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Updated: Mar 18, 2026

Quantitative Analysis of the Cellular Lipidome of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry
Published on: March 8, 2020
High Coverage Quantitative Lipidomic Analysis for Multiple Biological Matrices Using Ultrahigh-Performance
Qinsheng Chen1, Chenhan Zhang1, Xiaoli Zhang1
1State Key Laboratory of Genetics and Development of Complex Phenotypes, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
None:
The lipid composition (lipidome) in biological samples is extremely complex, having diverse biofunctions. Quantifying lipidomes with high coverage is vital to understand such functions but challenging due to their levels spanning several orders of magnitude, limited available standards, and poor chromatographic performances for many acidic lipids such as sphingosine-1-phosphate, phosphatidylserines, and phosphatidic acids. Here, we report a reliable method for high-coverage quantitative lipidomics using ultrahigh-performance liquid chromatography and tandem mass spectrometry (UHPLC-MS/MS). By using both pH and ammonium gradients in elution, all lipids, especially acidic ones, had obviously improved LC separation. By using 267 lipid standards in 49 subclasses, we also established quantitative structure-retention relationship models to predict the retention time (tR) with good accuracy (ΔtR < 0.33 min, MRE ∼3.4%) for all lipid subclasses. With UHPLC-MS/MS in multiple-reaction monitoring mode, we subsequently developed a quantitative lipidomics method using three UHPLC conditions to enable coverage of over 21,700 lipids in 190 subclasses with good sensitivity, precision, accuracy and stability. We further confirmed its applicability by quantifying 2375 lipids in seven typical biological matrices including human plasma, urine, and non-small-cell lung cancer cells together with E. coli, Arabidopsis leaves, mouse liver tissue, and feces. This offers a high-coverage quantitative method for understanding molecular phenotypes associated with lipid functions in physiology and pathophysiology.

