Establishing an Untargeted Lipidomics Workflow for Cellular Analysis: Insights into Endothelial Cell Function in Anaphylaxis

María Isabel Delgado Dolset^1,2Andrea Escolar-Peña²Sergio Fernández-Bravo³René Neuhaus¹Rosario Gonzalez-Mendiola^4,5Coral Barbas¹Domingo Barber²Jose Julio Laguna^4,5Maria M Escribese²Vanesa Esteban^3,5Alma Villaseñor^1*

• ¹Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Spain
• ²Departamento de Ciencias Médicas Básicas, Facultad de Medicina, Instituto de Medicina Molecular Aplicada – Nemesio Díez (IMMA-ND), Universidad San Pablo-CEU, CEU Universities, Madrid, Spain
• ³Department of Allergy and Immunology, Instituto de Investigacion Sanitaria de la Fundacion Jimenez Diaz, Madrid, Spain
• ⁴Allergy Unit, Allergo-Anaesthesia Unit, Hospital Central de La Cruz Roja San Jose y Santa Adela, Madrid, Spain
• ⁵Faculty of Biomedical and Health Sciences, Universidad Alfonso X el Sabio, Villanueva de la Cañada, Spain

Cell lipidomics presents several challenges regarding analyzing limited cell populations and distinguishing cellular metabolites from background signals originated from a stimuli. We have developed a novel workflow for untargeted cell lipidomics analysis. To study the impact of varying input cell numbers on the outcomes of untargeted cell lipidomics analysis, CD3+ cells isolated from a healthy donor at 6 different cell counts (50k, 100k, 250k, 500k, 750k, and 1M) were analyzed by liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (LC-QTOF-MS) in positive and negative electrospray ionization (ESI+ and ESI-, respectively) modes. After data quality assurance (QA), Spearman correlation analyses were carried out to select chemical signals derived from cells (ρ ≥ 0.7, p-value < 0.05). Then, this methodology was applied to human microvascular dermal endothelial cells (HMVEC-d), where a cell number calibration curve including 4 cell counts (25k, 50k, 75k, and 100k) was incorporated alongside the experimental samples to enable the analysis of cell-derived chemical signals. Here, the lipid response of HMVEC-d after contact with sera from patients at baseline and during the acute stage of anaphylaxis triggered by three different mechanisms was explored. For the CD3+ model, after correlation analyses, we found that the widest cell count interval considered for correlation analyses (50k-to-1M; k=70) showed clear clustering by cell number. The principal component analysis (PCA) models for ESI+ showed that for this cell count interval, the first component explained over 90% of the variance among samples. After applying the same methodology to HMVEC-d, we found k=157 and k=278 correlated chemical signals for ESI+ and ESI-in the cell curve (25k-100k). Statistical analysis identified 111 chemical signals that significantly (p-value < 0.05 and p-adjusted value < 0.2) differed between the acute and baseline stages of anaphylaxis. Without this correlation approach, 65 additional chemical signals would have been selected as significant. 75 unique lipids were annotated, including fatty acids, acyl carnitines, glycerophospholipids, and sphingolipids, all increased in the acute phase. These changes were associated with sphingolipid and glycosphingolipid metabolism, and ceramide and phospholipid signaling pathways. This workflow for cell lipidomics allows the selection of lipids derived from the intracellular content regardless external sources.