Normalization approaches for extracellular vesicle-derived lipidomic fingerprints – A human milk case study

Abstract

Human milk (HM) extracellular vesicles (EVs) are nano-sized, cell-derived particles sheathed in a lipid bilayer that encase specific cargo for delivery from mother to infant. The aim of this study was to expand our understanding of the lipidomic fingerprint of HM-EVs, with a specific focus on the impact of data normalization using simulated and experimental data obtained from the analysis of HM samples from mothers of preterm (N = 5) and term infants (N = 5), and a pool of donor human milk from 20 mothers (before and after pasteurization). EVs were isolated by multi-stage ultracentrifugation and characterized in terms of total protein content, total particle count and size, surface tetraspanin profile and protein markers, and morphology. Lipidomic analysis after single-phase extraction was performed by liquid chromatography mass spectrometry (LC-MS). The effect of widely used data normalization strategies (i.e., sample volume, particle count, protein content, total lipids signal) was compared. Results show that for the selection of the optimum normalization approach, the specific study aims, as well as the purity and homogeneity of size distribution of EV isolates should be considered. While normalization attending the particle number can be useful for between sample comparisons in EV populations with similar particle size, normalization to total lipid content is preferred when lipid contamination is encountered. Our findings exemplify the need for guidance with respect to data processing in LC-MS-based lipidomics studies of EVs.