Spatial Systems Lipidomics Reveals Nonalcoholic Fatty Liver Disease Heterogeneity

Abstract

Hepatocellular lipid accumulation characterizes nonalcoholic fatty liver disease (NAFLD). However, the types of lipids associated with disease progression are debated, as is the impact of their localization. Traditional lipidomics analysis using liver homogenates or plasma dilutes and averages lipid concentrations, and does not provide spatial information about lipid distribution. We aimed to characterize the distribution of specific lipid species related to NAFLD severity by performing label-free molecular analysis by mass spectrometry imaging (MSI). Fresh frozen liver biopsies from obese subjects undergoing bariatric surgery ( n = 23) with various degrees of NAFLD were cryosectioned and analyzed by matrix-assisted laser desorption/ionization (MALDI)-MSI. Molecular identification was verified by tandem MS. Tissue sections were histopathologically stained, annotated according to the Kleiner classification, and coregistered with the MSI data set. Lipid pathway analysis was performed and linked to local proteome networks. Spatially resolved lipid profiles showed pronounced differences between nonsteatotic and steatotic tissues. Lipid identification and network analyses revealed phosphatidylinositols and arachidonic acid metabolism in nonsteatotic regions, whereas low-density lipoprotein (LDL) and very low-density lipoprotein (VLDL) metabolism was associated with steatotic tissue. Supervised and unsupervised discriminant analysis using lipid based classifiers outperformed simulated analysis of liver tissue homogenates in predicting steatosis severity. We conclude that lipid composition of steatotic and nonsteatotic tissue is highly distinct, implying that spatial context is important for understanding the mechanisms of lipid accumulation in NAFLD. MSI combined with principal component-linear discriminant analysis linking lipid and protein pathways represents a novel tool enabling detailed, comprehensive studies of the heterogeneity of NAFLD.

Figure 1

Schematic overview of the parallel data analyses performed.

Figure 2

Molecular classification of liver tissue by PCA of the MALDI-TOF-MSI data in negative-ion mode. Projection of PCA loading (PC3) from a representative tissue section delineates nonsteatotic (top, PC–3) and steatotic (bottom, PC+3) regions. Ion images are scaled to relative intensity. These regions correspond well to histological annotations (middle). Molecular intensity-scaled loading spectra of the PC function show unique molecular mass profiles for each tissue region. Note that PC–3 is shown with absolute values for easier interpretation.

Figure 3

Preferential tissue distribution and high predictive value of phosphatidylglycerol (18:1_20:4) to steatotic regions. (A) MS ion image (top) of PG(18:1_20:4) (m/z 795.4) alongside the annotated histological image (bottom) of the same tissue section reveals preferential localization to steatotic areas. Color scale is in relative intensity. (B) Receiver operating characteristic (ROC) analysis of PG(18:1_20:4) shows high discriminatory power with AUC of 0.838. Inset, the relative ion intensity of PG(18:1_20:4) shows higher abundance in steatotic compared to nonsteatotic tissue regions (pixels). The horizontal line denotes the average value, the box indicates the 95% confidence interval, and the bars signify the standard deviation.

Figure 4

Increased PG(18:2_22:6) (m/z 817.5) abundance in regions with steatosis. (A) Relative intensity of PG(18:2_22:6) (m/z 817.5) in MS ion images shows increase in steatotic regions. (B) Box plots showing the intensity of this lipid in complete tissue regions grouped according to steatosis content: group 1 (<5% steatosis, green); group 2 (5–33% steatosis, blue); group 3 (>33–66% steatosis, black); and group 4 (>66% steatosis, red).

Figure 5

Lipid–protein interaction network determined from lipids prevalent in nonsteatotic (blue) and steatotic (green) regions.

Figure 6

PCA-LDA data-driven classifier. Histogram (middle right) showing the distribution of the 4 classes along the discriminant function 1 (DF1, middle left). Intensity-scaled loading spectra (top and bottom) displaying the mass channels associated with the discriminatory power of DF1 for nonsteatotic (top) and steatotic grades (bottom). Projection of the DF score onto the training set of MS images, where each pixel is given a color based on its DF1 score. The color code indicates the 4 classes used in the PCA-LDA classifier, which corresponds to steatosis stages 0 to 4, blue to orange, respectively.