Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, Lausanne CH-1005, Switzerland.
Department of Biochemistry and Precision Medicine TRP, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore.
Anal Chem. 2023 Feb 14;95(6):3168-3179. doi: 10.1021/acs.analchem.2c02598. Epub 2023 Jan 30.
Lipid analysis at the molecular species level represents a valuable opportunity for clinical applications due to the essential roles that lipids play in metabolic health. However, a comprehensive and high-throughput lipid profiling remains challenging given the lipid structural complexity and exceptional diversity. Herein, we present an 'omic-scale targeted LC-MS/MS approach for the straightforward and high-throughput quantification of a broad panel of complex lipid species across 26 lipid (sub)classes. The workflow involves an automated single-step extraction with 2-propanol, followed by lipid analysis using hydrophilic interaction liquid chromatography in a dual-column setup coupled to tandem mass spectrometry with data acquisition in the timed-selective reaction monitoring mode (12 min total run time). The analysis pipeline consists of an initial screen of 1903 lipid species, followed by high-throughput quantification of robustly detected species. Lipid quantification is achieved by a single-point calibration with 75 isotopically labeled standards representative of different lipid classes, covering lipid species with diverse acyl/alkyl chain lengths and unsaturation degrees. When applied to human plasma, 795 lipid species were measured with median intra- and inter-day precisions of 8.5 and 10.9%, respectively, evaluated within a single and across multiple batches. The concentration ranges measured in NIST plasma were in accordance with the consensus intervals determined in previous ring-trials. Finally, to benchmark our workflow, we characterized NIST plasma materials with different clinical and ethnic backgrounds and analyzed a sub-set of sera ( = 81) from a clinically healthy elderly population. Our quantitative lipidomic platform allowed for a clear distinction between different NIST materials and revealed the sex-specificity of the serum lipidome, highlighting numerous statistically significant sex differences.
基于脂质在代谢健康中所扮演的重要角色，对其进行分子物种水平的分析可为临床应用提供有价值的信息。然而，由于脂质结构的复杂性和多样性，全面且高通量的脂质谱分析仍然具有挑战性。在此，我们提出了一种“组学规模的靶向 LC-MS/MS 方法，可直接且高通量地定量分析 26 种脂质（亚）类中广泛的复杂脂质物种。该工作流程包括使用 2-丙醇进行自动化单步提取，然后使用亲水相互作用液相色谱在双柱设置中进行脂质分析，并结合串联质谱以时间选择反应监测模式进行数据采集（总运行时间为 12 分钟）。分析流程包括对 1903 种脂质进行初始筛选，然后对稳健检测到的脂质进行高通量定量。通过使用 75 种具有代表性的不同脂质类别的同位素标记标准品进行单点校准来实现脂质定量，涵盖了具有不同酰基/烷基链长度和不饱和度的脂质物种。当应用于人类血浆时，使用该方法可测量 795 种脂质物种，其日内和日间精密度中位数分别为 8.5%和 10.9%，可在单个和多个批次内进行评估。在 NIST 血浆中测量的浓度范围与之前的环试验确定的共识区间一致。最后，为了对我们的工作流程进行基准测试，我们对具有不同临床和种族背景的 NIST 血浆材料进行了表征，并分析了来自临床健康老年人群的一部分血清（n=81）。我们的定量脂质组学平台能够清晰地区分不同的 NIST 材料，并揭示了血清脂质组的性别特异性，突出了许多具有统计学意义的性别差异。
