Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.
Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.
J Proteome Res. 2023 May 5;22(5):1419-1433. doi: 10.1021/acs.jproteome.2c00682. Epub 2023 Feb 24.
Dysregulated lipid metabolism underpins many chronic diseases including cardiometabolic diseases. Mass spectrometry-based lipidomics is an important tool for understanding mechanisms of lipid dysfunction and is widely applied in epidemiology and clinical studies. With ever-increasing sample numbers, single batch acquisition is often unfeasible, requiring advanced methods that are accurate and robust to batch-to-batch and interday analytical variation. Herein, an optimized comprehensive targeted workflow for plasma and serum lipid quantification is presented, combining stable isotope internal standard dilution, automated sample preparation, and ultrahigh performance liquid chromatography-tandem mass spectrometry with rapid polarity switching to target 1163 lipid species spanning 20 subclasses. The resultant method is robust to common sources of analytical variation including blood collection tubes, hemolysis, freeze-thaw cycles, storage stability, analyte extraction technique, interinstrument variation, and batch-to-batch variation with 820 lipids reporting a relative standard deviation of <30% in 1048 replicate quality control plasma samples acquired across 16 independent batches (total injection count = 6142). However, sample hemolysis of ≥0.4% impacted lipid concentrations, specifically for phosphatidylethanolamines (PEs). Low interinstrument variability across two identical LC-MS systems indicated feasibility for intra/inter-lab parallelization of the assay. In summary, we have optimized a comprehensive lipidomic protocol to support rigorous analysis for large-scale, multibatch applications in precision medicine. The mass spectrometry lipidomics data have been deposited to massIVE: data set identifiers MSV000090952 and 10.25345/C5NP1WQ4S.
脂质代谢失调是许多慢性疾病(包括心血管代谢疾病)的基础。基于质谱的脂质组学是理解脂质功能障碍机制的重要工具,广泛应用于流行病学和临床研究。随着样本数量的不断增加,单次批量采集往往不可行,需要准确且稳健的先进方法来应对批间和日间分析变异。本文提出了一种优化的综合靶向工作流程,用于血浆和血清脂质定量,结合稳定同位素内标稀释、自动化样品制备以及超高效液相色谱-串联质谱与快速极性切换,靶向 1163 种脂质,涵盖 20 个子类。该方法对常见的分析变异源具有稳健性,包括采血管、溶血、冻融循环、储存稳定性、分析物提取技术、仪器间变异和批间变异,在 16 个独立批次的 1048 个重复质控血浆样本中,有 820 种脂质的相对标准偏差<30%(总进样数=6142)。然而,溶血程度≥0.4%会影响脂质浓度,特别是磷脂酰乙醇胺(PEs)。两个相同的 LC-MS 系统之间的低仪器间变异性表明该测定法在实验室内部/间进行平行化的可行性。总之,我们优化了一个全面的脂质组学方案,以支持在精准医学中进行大规模、多批处理的严格分析。该质谱脂质组学数据已存入 massIVE:数据集标识符 MSV000090952 和 10.25345/C5NP1WQ4S。