Abstract
Reversed-phase UHPLC-MS is extensively employed for both the profiling of biological fluids and tissues to characterize lipid dysregulation in disease and toxicological studies. With conventional LC-MS systems the chromatographic performance and throughput are limited due to dispersion from the fluidic connections as well as radial and longitudinal thermal gradients in the LC column. In this study vacuum jacketed columns (VJC), positioned at the source of the mass spectrometer, were applied to the lipidomic analysis of plasma extracts. Compared to conventional UHPLC, the VJC-based methods offered greater resolution, faster analysis, and improved peak intensity. For a 5 min VJC analysis, the peak capacity increased by 66%, peak tailing reduced by up to 34%, and the number of lipids detected increased by 30% compared to conventional UHPLC. The narrower peaks, and thus increased resolution, compared to the conventional system resulted in a 2-fold increase in peak intensity as well a significant improvement in MS and MS/MS spectral quality resulting in a 22% increase in the number of lipids identified. When applied to mouse plasma samples, reproducibility of the lipid intensities in the pooled QC ranged from 1.8-12%, with no related drift in tR observed.
Highlights
As is well accepted, lipids form a class of biological molecules with many important roles and functions such as energy storage, cellular signaling, and the pathophysiology of a broad spectrum of diseases including cancer, neurodegenerative diseases, infections, diabetes, etc.[1,2] Lipidomics, which involves the comprehensive analysis of lipids of all types, can be used to detect and identify thousands of lipids across the eight common lipid classes
The development of such lipid-based panels usually relies upon extensive sample preparation and lengthy chromatographic analysis combined with high-resolution mass spectrometry (HRMS).[5]
We demonstrate the application of vacuum jacketed columns (VJC)-based UHPLC-MS to the analysis of lipids in the NIST 1950 human plasma, a bovine liver extract and samples of mouse plasma obtained following the administration of gefitinib, an EGFR inhibitor used for certain breast, lung, and other cancers
Summary
Lipids form a class of biological molecules with many important roles and functions such as energy storage, cellular signaling, and the pathophysiology of a broad spectrum of diseases including cancer, neurodegenerative diseases, infections, diabetes, etc.[1,2] Lipidomics, which involves the comprehensive analysis of lipids of all types, can be used to detect and identify thousands of lipids across the eight common lipid classes. Meikle et al showed that lipidomics could be employed for risk prediction in diabetic cardiovascular disease[3] and Eghlimi et al employed LC-MS/MS-based analysis to create two targeted lipid panels for triple negative breast cancer (TNBC).[4] a 19-lipid biomarker panel was found to be capable of distinguishing TNBC (and ES-TNBC) from controls while a 5-lipid biomarker panel enabled the differentiation of TNBC from non-TNBC The development of such lipid-based panels usually relies upon extensive sample preparation and lengthy chromatographic analysis combined with high-resolution mass spectrometry (HRMS).[5] While this mode of analysis is ideal for discovery science, the long analysis times employed generally make it less suitable for high-throughput, large cohort, analysis. The increased interest in employing lipidomic analysis in large scale drug discovery, clinical, and epidemiological studies means that there is a need for robust highthroughput, sensitive, and highly reproducible methodologies which can be used for longitudinal studies and can be readily transferred between laboratories.[6]
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