Profinder

Targeted Feature Extraction of the acquired LC-MS data was performed using the Profinder™ software package, version B.06.00 (Agilent Technologies Inc., Santa Clara, CA, USA) and an in-house retention time and mass spectra library consisting of 713 metabolites or 487 lipid species. Feature detection was based on following parameters: allowed ion species in positive ionization mode: (+H, +Na, +K, +NH₄); in negative ionization mode: (-H, +HCOO) peak spacing tolerance: 0.0025–7 ppm; isotope model: common organic molecules; charge state: 1; mass tolerance: 10 ppm; retention time tolerance: 0.1 min. After extraction of peaks, each compound was manually checked for mass and retention time agreement with appropriate standards from the library; peaks with bad characteristics (overloaded, noisy, non- Gaussian, etc.) were excluded from the analysis. Identification of the compounds was confirmed by comparison of their MS/MS spectra with MS/MS spectra of relevant standards. For LCMS analysis of metabolites, combined data set was used, with compounds detected in both negative and positive ion modes. If a metabolite was detected in both modes, the one with higher signal intensity was retained for statistical analysis. In the lipidomics study only the positive mode was used.

Following supernatant removal for LA quantitation (above section), cell pellets were prepared for lipidomics as follows. Supernatant was removed from flasks, and cell monolayers washed twice with warm PBS. Monolayers were scraped using plastic cell scrapers, spun at 2500xg for 15 seconds, washed once more with PBS, and spun again. Resulting pellets were frozen at −80°C until time of analysis. Each pooled sample or individual sample was analyzed separately in both positive and negative ionization modes. This was to permit different dilution factors to be employed for each mode to maximize lipid annotations. In this sample set it was experimentally determined a 40x dilution was appropriate for the positive mode and a 2x dilution for the negative mode. Downstream processing of data was conducted independently for each ionization mode. Lipid libraries were obtained by iterative MS/MS analysis of a pooled sample representing the treatment conditions and processed using Lipid Annotator (Agilent) software. This produced 4620 and 4517 features, and 375 and 560 identified lipids in positive and negative modes respectively. Individual sample lipid quantifications were obtained by MS1 analysis and processing with Profinder (Agilent) software utilizing the lipid library for that ionization mode.

Hgd tm1a −/− (n = 4) and Hgd tm1a −/+ (n = 4) mice were injected with ¹³C₆-HGA into the lateral tail vein, adjusted to body weight to achieve a final blood concentration of ~1 mmol/L. Under anaesthesia, venous tail bleeds were collected at time points post-injection, ranging from 2 to 60 min. Whole blood was centrifuged, and the supernatant removed and immediately frozen.
Non-targeted metabolic flux analysis was performed to trace metabolism of ¹³C₆-HGA. Metabolic profiling was performed using a published mass spectrometric technique (44 (link)). Briefly, plasma was diluted 1:9 plasma:deionized water and HPLC performed on an Atlantis dC₁₈ column (3 × 100 mm, 3 μm, Waters, UK) coupled to an Agilent (Cheadle, UK) 6550 quadrupole time-of-flight mass spectrometer. An accurate-mass compound database with potential association to HGA was generated for data mining using Agilent Pathways to PCDL. Data were mined for these compound targets with an accurate mass window of ± 5 ppm using ‘batch isotopologue extraction’ in ProFinder (build 08:00, Agilent). Isotopologue extraction investigates association with the injected ¹³C₆-HGA by examining the relative abundances of the M + 0–M + 6 isotopologues for compound targets.

Data were acquired from an Agilent 6545 UHD QTOF interfaced with an Agilent 1290 UHPLC. Metabolites were separated by using a Millipore Sigma SeQuant ZIC-pHILIC (150 mm x 2.1 mm, 5 μm) column. Solvents were A, 95% water in acetonitrile with 10 mM ammonium acetate and 5 μM phosphate, and B, 100% acetonitrile. A flow rate of 200 μL/min was applied with the following gradient (minutes, %B): 0, 94.7%; 2, 94.7%; 27, 36.8%; 35, 20.0%; 37, 20.0%; 39, 36.8%. For all experiments, 2 μL of metabolic extract was injected. MS parameters were as follows: gas, 200°C 4 L/min; nebuilizer, 44 psi; sheath gas, 300°C 12 L/min; capillary, 3kV; fragmentor, 100V; scan rate, one scan/s. MS detection was carried out in both positive and negative modes with a mass range of 65–1,700 Da. Identifications were established by comparing the retention times and fragmentation data of compounds to model standards. All raw data files were converted into mzXML files by using msconvert. Data analysis was performed by using either Agilent’s Profinder or in-house R packages.

QC samples and blanks were injected throughout the sample queue to ensure the reliability of acquired lipidomics data. Results from LC-MS experiments were collected using Agilent Mass Hunter (MH) Workstation Data Acquisition. Briefly, a pooled lipid extract comprised of an aliquot from each sample was analyzed in MS/MS mode. From this data, a lipid library was created using Lipid Annotator (Agilent Technologies, Inc) for positive and negative ion data, incorporating lipid identity (with either enumerated acyl chain composition or a sum composition), m/z value, and retention time. Data for all individual samples was collected in MS mode. Profinder (Agilent Technologies, Inc.) was used to perform retention time alignment between samples and to extract peak areas from each sample, for each lipid present in the library generated by Lipid Annotator. The results from Profinder were exported to .csv files for positive and negative ionization modes, reported by area. Area values were imported into RStudio for statistical analysis and figure preparation.

Profinder (version B.06.00, Agilent Technologies, Santa Clara, CA, USA) was used for data processing. Up to 2000 compounds were extracted. Data filtering, data normalization, missing value estimation, and fold changes were obtained using the MetaboAlalyst 3.0 online software. Resultant data matrices were analyzed by the SIMCA-P+ 13.0 (Umetrics, Umeå, Sweden) software. The significant differences were screened using the fold change value (>1.5) combined with the t-test (p < 0.05) and ANOVA (p < 0.05). Variables with significant changes were determined as potential biomarkers for further identification of molecular formulas. Biomarkers were tentatively identified by the online METLIN database. Pathway analysis was performed using MetaboAnalyst 3.0 18 (http://www.MetaboAnalyst. ca/).