., Xiong, X., Shen, X., & Zhu, Z. J. (2017). LipidCCS: Prediction of collision cross-section values for lipids with high precision to support ion mobility-mass spectrometry-based lipidomics. Analytical Chemistry, 89(17), 9559–9566. https://doi.org/10.1021/acs.analchem.7b02625.
Acknowledgements
The work is financially supported by National Natural Science Foundation of China (Grant No. 21575151). Z.-J. Z. is supported by Thousand Youth Talents Program from Government of China.
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All institutional and national guidelines for the care and use of biological samples were followed. The data were acquired in accordance with appropriate ethical requirements. No human study was involved in this work. All experiments on mice were conducted according to the protocols approved by Animal Care Committee of the Interdisciplinary Research Center on Biology and Chemistry (IRCBC), Chinese Academy of Sciences (CAS).
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Tu, J., Yin, Y., Xu, M. et al. Absolute quantitative lipidomics reveals lipidome-wide alterations in aging brain. Metabolomics 14, 5 (2018). https://doi.org/10.1007/s11306-017-1304-x
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DOI: https://doi.org/10.1007/s11306-017-1304-x