The goal of this work was to develop a label-free, comprehensive, and reproducible high-resolution liquid chromatography-mass spectrometry (LC-MS)-based untargeted lipidomic workflow using a single instrument, which could be applied to biomarker discovery in both basic and clinical studies. For this, we have (i) optimized lipid extraction and elution to enhance coverage of polar and nonpolar lipids as well as resolution of their isomers, (ii) ensured MS signal reproducibility and linearity, and (iii) developed a bioinformatic pipeline to correct remaining biases. Workflow validation is reported for 48 replicates of a single human plasma sample: 1124 reproducible LC-MS signals were extracted (median signal intensity RSD = 10%), 50% of which are redundant due to adducts, dimers, in-source fragmentation, contaminations, or positive and negative ion duplicates. From the resulting 578 unique compounds, 428 lipids were identified by MS/MS, including acyl chain composition, of which 394 had RSD < 30% inside their linear intensity range, thereby enabling robust semiquantitation. MS signal intensity spanned 4 orders of magnitude, covering 16 lipid subclasses. Finally, the power of our workflow is illustrated by a proof-of-concept study in which 100 samples from healthy human subjects were analyzed and the data set was investigated using three different statistical testing strategies in order to compare their capacity in identifying the impact of sex and age on circulating lipids.
Keywords: WGCNA; annotation; coverage; lipid isomers; lipidomics; lipids; mass spectrometry; reproducibility; untargeted; workflow.