Transcriptional basis of stereotyped neural architectures

ABSTRACT Recent advances in high-resolution volumetric imaging and single-cell RNA sequencing have enabled the characterization of neuronal diversity and the genetic programs that specify identity. Meanwhile, our understanding of the diversity of synapses and their genetic underpinnings remains limited. Decoding the genetic programs responsible for the formation and maintenance of neural architectures can help us understand the functional role of synapses in the brain and offer entry points towards designing genetic targets for the treatment of mental health disorders related to brain connectivity. Based on the evidence of conservation of neural architectures in a wide range of neural systems and strong preliminary results in C. elegans, I hypothesize that synaptic connectivity is genetically encoded. Specifically, I hypothesize that complimentary gene combinations specify pre-synaptic neurons and their post-synaptic neural partners (resembling a "key-and-lock" combination). Single-cell RNA sequencing and single-cell resolution connectivity datasets make this hypothesis testable. I will test this hypothesis in two parallel aims using the computational Network Differential Gene Expression (nDGE) tool I have pioneered. This technique integrates single-cell resolution gene expression data with single-cell resolution connectivity to assign statistical significance to combinatorial genetic patterns enriched in synaptically connected neurons. Across two aims, I will investigate the transcriptional encoding of the structural and functional connectome of C. elegans (Aim 1) and the micro-connectivity of pyramidal cells and interneurons in the CA1 region of the rodent hippocampus (Aim 2). To accomplish these aims, I will build additional computational tools to extract a functional connectome in C. elegans (Aim 1b) and harmonize spatial transcriptomic data with functional calcium imaging data in the rodent hippocampus (Aim 2a). Together, these aims will provide two substantial entry points towards elucidating the genetic programming of neural architectures across multiple animal nervous systems. Additionally, these aims will generate valuable computational tools for the benefit of the molecular and systems neuroscience community as a whole. The multiple animal approach will ensure the robustness and biological validity of the computational models and tools that I will introduce to the neuroscience community. During the K99 phase of this award, occurring within Columbia's vibrant neuroscience community, I will be mentored by Dr. Liam Paninski, Dr. Oliver Hobert, and Dr. Attila Losonczy while consulting with Dr. Larry Abbott, and Dr. Ashok Litwin-Kumar. These professors represent diverse expertise in computational, molecular, and systems-level neuroscience in C. elegans and rodent models. They will guide me to hone my computational skills further and provide needed training in molecular and circuit neurobiology during my transition to becoming an independent computational investigator at the interface of molecular and systems neuroscience.