Adipose tissue plays a critical role in regulating systemic metabolism. The adipose depots themselves are heterogeneous mixtures of cell types that include immune cells, preadipocytes, stromal fibroblasts, endothelial cells, and others, in addition to the adipocytes themselves. The relative proportions of these cells, as well as their activation state, is known to differ following changes in nutritional state. Despite a longstanding realization that the behavior of adipose tissue is determined by interactions between the various component cell types within the fat pad, our knowledge of the identity of these cellular sub-populations is woefully incomplete. Here we propose to use single cell sequencing technology to identify novel subpopulations of cells within different adipose depots of mice and humans. We will assess how these different cells vary with sex, age, and metabolic perturbations, like fasting, high fat diet, gastric bypass, and thermal stress. Further, we will determine the activation state of these cells under these different conditions, and will utilize a variety of experimental and computational techniques to characterize the interactions that occur between cell types. The resulting database will be a community resource and the starting point for a new era of adipose tissue biology.
Adipose tissue contains numerous constituent cell types, and the relative proportions of these cells is known to change during nutritional perturbation. In particular, stromal and immune cells interact with adipocytes to regulate the behavior of the tissue as a whole, and thus have an enormous impact on the metabolism of the organism. The studies in this proposal will leverage recent advances in single cell profiling to identify novel cellular subpopulations across the various adipose depots of mice and men, will assess how these profiles change following metabolic perturbations like fasting, high fat feeding, and gastric bypass, and will utilize experimental and computational means to identify novel pathways of cellular cross-talk.
