Plasma Lipidomic Profiling Method Based on Ultrasound Extraction and Liquid Chromatography Mass Spectrometry

Lipidomics is an emerging field in biomedical research that includes the analysis of all the lipids present in complex biological samples. To evaluate the chemical and biological diversity of lipids, lipid extraction is usually the first step toward lipidomics analysis. Nevertheless, sample preparation is still a time-consuming and error prone analytical step. Therefore, the development of simple and robust methods suitable for high-throughput lipid analysis is of great interest. This study presents a new method for exhaustive lipid fingerprinting of human blood plasma samples based on the employment of methyl <i>tert</i>-butyl ether (MTBE) and ultrasound (US) energy combined with liquid chromatography-electrospray ionization quadrupole-time-of-flight mass spectrometry (LC–ESIqToF-MS). First, the MTBE-US extraction step was optimized by means of experimental design methodology. After the optimization step, a comparative study was performed to assess the suitability of the proposed method. The new method allowed extraction time to be reduced to half, in comparison with previously reported methods. The proposed method also allowed increasing extraction repeatability (with RSDs below 5.55%) and efficiency (recoveries higher than 70% were obtained for all lipids evaluated). Moreover, the new proposed method enables more than 800 different features to be detected. Thus, the overall number of lipids identified with the databases for this novel extraction method (352) was the highest of the evaluated methods. The efficiency, precision, and feature detection capacity of the proposed method confirmed its suitability for the evaluation of the lipid profile of human blood plasma samples. Moreover, taking into account its simplicity, low time consumption, and compatibility with automation, the new proposed method could be a suitable alternative to previously reported methods for use in laboratories for comprehensive lipidomic profiling