A systematic survey of lipids across mouse tissues

Abstract

Lipids are a diverse collection of macromolecules essential for normal physiology, but the tissue distribution and function for many individual lipid species remain unclear. Here, we report a mass spectrometry survey of lipid abundance across 18 mouse tissues, detecting ~1,000 mass spectrometry features, of which we identify 179 lipids from the glycerolipids, glycerophospholipids, lysophospholipids, acylcarnitines, sphingolipids, and cholesteryl ester classes. Our data reveal tissue-specific organization of lipids and can be used to generate testable hypotheses. For example, our data indicate that circulating triglycerides positively and negatively associated with future diabetes in humans are enriched in mouse adipose tissue and liver, respectively, raising hypotheses regarding the tissue origins of these diabetes-associated lipids. We also integrate our tissue lipid data with gene expression profiles to predict a number of substrates of lipid-metabolizing enzymes, highlighting choline phosphotransferases and sterol O-acyltransferases. Finally, we identify several tissue-specific lipids not present in plasma under normal conditions that may be of interest as biomarkers of tissue injury, and we show that two of these lipids are released into blood following ischemic brain injury in mice. This resource complements existing compendia of tissue gene expression and may be useful for integrative physiology and lipid biology.

Keywords: biomarkers; lipid metabolism; lipidomics; stroke; tissue physiology and metabolism.

Fig. 1.

Measurement of lipid species in mouse tissues. A: tissue isolation and lipid extraction and analysis by LC-MS results in 2-D full scan images of lipid abundance, separated by retention time and mass/charge (m/z). A full scan image for a single heart tissue sample is shown; the magnified inset is <1% of the full data. Dark color indicates high lipid abundance. Lipid classes form predictable patterns in this space that aid in peak identification, exemplified here by 4 phosphatidylcholines. A known lipid standard (red box) and available lipid structure database defines m/z and retention time coordinates. Additional lipids of the same family can then be identified (blue boxes). Lipid abundances are obtained by integrating 2-D peaks followed by correction for ionization efficiency (see materials and methods). B: general structures for the 9 lipid classes targeted. R-groups denote acyl chains. C: total abundance across tissues per targeted lipid class. Fatty acylcarnitines (FAC) and lysophosphatidylethanolamine (LPE) could not be quantified and are presented as MS peak areas. Error bars denote SD across 5–6 replicates. D: projection of targeted data from each sample into 2 dimensions, such that the distance between points reflects sample-to-sample correlation. E: hierarchical clustering trees derived from microarray gene expression profiles (top) and lipid data (bottom).

Fig. 2.

Lipid abundance patterns across 18 mammalian tissues. A: heat map of abundance of 179 distinct lipid species across the 8 lipid classes, presented as fold change above average for each lipid; for highlighted species, see main text. B: selected lipid abundance patterns; for discussion see text. Error bars denote SD across 5–6 replicates.

Fig. 3.

Lipid composition within mammalian tissues. A: relative abundance of triacylglycerols (TAGs), normalized per sample to the total abundance within this lipid class to obtain molar fractions. Each solid line indicates tissue from an individual mouse. Gray vertical lines separate TAGs by total number of acyl chain carbons. The number of double bonds increase from left to right within each group, as indicated by triangles. B and C: relative abundance of diacylglycerol (DAG) and phosphatidylcholine (PC) lipids, as described for A.

Fig. 4.

Abundance patterns from 2-D LC-MS data. A: z-Scores computed directly from 2-D LC-MS data of heart vs. gastrocnemius muscle. A region covering ∼1/3 of the full matrix is shown. B: magnified view of the region marked by a white rectangle in A. Left and middle: images of representative samples from heart and gastrocnemius tissue; dark color indicates high abundance. Right: corresponding z-scores. C: intensity and z-scores from white vs. brown adipose tissue, as in B. For highlighted lipids, see main text.

Fig. 5.

Cluster analysis of lipid profiles. Left: heat map of Spearman correlation matrix across the 179 targeted lipids with corresponding hierarchical cluster tree. White boxes indicate lipid clusters chosen for further analysis. Middle: list of species in lipid clusters. Right: centroids of lipid clusters across the 18 mouse tissues. For highlighted clusters, see main text.

Fig. 6.

Integration of tissue lipid profiles and gene expression. A: correlation profiles for the PC-synthesizing enzymes choline/ethanolamine phosphotransferase 1 (Cept1) and choline phosphotransferase 1 (Chpt1) across all identified PC species. Gray lines separate PCs by total number of acyl chain carbons. Unsat, number of unsaturated acyl chain bonds. B: correlation profiles for cholesteryl ester (CE)-synthesizing enzymes sterol O-acyltransferase 1/2 (Soat1 and Soat2) across all identified CE species, as in A. See also supplementary dataset.

Fig. 7.

Release of tissue-specific lipids with tissue injury. A: abundance of sphingomyelin (SM) 36:1 and 36:2 in brain tissue relative to plasma isolated from the jugular vein. B: abundance of SM species in jugular vein plasma. C: release of SM 36:1 and 36:2 into peripheral blood of mice 24 h after ischemic stroke (●) compared with control animals undergoing sham surgery (○), expressed as fold change over time 0. Horizontal lines denote group means.

Fig. 8.

Qualitative MS lipid standards used for lipid identification.

Fig. 9.

Quantitative MS lipid standards used to assess ionization efficiency.