Identifying transcriptomic axes governing neural dynamics, behavior, and states

PROJECT SUMMARY/ABSTRACT Environmental inputs are dynamically processed by a network of functionally distinct cell types organized into specific brain circuits to generate purposeful motor actions. While it is evident that physiological requirements significantly influence the selection of behavioral responses, the precise mechanisms through which internal states shape these innate behaviors remain poorly characterized. The lateral hypothalamus (LH) and the midbrain periaqueductal gray (PAG), which are interconnected, play a central role in various homeostatic and innate behaviors such as sleep, feeding, parenting, mating, and aggression. However, achieving a comprehensive understanding of how sensory information integrates with internal states to generate purposeful motor actions has remained a significant challenge due to their functional and transcriptomic diversity, where genetically defined neurons respond with great heterogeneity to a wide range of behaviors. Moreover, while tools have been developed to independently measure activity dynamics, connectivity, and transcriptional profiles of individual neurons, it remains challenging to integrate this diverse information into a coherent model of behavior. To address these challenges, I will develop a novel technological pipeline that enables monitoring of large-scale neural activity in freely moving animals and identifies transcriptomic identity of the same individual neurons by combining post-hoc spatial transcriptomics in the same brain tissue. My preliminary data examining neural population activity in the LH and PAG during social behavior suggest that LH neurons exhibit state-specific information whereas PAG neurons exhibit social distance information. I hypothesize that unique transcriptomic axes are involved in the neural dynamics that encode homeostatic, physiological states, and social behavior in these brain regions. This approach will enable functional probing of multiple cell types and their interaction during innate behavior and offer a mechanistic understanding of neural computation by revealing each recorded neuron’s molecular expression that contributes to their activity patterns. In the independent R00 phase, I will utilize these approaches to understand the neural circuit mechanism of aging-associated changes in cognitive, homeostatic, and social behavior in terms of behavior, gene expression, and neuronal activity. The successful completion of this project will provide a platform for future experiments toward understanding the aging-associated innate behavior circuits. The training phase of the award will be conducted in the lab of Dr. Catherine Dulac at Harvard University. I will be mentored by an outstanding team of scientists on my advisory committee, with specific training goals and career guidance. In my application, I outline a comprehensive plan for the acquisition of conceptual, technical, and professional skills that will enable my transition to an independent research position.