Front Immunol. 2026 ;17
1711640
María Isabel Delgado Dolset,
Andrea Escolar-Peña,
Sergio Fernández-Bravo,
Antonio J García-Cívico,
Lucía Pajares,
René Neuhaus,
Rosario González-Mendiola,
Coral Barbas,
Domingo Barber,
José Julio Laguna,
María M Escribese,
Vanesa Esteban,
Alma Villaseñor.
Background: Cell metabolomics, including lipidomics, presents several challenges regarding analyzing limited cell populations and distinguishing cellular metabolites from background signals originated from a stimuli or after a treatment. To address this, we have developed a novel workflow for untargeted cell lipidomics analysis.
Methods: 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.
Results: For the CD3+ model, we found that although 1087 chemical signals (k) passed the QA, samples did not cluster according to their cell count when taking all signals into account. After correlation analyses, 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 193 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, 67 additional chemical signals would have been selected as significant. From the 193 chemical signals, 75 unique lipids were annotated, mainly 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.
Conclusions: This workflow for cell lipidomics analysis allows the selection of lipids derived from the intracellular content regardless external sources, supporting specific intracellular metabolism profiling.
Keywords: LC-MS; cell count interval; correlations; immunometabolism; lipidomics; metabolites