Improved quantitation of lipid classes using supercritical fluid chromatography with a charged aerosol detector

Hiroaki Takeda, Masatomo Takahashi, Takeshi Hara, Yoshihiro Izumi, Takeshi Bamba

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6 Citations (Scopus)

Abstract

Quantitatively and rapidly analyzing lipids is necessary to elucidate their biological functions. Herein, we developed a quantitative method for various lipid classes using supercritical fluid chromatography (SFC) coupled with a charged aerosol detector (CAD), providing high-throughput data analysis to detect a large number of molecules in each lipid class as one peak. Applying the CAD was useful for analyzing lipid molecules in the same lipid class with a constant response under the same mobile phase composition. First, we optimized the washing method for the diethylamine column, achieving baseline separation of lipid classes while maintaining good peak shapes. In addition, the CAD conditions (organic solvent evaporation and numerical correction of the CAD data) were optimized to improve the signal-to-noise ratio. We used an internal standard (ceramide phosphoethanolamine d17:1-12:0), which did not coelute with the lipid classes and showed high extraction efficiency. Based on a quantitative analysis of HepG2 cells, the concentration of lipid classes detected by CAD was adequate compared with that obtained by triple-quadrupole MS (QqQMS) in a previous study because the deviations of the concentrations were 0.6- to 2.3-fold. These results also supported the quantitative performance of SFC-QqQMS developed in our previous report.-Takeda, H., M. Takahashi, T. Hara, Y. Izumi, and T. Bamba. Improved quantitation of lipid classes using supercritical fluid chromatography with a charged aerosol detector. J. Lipid Res. 2019. 60: 1465-1474.

Original languageEnglish
Pages (from-to)1465-1474
Number of pages10
JournalJournal of Lipid Research
Volume60
Issue number8
DOIs
Publication statusPublished - 2019

All Science Journal Classification (ASJC) codes

  • Biochemistry
  • Endocrinology
  • Cell Biology

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