UC Irvine

The study of cross-tissue communication has become more significant to the study of metabolic regulation and physiological homeostasis, from the level of fundamental signaling studies to the consideration of clinical treatment therapies. But these communications are not static, plain-truth relationships, as they are also influenced by factors like genetic sex, the environment or diet, and individual variation in the subjects studied. In this dissertation, I showcase my graduate work studying patterns of cross-tissue communication by using different contexts, like genetic sex, cell-type interactions, and functional pathways, through the application of a published cell-type deconvolution pipeline called ADAPTS. Cell type deconvolution allows me to estimate cell type proportions in bulk RNA-sequencing data, which I can then correlate with expression in other cell types or tissues. First, I investigated the influence of genetic sex on cell-type interactions of a single tissue of origin, skeletal muscle. I then expanded the approach to deconvolute multiple tissues, and these cell type proportion estimates were incorporated into a publicly available web tool, GD-CAT, developed by the lab. Finally, I applied the ADAPTS pipeline as part of the discovery of a novel factor affecting heart function, to gain a better understanding of the systemic regulation of heart failure.