Mouse lipidomics reveals inherent flexibility of a mammalian lipidome

Abstract

Lipidomics has become an indispensable method for the quantitative assessment of lipid metabolism in basic, clinical, and pharmaceutical research. It allows for the generation of information-dense datasets in a large variety of experimental setups and model organisms. Previous studies, mostly conducted in mice (Mus musculus), have shown a remarkable specificity of the lipid compositions of different cell types, tissues, and organs. However, a systematic analysis of the overall variation of the mouse lipidome is lacking. To fill this gap, in the present study, the effect of diet, sex, and genotype on the lipidomes of mouse tissues, organs, and bodily fluids has been investigated. Baseline quantitative lipidomes consisting of 796 individual lipid molecules belonging to 24 lipid classes are provided for 10 different sample types. Furthermore, the susceptibility of lipidomes to the tested parameters is assessed, providing insights into the organ-specific lipidomic plasticity and flexibility. This dataset provides a valuable resource for basic and pharmaceutical researchers working with murine models and complements existing proteomic and transcriptomic datasets. It will inform experimental design and facilitate interpretation of lipidomic datasets.

Figure 1

Lipid composition of mouse organs. (A) Principal component analysis (PCA) based on lipid species in mol%. Axis labels indicate principal component 1 and 2, including % variance explained. (B) PCA as in A, but excluding storage lipids (DAG, TAG, CE). (C) Lipid class composition. (D) Profiles of fatty acids derived from complex lipids. Shown are mean values for all combinations of experimental conditions (biological triplicates for all combinations of diet, sex, and genotype; $n=24$). Error bars indicate standard deviations.

Figure 2

Feature profiles of the major lipid classes. Shown is the weighted mean number of double bonds per lipid class in a given sample and the weighted mean number of carbons in the hydrocarbon chain moiety per lipid class in a given sample (each dot represents an individual sample). Minor lipid classes (lyso-lipids) are omitted for clarity.

Figure 3

Lipid class plasticity across all combinations of experimental conditions. (A) Plasticity is calculated per class and organ by multiplying the scaled ranges of values of weighted mean number of double bonds and the weighted mean number of carbons in the hydrocarbon chain moiety per lipid class. The wider the ranges, the higher the plasticity. (B) Lipid class plasticity for the different mouse organs. For details see Materials & Methods.

Figure 4

Flexibility of the mouse lipidome. (A) Analysis of lipidomic flexibility in mouse organs based on the tested experimental conditions. For details see main text. Mouse plots were created with the R library gganatogram (v.2). (B) Analysis of lipidomic responses by multiple linear regression. Shown are $\beta$ coefficients. Error bars indicate standard error. Examples shown are: Genotype effects for brain; diet effects for liver; sex effects for full blood. Significantly affected features ($p<0.01$) are displayed non-transparently.

Figure 5

(A) Genotype-specific effects of diet on ceramide levels and degree of PE unsaturation in kidney. (B) Genotype- and sex-specific (C) and effects of diet based on a multiple linear regression including interaction between diet and genotype and diet and sex, respectively. Shown are $\beta$ coefficients. Error bars indicate standard error. Significantly affected features ($p<0.05$ for the interaction term) are displayed non-transparently. Lipidomic features are depicted on the y axis (with “_db” denoting the weighted mean double bond number and “_c” denoting the weighted mean of the carbon chain length of a lipid class; when neither is specified, the feature name refers to lipid class abundance).

Figure 6

Correlations of organ lipidomes with blood lipidomes. (A) Hierarchical clustering based on correlation coefficients for diet effects on individual lipids in different organs with lipids in blood plasma as reference point. Colour scale indicates correlation coefficient $\rho$. Non-significant positive correlations and negative correlations are shown in blue. (B) Scatter plots showing correlations for TAG lipidomic features in organs with counterparts in blood plasma and full blood. Dashed lines show a linear regression of the data (with grey areas indicating the 95% confidence interval). In both panels only significant ($p<0.05$) positive correlations are shown and correlations were adjusted for sex and genotype.