We present the LipidQuant 1.0 tool for automated data processing workflows in lipidomic quantitation based on lipid class separation coupled with high-resolution mass spectrometry. Lipid class separation workflows, such as hydrophilic interaction liquid chromatography or supercritical fluid chromatography, should be preferred in lipidomic quantitation due to the coionization of lipid class internal standards with analytes from the same class. The individual steps in the LipidQuant workflow are explained, including lipid identification, quantitation, isotopic correction, and reporting results. We show the application of LipidQuant data processing to a small cohort of human serum samples.

Chemicals and lipid standards

Chemicals and solvents (LC/MS grade, Chromasolv-Honeywell, Riedel-de Haën, Germany) were purchased from Sigma Aldrich (St. Louis, MI, USA) or Merck (Darmstadt, Germany). The following nonendogenous lipids were purchased from Avanti Polar Lipids (Alabaster, AL, USA) or Nu-Chek Prep (Elysian, MN, USA) and used as internal standards (IS) for the quantitative analysis: MG 19:1/0:0/0:0, DG 12:1/0:0/12:1, TG 19:1/19:1/19:1, D7-CE 16:0, Cer d18:1/12:0, D7-cholesterol, LPC 17:0/0:0, LPE 14:0/0:0, PC 14:0/14:0, PE 14:0/14:0, SM d18:1/12:0, PS 14:0/14:0, PA 14:0/14:0, PG 14:0/14:0, LPG 14:0/0:0, HexCer d18:1/12:0, Hex2Cer d18:1/12:0, and SHexCer d18:1/12:0. Carbon dioxide (scCO₂) with 99.995% purity was purchased from Messer (Bad Soden, Germany).

Samples

Human serum samples were isolated from the whole blood, drawn into tubes without anticoagulant (Sarstedt S-Monovette, Germany), incubated at room temperature for 60 min, centrifuged at 1500 × g for 15 min, the supernatant was transferred to Eppendorf tubes, and immediately frozen at ‑80°C until the extraction. The study was approved by the institutional ethical committee. All donors signed the informed consent. In total, 43 samples from female donors with an average age of 47 years and 22 samples of male donors with the average age of 44 years were investigated. The QC sample was a pooled sample from all serum samples.

Internal standard mixture

Stock solutions of all IS in the range of 0.25 to 2.1 µg/µL were prepared and mixed to obtain an IS mixture for spiking. The final concentrations of IS were reported in Table 1 in nmol/mL serum.

Table 1. Concentrations of IS for individual lipid classes

Extraction

A modified Folch procedure was used for lipid extraction. Human serum (25 µL), and the mixture of IS (17.5 µL) were homogenized in 3 mL of chloroform - methanol (2:1, v/v) for 10 min in an ultrasonic bath (40°C). When the samples reached ambient temperature, 600 µL of water were added, and the mixture was vortexed for 1 min. After 3 min of centrifugation (3000 rpm), the aqueous layer was removed, and the organic layer was evaporated under a gentle stream of nitrogen. The residue was dissolved in a mixture of 500 µL of chloroform - 2-propanol (1:1, v/v), carefully vortexed, and filtered (0.2 µm syringe filter). The extract was diluted 1:20 with the mixture of hexane - 2-propanol - chloroform (7:1.5:1.5, v/v/v) for ultrahigh-performance supercritical fluid chromatography – mass spectrometry (UHPSFC/MS) analysis.

Analysis

UHPSFC/MS measurements were carried out on an Acquity Ultra Performance Convergence Chromatography (UPC²) system hyphenated to the hybrid quadrupole - traveling wave ion mobility time-of-flight mass spectrometer Synapt G2 Si from Waters by using the commercial interface kit (Waters, Milford, MA, USA). The instrumental setting was the same as in the previous works (Lísa et al., 2015; Lísa et al., 2017). The lipid class separation was achieved by employing a Viridis BEH column (Waters, 100 x 3 mm, 1.7 µm) and the gradient elution. The mobile phase A was scCO₂, and the mobile phase B and make-up solvent were MeOH with 1% water and 30 mM NH₄OAc. The linear gradient was employed: 0 min - 1% B, 5 min - 51 % B, 6.5 min - 51% B, 6.8 min - 1% B. The total run time was 7.5 min. The column temperature was 60°C, the automatic back-pressure regulator was set to 1800 psi, the flow rate to 1.9 mL/min, the injection volume to 1 µL, and the make-up flow rate to 0.25 mL/min. Electrospray ionization in the positive-ion mode was used, and the mass range was set to m/z 50-1200 in the sensitivity mode. The continuum mode with a scan rate of 0.15 s was used for the analysis. The peptide leucine enkephalin was used as the lock mass with the scan time of 0.1 s and the interval of 30 s. The lock mass was scanned but not automatically applied for mass calibration correction. All samples were measured in duplicate.

Data processing

The noise reduction was performed on the raw files after measurements using the Waters compression tool. Afterwards, the files were lock mass corrected and converted into centroid data using the exact mass measure tool from Waters. Retention time ranges or mass scan ranges of individual lipid classes were determined by comparing the first and last measured samples to verify that the lipid class peak was still within the determined range even in case of possible retention time shifts. For each lipid class, the combined mass scan range of each lipid class was prepared by MarkerLynx XS (Waters). The peak separation window was 0.05 Da, and the intensity threshold was 3000 counts. Each method was applied for all quantified lipid classes in all samples within the sequence to obtain a summary table containing all features within the defined m/z range together with intensities for all samples in MarkerLynx XS. These tables obtained for each lipid class were exported as txt file and further processed by LipidQuant 1.0. The similar protocol may be used for data measured by mass spectrometers from other manufacturers to obtain the final txt file suitable for LipidQuant 1.0 processing.

Statistical analysis and visualization

SIMCA software, version 13.0 (Umetrics, Umeå, Sweden) was used to perform unsupervised principal component analysis (PCA) and supervised orthogonal projections to latent structures discriminant analysis (OPLS-DA). The scatter plots of the first and second components are shown for PCA. OPLS-DA separates samples into predefined classes, i.e., gender. The results table from LipidQuant 1.0 was copied into the SIMCA software, the studied lipids were defined as variables, and samples were defined as different observations. The data were pretreated by logarithmic transformation, centering, Pareto scaling, and evaluation of outliers. The logarithmic transformation aims to convert each lipid species into a Gaussian distribution. The centering relates the relative changes of lipid species to the average, where the Pareto scaling compensates the concentration differences of lipid species. The scaling allows that low abundant species contribute to the model to the same extent by dividing the centered species by the root of the standard deviation (Pareto scaling). To evaluate lipids of statistical relevance, a two-sided two sample T-test assuming unequal variances (Welch test) was performed for female and male samples in Microsoft Excel. P‑values <0.05 were considered to indicate statistical significance. For better visualization of differences in lipid concentrations between males and females, the S-plot was generated from the OPLS-DA plot (in SIMCA), box plots were constructed in R free software environment (https://www.r-project.org) using readxl and ggplot2 packages and diagram types from Microsoft Excel.

ReadMe file is uploaded separately to help to run the LipidQuant 1.0.