Blood and Plasma-based Lipid Profiling Reveals Distinctive Metabolic Changes in Systemic Lupus Erythematosus and Systemic Sclerosis

Lipids were separated by a C18 reverse phase column, using an Ascentis® Express 90 Å C18 HPLC column (15 cm x 2.1 mm; 2.7 μm, Supelco®) inserted into an HPLC system (Ultimate 3000 Dionex, Thermo Fisher Scientific, Bremen, Germany) with an autosampler coupled online to a Q-Exactive™ Hybrid Quadrupole-Orbitrap™ Mass Spectrometer (Thermo Fisher Scientific, Bremen, Germany). A volume of 5 μL of each sample mixture was injected into the HPLC column, at a flow rate of 260 μL/min. The temperature of the column oven was maintained at 50 °C. Elution started with 32% of mobile phase B, and the gradient used was: 45% B (1.5 min), 52% B (4 min), 58% B (5 min), 66% B (8 min), 70% B (11 min), 85% B (14 min), 97% B (18 min, maintained for 7 min), and 32% B (25.01 min, followed by a re-equilibration period of 8 min prior next injection).The Q-Exactive™ orbitrap mass spectrometer with a heated electrospray ionization source was operated in the positive mode (electrospray voltage of 3.0 kV) and negative mode (electrospray voltage of -2.7 kV). The sheath gas flow was 35 U, the auxiliary gas was 3 U, the capillary temperature was 320 °C, the S-lenses RF was 50 U and the probe`s temperature was 300 °C. Full scans MS spectra were acquired both in positive and negative modes in an m/z range of 300-1600, with a resolution of 70.000, automatic gain control (AGC) target of 3x10E6 and maximum injection time of 100 ms. For tandem MS (MS/MS) experiments, a top-10 data-dependent method was used. The top 10 most abundant precursor ions in full MS were selected to be fragmented in the collision cell HCD, with the dynamic exclusion of 30 s and an intensity threshold of 8x10E4. The MS/MS spectra were obtained with a resolution of 17,500; an AGC target of 1x10E5; an isolation window of 1 m/z; and a maximum injection time of 100 ms. A stepped normalized collision energy™ scheme was used and ranged between 25 and 30 eV for the positive ion mode and between 20, 24 and 28 for the negative ion mode. The MS/MS spectra obtained were those combining the information obtained with the different collision energies applied to each ionization mode. Data acquisition was carried out using the Xcalibur data system (V3.3, Thermo Fisher Scientific, USA).PC, LPC and SM were analysed in the LC-MS spectra in the positive ion mode, as [M+H]+ ions. The presence of the fragment ion at m/z 184, corresponding to the phosphocholine polar head group, in the MS/MS of [M+H]+ ions allows identifying PL molecular species belonging to the PC, LPC and SM classes, which were further differentiated by the characteristic retention times. The identification of PC, LPC and SM classes was confirmed in the LC-MS spectra in the negative ion mode, as formate adducts ([M+HCOO]- ions). MS/MS spectra of [M+HCOO]- ions of these three PL classes should display the typical fragment ion at m/z 168 (phosphocholine polar head group minus a methyl moiety). Carboxylate anions of fatty acyl chains can also be seen for PC and LPC. PE and LPE classes were analysed in negative ion mode ([M−H]- ions). The fragment ion at m/z 140 (phosphoethanolamine polar head group) and the carboxylate anions of fatty acyl chains can be found in the MS/MS data from negative ion mode. PI and PS species were analysed in negative ion mode, as [M−H]- ions. The presence of the fragment ion at m/z 241, corresponding to the phosphoinositol polar head group, in the MS/MS of [M-H]- ions, allows the identification of PI molecular species. PS species were identified in the MS/MS of [M-H]- ions by the neutral loss of -87Da from the molecular ion. The identification of the remaining lipid species belonging to the classes of carnitines (CAR), ceramides (Cer), cholesteryl esters (CE, fragment ion at m/z 369), diacylglycerols (DG) and triacylglycerols (TG), was made in LC-MS spectra in the positive ion mode, as [M+H]+ ions (CAR and Cer), [M+NH4]+ ions (CE) and [M+NH4]+ ions (DG and TG) respectively. LC-MS data were processed using the Lipostar software (Molecular Discovery Ltd., version 2.1.1 x64). This software was used for raw data import, peak detection, and identification. Lipid assignment and identification was made against a database created from LIPID MAPS structure database (version December 2022), that was then fragmented using the DM Manager Module in Lipostar, according to Lipostar fragmentation rules. The raw files were imported directly and aligned using the settings according to Lange et al. Briefly, automatic peak picking was performed with SDA smoothing level set to high and minimum S/N ratio 3. Automatic isotope clustering settings were set to 7 ppm with an RT tolerance of 0.2 min. The MS/MS filter was applied to keep only features with MS/MS spectra for identification. Lipid identification was made according to the following parameters: 5 ppm precursor ion mass tolerance and 10 ppm product ion mass tolerance. The automatic approval was performed to keep structures with a quality of 2-4 stars. The lists with the identified and approved species results were exported and we used MZmine software (v2.42) to perform relative quantification.Relative quantification was performed by exporting the peak area values to a computer spreadsheet. For data normalization, the peak areas of the extracted ion chromatograms (XIC) of the lipid precursor ions of each class were divided by the peak areas of the internal standards selected for the class. Missing values were replaced by 1/5 of the minimum positive values detected in the data set. Univariate and multivariate statistical analyses were performed using R version 3.5.1 in Rstudio version 1.1.4. The data sets were then normalized to the internal standard, generalized log2, and EigenMS. Principal component analysis (PCA) was performed using the R libraries FactoMineR and factoextra. Heatmaps were created using the R package heatmap using “Euclidean” as the clustering distance and “ward.D” as the clustering method. The normality of the data was tested with the Shapiro−Wilk test. To test the significance of the differences between conditions, we used either the ANOVA or Kruskal−Wallis test, followed by Tukey’s or Dunn’s test, respectively, using the R package Rstatix. A p-value < 0.05 was considered an indicator of statistical significance. All graphics and boxplots were created using the R package ggplot2.