Quantification of Diacylglycerol Species from Cellular Extracts by Electrospray Ionization Mass Spectrometry Using a Linear Regression Algorithm

Abstract

Diacylglycerols (DAGs) play significant roles in both intermediate metabolism and signal transduction. These lipid species are second messengers involved in modulating a plethora of cellular processes. Evaluation of DAG species concentrations has been hampered by the lack of a reliable method for molecular species analysis within a complex mixture of cellular lipids. We describe a new method for quantitative analysis of DAG species from complex biological extracts based on positive mode electrospray ionization mass spectrometry without prior derivatization. Quantification is achieved using internal standards and calibration curves constructed by spiking cell extracts with different concentrations of DAG species containing various acyl chain lengths and degrees of unsaturation. The new mass spectral data processing algorithm incorporates a multiple linear regression model including a factor accountable for possible interactions between experimental preparations and the slope of the curve for the standards, allowing the examinations of the effects of sample origin conditions (such as cell types, phenotypes, etc.) and instrument variability on this slope. Internal standards provide a basis for quantification of 28 DAG molecular species detected in RAW 264.7 cells after stimulation of a G-protein coupled receptor with platelet activating factor. This method displays excellent reproducibility over the established range of concentrations with variations of < or =10% and is highly sensitive with a detection limit of 0.1-0.4 pmol/microL depending upon acyl chain composition. We have shown differential effects on various DAGs in response to a ligand which illustrates the importance of examining lipids at the molecular species level rather than as a single homogeneous entity.