UC San Diego

Activity-dependent gene expression is a fundamental mechanism cells use to transduce stimuli and coordinate adaptations. In this dissertation, I describe the construction and analysis of a high-quality single-nucleus RNA-seq dataset capturing dimensions of activity-dependent transcription across a range of cell types, activity stimuli, and circadian timepoints. This dataset reveals both conserved and distinct cell-type responses to both physiological and pathological activity conditions; uncovers novel cell-type-specific and widely shared genetic signatures of activity; and dissects the contributions of both experience-induced activity and circadian time to the coordination of gene expression. Moreover, a broader objective of this project is to integrate these discoveries regarding cell-type-specific activity states into preexisting brain-wide cell-type ontologies, creating valuable resources for the wider neuroscience community.