AdipoAtlas: A reference lipidome for human white adipose tissue

Abstract

Obesity, characterized by expansion and metabolic dysregulation of white adipose tissue (WAT), has reached pandemic proportions and acts as a primer for a wide range of metabolic disorders. Remodeling of WAT lipidome in obesity and associated comorbidities can explain disease etiology and provide valuable diagnostic and prognostic markers. To support understanding of WAT lipidome remodeling at the molecular level, we provide in-depth lipidomics profiling of human subcutaneous and visceral WAT of lean and obese individuals. We generate a human WAT reference lipidome by performing tissue-tailored preanalytical and analytical workflows, which allow accurate identification and semi-absolute quantification of 1,636 and 737 lipid molecular species, respectively. Deep lipidomic profiling allows identification of main lipid (sub)classes undergoing depot-/phenotype-specific remodeling. Previously unanticipated diversity of WAT ceramides is now uncovered. AdipoAtlas reference lipidome serves as a data-rich resource for the development of WAT-specific high-throughput methods and as a scaffold for systems medicine data integration.

Keywords: LC-MS/MS; ceramides; human white adipose tissue; lipid identification; lipid metabolism; lipidomics; obesity; plasmalogens; semi-absolute lipid quantification; sphingolipids; subcutaneous white adipose tissue; triacylglycerols; visceral white adipose tissue.

Graphical abstract

Figure 1

Optimization of sample preparation protocols for global lipidome profiling of human WAT (A) Lipid-class-specific WAT lipidome composition as determined by quantitative high-performance thin layer chromatography (qHPTLC) of Folch lipid extracts in combination with liquid/liquid extraction (LLE) for polar lipid enrichment. (B) Extraction efficiency of unpolar lipids as determined by qHPTLC. WAT lipids were extracted by the Folch, the methyl-tert-butyl-ether (MTBE), or the hexane/i-PrOH/HOAc (Hex/IPA) method. (C) Extraction efficiency of phosphate containing polar lipids by the different lipid extraction protocols was determined with ³¹P-NMR. (D) Efficiency to separate polar and unpolar lipids from WAT lipid extracts was compared using polarity separation by LLE, by separation based on the presence of phosphate groups in lipids by zirconia-oxide-based solid phase extraction (Zr-SPE), or by aminopropyl SPE-based lipid class fractionation (LipFrac). (E) Schematic depiction of the optimized lipid extraction and fractionation protocol. (F) qHPTLC analysis of WAT lipidome before (total lipid extract dominated by unpolar lipids) and after (enriched polar and amphiphilic lipids; ethanol fraction) LLE fractionation.

Figure 2

Workflow adapted for high-confidence lipid identification from human WAT (A) Schematic depiction of the identification strategy for three-dimensionally curated, high-confidence lipid library of human WAT. (B) Lipid-class-specific LC separation was applied to allow for the highest possible chromatographic resolution to achieve optimal lipidome coverage. LC-MS chromatograms of unpolar lipids separated by C30 reversed-phase chromatography (RPC), amphiphilic lipids by C18 RPC, and polar acylcarnitines (CAR) by hydrophilic interaction chromatography (HILIC). (C) Exemplary depiction of retention time mapping for TG, PC, and CAR lipid classes. Kendrick mass defect to the hydrogen base (KMD[H]) was plotted against lipid retention time to increase confidence of lipid identification. (D) Graphical representation of human WAT lipid molecular species grouped by the corresponding lipid class obtained by high-confidence identification strategy.

Figure 3

Quantitative representation of human WAT lipidome and description of analytical strategy used (A) Schematic depiction of the quantitative lipidomics workflow. (B) Quantitative distribution of lipid class and corresponding lipid molecular species within subclasses of human WAT. Total lipid class concentration is represented by bold lines (SUM), and each single lipid molecular species is represented by thin lines. (C) Distribution of single TG based on bulk FA chain length (expressed as total carbon number, n(C); black dots) and unsaturation (expressed as double bond number, n(DB); red dots). (D–G) Quantitative distribution of FAs, fatty alcohols, fatty vinyl alcohols, and SPBs across (D) PLs, (E) acylCARs, (F) CEs and diacylglycerols and, (G) SPs. Plotted FA concentrations were determined by summing up the concentration of individual species containing a particular fatty acyl chain per lipid subclass.

Figure 4

Human WAT displays distinct lipidome profiles depending on WAT depot (VAT versus SAT) and phenotype (lean versus obese) The global lipidome was quantified for representative pools of WAT from VAT and SAT depots of lean (n = 5) and obese (n = 81) individuals. (A) Heatmap displaying statistically significantly regulated lipid molecular species between the WAT of obese and lean patients. (B) Concentration of differentially regulated triacylglycerols (TGs) between obese and lean WAT. (C) Pearson correlation of significantly regulated lipids between lean and obese WAT. (D) Concentration of statistically significantly regulated Cer species between lean and obese WAT. (E) Heatmap displaying statistically significantly regulated lipid molecular species between obese VAT (VAT[ob]) and obese SAT (SAT[ob]) depots. (F) Concentrations of PL and plasmalogen PL species that are statistically significantly regulated between VAT(ob) and SAT(ob). Statistical significance was determined by Student’s t test (FDR adjusted) with a cutoff of p ≤ 0.05 and a minimum fold change ≥ 2.

Figure 5

Complexity of the human WAT sphingolipidome (A) Sankey plot displays the concentration of Cer subclasses, its corresponding esterified SPBs, and FAs. Depicted concentrations were calculated by averaging concentrations of WAT from SAT and VAT depots of lean and obese patients in order to reflect the general WAT sphingolipidome. Length of boxes corresponds to the determined concentrations. (B) Differential regulation of sphingosine and sphingadienine SPBs over Cer subclasses in obese (ob) and lean tissues from VAT and SAT depots. (C) Differential regulation of deoxy-sphingosine and deoxy-sphingadienine SPBs over Cer subclasses in obese (ob) and lean tissues from VAT and SAT depots. Statistical significance was calculated by ANOVA. ∗p ≤ 0.05; ∗∗p ≤ 0.01; ∗∗∗p ≤ 0.005.