Lipid annotator software

Agilent Mass Profiler Professional (MPP version 15.1) was used to process the LCMS untargeted and targeted data. Retention times (RT) were aligned by setting a RT window of 0.6 min, whereas m/z binning was performed by setting windows at 10 ppm. Absolute abundance of each entity was normalised by the absolute abundance of the d31CerNS internal standard. Data were filtered by frequency of detection, which reflects the number of samples that presented particular features. A frequency filter was applied to data extracted from MPP and only entities present in 100% of samples belonging to at least one of the investigated groups were retained for the statistical analysis. Fold changes of filtered entities were compared between groups volcano plots in the MPP tools. Fold changes with p values <0.05 after Bonferroni’s correction were considered as significant. Identification of entities within the MPP workflow was performed based on the METLIN Metabolomics Database (http://metlin.scripps.edu/) and the Lipid Annotator software (Agilent Technologies, CA, United States). Quantitative assessment of cholesterol sulfate (CSO₄) was performed with the deuterium labelled internal standard d7CSO₄.

MS/MS spectra for lipids were acquired using an iterative approach in the MassHunter Acquisition Software (Version 10.1.48, Agilent Technologies) on an Agilent 6545 QTOF. Source settings for MS1 data acquisition were used. MS/MS spectra were acquired at a scan rate of 3 spectra/s with different intensity thresholds and collision energies of 10, 20, and 40 V to increase identification rates. For the ID-X Orbitrap, data were acquired in data-dependent acquisition (DDA) by using the built-in deep scan option (AcquireX) with a mass range of 67–900 m/z. The MS/MS scans were acquired at 15 K resolution and cross referenced to a library generated from NIST SRM 1950 plasma sample in both positive and negative ion mode with different collision energies in the range of 20 NCE to 50 NCE for HCD and 30 NCE for CID to maximize identifications.
Lipid iterative MS/MS data were annotated with the Agilent Lipid Annotator software. All data files were then analyzed in Skyline-daily (Version 22.2.1.256) to obtain peak areas, m/z values of the metabolite and lipid target lists, obtained from the metabolite identification workflow, which had at least an MS/MS match to an online library, were extracted under consideration of retention times.

The annotation workflow [28 (link)] consisted of three steps: (i) initial tentative identification of lipid features based on the MS1 data using the online tool CEU Mass Mediator (CMM) (http://ceumass.eps.uspceu.es/mediator/ (accessed on 4 July 2021)) [29 (link),30 (link)], (ii) reprocessing of the raw LC–MS/MS data with Lipid Annotator software (Agilent Technologies Inc., Santa Clara, CA, USA) and, finally, (iii) manual MS/MS spectral interpretation using the Agilent MassHunter Qualitative Analysis software (version 10.0), comparing the retention time and MS/MS fragmentation with the available spectral data included in several databases [31 (link),32 (link),33 (link)]. Detailed information about the lipid annotation process can be found in the Supplementary Materials. The lipid nomenclature convention used here for the lipid species reported follows the latest update of the shorthand annotation [37 (link)]. Once the most affected lipids were annotated, the Lipid Network Explorer (LINEX) [38 (link)] tool was used to visualize and analyze the lipidomics networks generated with the specific sets of lipids previously marked as being the most distinctive within the SGA and LGA subgroups when compared to the AGA subgroup, and also the most distinctive sets of lipids within each of the SGA and LGA subgroups for comparisons between them.