Spatial Recruitment of Cardiolipins in Inguinal White Adipose Tissue after Cold Stimulation is Independent of UCP1

Brown and brite adipocytes are unique for uncoupling protein 1 (UCP1) dependent non-shivering thermogenesis (NST) induced by cold exposure. Several lipid species are associated with NST in brown and white adipose tissues (WAT). Studies investigating this association rely on the analysis of whole organ homogenates, or pre-adipocytes differentiated in vitro. These approaches do not account for the regional heterogeneity of WAT. The authors aimed to characterize the spatial lipid composition of WAT in an in vivo context to identify and validate lipids colocalized with UCP1. Therefore, matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI), high-resolution mass spectrometry, and immunohistochemistry are applied on sections of inguinal WAT from UCP1 knockout and wildtype mice acclimatized to cold. The authors identified spatial overlap of cardiolipins and diacylglycerols in UCP1 positive regions, and triacylglycerols as the main lipid class characteristic for UCP1 negative regions within inguinal WAT. Investigation of UCP1 knockout and wildtype mice housed at room temperature or acclimatized to cold demonstrated that cardiolipins content in WAT is increased upon cold stimulation, independent of UCP1. In summary, a MALDI-MSI-based approach is introduced to identify lipids associated with thermogenic adipocytes in adipose tissues and demonstrate a regional cold-dependent upregulation of cardiolipins independent of UCP1. Practical application: It is demonstrated that MALDI-MSI is a valuable tool to investigate the spatial distribution of lipids in WAT. Consequently, it is an important complement to LC-MS/MS analysis of whole tissue homogenates. In combination with immunohistochemistry, it allows the straightforward examination of potential interactions between structural and metabolic traits, as demonstrated by spatial colocalization of UCP1 expression with specific lipid species in the current study. As a major advantage, this enables analyses on subsets of the same sample combining different analytic approaches, thereby potentially reducing the number of experimental animals.