Optimization of a liquid chromatography-ion mobility-high resolution mass spectrometry platform for untargeted lipidomics and application to HepaRG cell extracts

Abstract

Analytical methods to evaluate the lipidome of biological samples need to provide high data quality to ensure comprehensive profiling and reliable structural elucidation. In this perspective, liquid chromatography-high resolution mass spectrometry (LC-HRMS) is the state-of-the-art technique for lipidomic analysis of biological samples. There are thousands of lipids in most biological samples, and therefore separation methods before introduction to the mass spectrometer is key for relative quantitation and identification. Chromatographic methods differ across laboratories, without any consensus on the best methodologies. Therefore, we designed an experiment to determine the optimal LC methodology, and assessed the value of ion mobility for an additional dimension of separation. To apply an untargeted method for hypothesis generation focused on lipidomics, LC-HRMS parameters were optimized based on the measurement of 50 panel lipids covering key human metabolic pathways. Reversed-phase liquid chromatography columns were compared based on a quality scoring system considering the signal-to-noise ratio, peak shape, and retention factor. Furthermore, drift tube ion mobility spectrometry (DTIMS) was implemented to increase peak capacity and confidence during annotation by providing collision cross section (CCS) values for the analytes under investigation. However, hyphenating DTIMS to LC-HRMS may result in a reduced sensitivity due to impaired duty cycles. To increase the signal intensity, a Box-Behnken design (BBD) was used to optimize four key factors, i.e. drift entrance voltage, drift exit voltage, rear funnel entrance, and rear funnel exit voltages. Application of a maximized desirability function provided voltages for the above-mentioned parameters resulting in higher signal intensity compared to each combination of parameters used during the BBD. In addition, the influence of single pulse and Hadamard 4-bit multiplexed modes on signal intensity was explored and different trap filling and release times of ions were evaluated. The optimized LC-DTIM-HRMS platform was applied to extracts from HepaRG cells and resulted in 3912 high-quality features (<30% median relative standard deviation; n = 6, t = 24 h). From these features, 436 lipid species could be annotated (i.e., matching based on accurate mass <5 ppm, isotopic pattern, in-silico MS/MS fragmentation, and in-silico CCS database matching <3%). The application of LC-DTIM-HRMS for untargeted analysis workflows is growing and the platform optimization, as described here, can be used to guide the method development and CCS database comparison for high confidence lipid annotation.