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 神经元多样性的特征和确定身份的遗传程序。与此同时，我们的 对突触的多样性及其遗传基础的了解仍然有限。破译基因 负责神经结构的形成和维护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 与大脑连通性相关的精神健康障碍。 基于神经结构守恒的证据，在广泛的神经系统和强大的 在线虫的初步结果中，我假设突触连接是由基因编码的。具体来说，我 假设互补基因组合指定突触前神经元及其突触后神经 合作伙伴(类似于“钥匙和锁”的组合)。单细胞RNA测序与单细胞分辨 连通性数据集使得这一假设是可检验的。我将用两个平行的目标来测试这个假设 计算网络差异基因表达(NDGE)工具是我的先驱。这项技术集成了 单细胞分辨率基因表达数据与单细胞分辨率连通性分配统计 富含突触连接神经元的组合遗传模式的意义。跨越两个目标，我 将研究线虫结构和功能连接体的转录编码(目标1) 以及啮齿动物海马区CA1区锥体细胞和中间神经元的微连接(Aim 2)。为了实现这些目标，我将构建额外的计算工具来提取C语言中的功能连接体。 Elgans(目标1b)和协调啮齿类动物的空间转录数据和功能钙成像数据 海马区(目标2a)。总之，这些目标将提供两个重要的切入点，以澄清 跨多个动物神经系统的神经结构的遗传编程。此外，这些目标 将产生有价值的计算工具，造福分子和系统神经科学界 作为一个整体。多动物方法将确保计算的健壮性和生物学有效性 我将向神经科学界介绍的模型和工具。 在这个奖项的K99阶段，发生在哥伦比亚大学充满活力的神经科学界，我将 由利亚姆·帕宁斯基博士、奥利弗·霍伯特博士和阿提拉·洛松奇博士指导，同时咨询拉里·阿博特博士， 以及阿肖克·利特温-库马尔博士。这些教授代表了不同的计算、分子和 线虫和啮齿动物模型的系统水平神经科学。他们将引导我磨练我的计算能力 进一步的技能，并提供必要的分子和电路神经生物学的培训 在分子和系统神经科学的界面上的独立计算研究员。

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.