Transcriptional basis of stereotyped neural architectures

摘要 高分辨率体积成像和单细胞RNA测序的最新进展使得 神经元多样性的表征和指定身份的遗传程序。同时我们 对突触的多样性及其遗传基础的了解仍然有限。解码基因 负责形成和维持神经结构的程序可以帮助我们理解 突触在大脑中的功能作用，并为设计治疗的遗传靶点提供切入点 与大脑连通性有关的精神疾病 基于在广泛的神经系统中神经结构保守的证据， C.我假设突触连接是由基因编码的。我特别 假设互补基因组合指定突触前神经元和它们突触后神经元 合作伙伴（类似于“钥匙和锁”组合）。单细胞RNA测序和单细胞分辨率 连接性数据集使这一假设变得可测试。我将在两个平行的目标中测试这个假设， 计算网络差异基因表达（nDGE）工具，我开创了。这项技术整合了 具有单细胞分辨率连接性单细胞分辨率基因表达数据以分配统计 重要性的组合遗传模式丰富的突触连接的神经元。我有两个目标， 将研究C. elegans（Aim 1） 和啮齿动物海马CA 1区锥体细胞和中间神经元的微连接（目的 2）。为了实现这些目标，我将建立额外的计算工具来提取C语言中的功能性连接体。 elegans（Aim 1b），并协调啮齿动物中的空间转录组数据与功能性钙成像数据 海马（Aim 2a）。总之，这些目标将提供两个重要的切入点，以阐明 跨多种动物神经系统的神经结构的遗传编程。此外，这些目标 将为分子和系统神经科学界提供有价值的计算工具 作为一个整体。多动物方法将确保计算的鲁棒性和生物有效性。 模型和工具，我将介绍给神经科学界。 在这个奖项的K99阶段，发生在哥伦比亚充满活力的神经科学界，我将 在Liam Paninski博士、奥利弗霍伯特博士和Attila Losonczy博士的指导下，同时与Larry Abbott博士进行咨询， 和阿肖克·利特温-库马尔博士这些教授代表了计算，分子和 系统层次的神经科学在C.线虫和啮齿动物模型。他们会引导我磨练我的计算能力 技能进一步，并提供必要的培训，在分子和电路神经生物学在我的过渡，成为 一个在分子和系统神经科学的接口独立的计算调查员。

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.