{"id":321,"date":"2026-05-05T17:47:52","date_gmt":"2026-05-05T08:47:52","guid":{"rendered":"https:\/\/www.med.osaka-u.ac.jp\/pub\/sysmet\/?p=321"},"modified":"2026-05-05T17:52:46","modified_gmt":"2026-05-05T08:52:46","slug":"development-of-a-quantitative-lipidomics-method","status":"publish","type":"post","link":"https:\/\/www.med.osaka-u.ac.jp\/pub\/sysmet\/en\/development-of-a-quantitative-lipidomics-method\/","title":{"rendered":"Quantitative Lipidomics Method"},"content":{"rendered":"\n
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\u3000Lipids, along with carbohydrates and proteins, are known as the three major macronutrients. They are essential for sustaining life and physical activity, serving as the primary components of biological membranes, a source of energy, and regulators of neural transmission and immune defense. Since lipids consist of various types of fatty acids linked to alcohols (glycerol, sphingosine, sterols) via ester or amide bonds, it is estimated that tens of thousands of different lipid molecules exist in theory. It has been suggested that differences in the composition of these lipid molecules are involved in various diseases, and the identification and quantification of individual lipid molecules are essential for understanding their biological functions and metabolic regulation. In fact, lipidomics\u2014the comprehensive and quantitative analysis of individual lipid molecules\u2014is attracting attention in various research fields, including medical research.
\u3000Because there are a vast number of lipid molecules in the body due to the diversity of polar heads and fatty acid side chains, advanced analytical techniques are required to measure these lipid molecules comprehensively and accurately. An electrospray ionization mass spectrometer (ESI-MS) is a device capable of simultaneously measuring ionized compounds and can be coupled with various types of chromatographs that separate compounds based on differences in elution time. However, a major issue with ESI-MS is that ionization is suppressed by contaminants that elute simultaneously with the target compound. Because the impurities eluting simultaneously with the target compound vary depending on the separation conditions of the chromatograph, completely different quantitative values were calculated at each research facility, making it impossible to accumulate data\u00b9). The Baba Laboratory has developed a novel analytical method that enables comprehensive quantification of lipid molecules in living organisms by optimizing the separation and analysis conditions of the chromatograph and mass spectrometer\u00b2 (Figure 1).<\/p>\n\n\n\n

Figure 1. Strategy for the quantitative analysis of biological lipid molecules<\/p>\n\n\n\n

To perform quantification using ESI-MS, internal standards corresponding to each lipid molecule must be added to correct for suppression of ionization; however, it is practically impossible to obtain internal standards for all lipid molecules. First, using lipid synthesis standards, we confirmed that lipid molecules within the same lipid class (lipid molecules sharing a common backbone, such as glycerol, or a common polar head group, such as phosphocholine) ionize with similar efficiency. We therefore optimized conditions using supercritical fluid chromatography (SFC) to separate each lipid class based on differences in elution time, and performed measurements by adding internal standards not found in the body for each lipid class. The results showed that quantitative values could be calculated with an accuracy of 64.9% to 103.5% for all lipid classes. Furthermore, the daily variation was kept within 10% for almost all lipid classes, allowing us to overcome the issues related to quantification.<\/p>\n\n\n\n

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Figure 1. Strategy for the quantitative analysis of biological lipid molecules<\/p>\n\n\n\n

\u3000To perform quantification using ESI-MS, internal standards corresponding to each lipid molecule must be added to correct for suppression of ionization; however, it is practically impossible to obtain internal standards for all lipid molecules. First, using lipid synthesis standards, we confirmed that lipid molecules within the same lipid class (lipid molecules sharing a common backbone, such as glycerol, or a common polar head group, such as phosphocholine) ionize with similar efficiency. We therefore optimized conditions using supercritical fluid chromatography (SFC) to separate each lipid class based on differences in elution time, and performed measurements by adding internal standards not found in the body for each lipid class. The results showed that quantitative values could be calculated with an accuracy of 64.9% to 103.5% for all lipid classes. Furthermore, the daily variation was kept within 10% for almost all lipid classes, allowing us to overcome the issues related to quantification.
\u3000When chromatography elutes lipids of the same class simultaneously, the separation of lipid molecules within that class must rely on mass spectrometry. Therefore, we applied separation using the multiple reaction monitoring (MRM) mode of a triple quadrupole mass spectrometer (QqQ-MS). In MRM mode, ionized lipid molecules are selected, cleaved by an inert gas, and then the resulting fragments are further selected for detection. By detecting fragments derived from the constituent fatty acids cleaved from individual lipid molecules, we were able to identify individual lipid molecules, including their structural isomers (Figure 2).<\/p>\n\n\n\n

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Figure 2. Quantitative analysis of lipid molecules in rabbit plasma<\/p>\n\n\n\n

\u3000In MRM mode, the target compounds must be determined in advance. To apply this analytical method to all biological samples, we created an in-house lipid MRM library containing lipid molecules found in living organisms. First, we screen for lipid molecules in biological samples using the in-house lipid MRM library. By reconstructing the MRM method based on the detected lipid molecules, we have successfully achieved quantitative lipid analysis for all biological samples. This method is being utilized in various collaborative research projects and is expected to play a role in elucidating new biological functions in the future.<\/p>\n\n\n\n

References<\/p>\n\n\n\n

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  1. J. A. Bowden, A. Heckert, C. Z. Ulmer, C. M. Jones, J. P. Koelmel, L. Abdullah, L. Ahonen, Y. Alnouti, A. Armando, J. M. Asara, T. Bamba, J. R. Barr, J. Bergquist, C. H. Borchers, J. Brandsma, S. B. Breitkopf, T. Cajka, A. Cazenave-Gassiot, A. Checa, M. A. Cine, R. A. Colas, S. Cremers, E. A. Dennis, J. E. Evans, A. Fauland, O. Fiehn, M. S. Gardner, T. J. Garrett, K. H. Gotlinger, J. Han, Y. Huang, A. H. Neo, T. Hyotylainen, Y. Izumi, H. Jiang, H. Jiang, J. Jiang, M. Kachman, R. Kiyonami, K. Klavins, C. Klose, H. C. Kofeler, J. Kolmert, T. Koal, G. Koster, Z. Kuklenyik, I. J. Kurland, M. Leadley, K. Lin, K. R. Maddipati, D. McDougall, P. J. Meikle, N. A. Mellett, C. Monnin, M. A. Moseley, R. Nandakumar, M. Oresic, R. E. Patterson, D. Peake, J. S. Pierce, M. Post, A. D. Postle, R. Pugh, Y. Qui, O. Quehenberger, P. Ramrup, J. Rees, B. Rembiesa, D. Reynaud, M. R. Roth, S. Sales, K. Schuhmann, M. L. Schwartzman, C. N. Serhan, A. Shevchenko, S. E. Somerville, L. St. John-Williams, M. A. Surma, H. Takeda, R. Thakare, J. W. Thompson, F. Torta, A. Triebl, M. Trotzmuller, S. J. K. Ubhayasekera, D. Vuckovic, J. M. Weir, R. Welti, M. R. Wenk, C. E. Wheelock, L. Yao, M. Yuan, X. H. Zhao, S. Zhou: Harmonizing Lipidomics: NIST Interlaboratory Comparison Exercise for Lipidomics using Standard Reference Material 1950 Metabolites in Frozen Human Plasma. J. Lipid Res. 58. 2275\u20122288 (2017).
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  2. H. Takeda, Y. Izumi, M. Takahashi, T. Paxton, S. Tamura, T. Koike, Y. Yu, N. Kato, K. Nagase, M. Shiomi, T. Bamba: Widely-targeted quantitative lipidomics method by supercritical fluid chromatography triple quadrupole mass spectrometry, J. Lipid Res. 59. 1283\u20121293 (2018).<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    \u3000Lipids, along with carbohydrates and proteins, are known as the three major macronutrients. They are essentia […]<\/p>\n","protected":false},"author":1,"featured_media":81,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"sns_share_botton_hide":"","vkExUnit_sns_title":"","_vk_print_noindex":"","sitemap_hide":"","_veu_custom_css":"","veu_display_promotion_alert":"common","vkexunit_cta_each_option":"","footnotes":""},"categories":[35,33],"tags":[],"class_list":["post-321","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-development-en","category-research-en"],"veu_head_title_object":{"title":"","add_site_title":""},"_links":{"self":[{"href":"https:\/\/www.med.osaka-u.ac.jp\/pub\/sysmet\/wp-json\/wp\/v2\/posts\/321","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.med.osaka-u.ac.jp\/pub\/sysmet\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.med.osaka-u.ac.jp\/pub\/sysmet\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.med.osaka-u.ac.jp\/pub\/sysmet\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.med.osaka-u.ac.jp\/pub\/sysmet\/wp-json\/wp\/v2\/comments?post=321"}],"version-history":[{"count":2,"href":"https:\/\/www.med.osaka-u.ac.jp\/pub\/sysmet\/wp-json\/wp\/v2\/posts\/321\/revisions"}],"predecessor-version":[{"id":325,"href":"https:\/\/www.med.osaka-u.ac.jp\/pub\/sysmet\/wp-json\/wp\/v2\/posts\/321\/revisions\/325"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.med.osaka-u.ac.jp\/pub\/sysmet\/wp-json\/wp\/v2\/media\/81"}],"wp:attachment":[{"href":"https:\/\/www.med.osaka-u.ac.jp\/pub\/sysmet\/wp-json\/wp\/v2\/media?parent=321"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.med.osaka-u.ac.jp\/pub\/sysmet\/wp-json\/wp\/v2\/categories?post=321"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.med.osaka-u.ac.jp\/pub\/sysmet\/wp-json\/wp\/v2\/tags?post=321"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}