Transcriptional basis of stereotyped neural architectures

刻板神经结构的转录基础

基本信息

批准号：
10672300
负责人：
Erdem Varol
金额：
$ 12.54万
依托单位：
COLUMBIA UNIV NEW YORK MORNINGSIDE
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2023-09-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10672300
关键词：
3-Dimensional Anatomy Animal Model Animals Architecture Area Award Behavior Biological Brain Brain Diseases Cadherins Caenorhabditis elegans Calcium Cell Adhesion Molecules Cells Code Communities Complex Computational Technique Computer Models Data Data Set Defect Ensure Gap Junctions Gene Combinations Gene Expression Gene Expression Profile Gene Expression Profiling Genes Genetic Genetic Programming Genetic Transcription Heterogeneity Hippocampus Image Image Analysis Individual Interneurons Linear Models Location Maintenance Memory Mental Depression Mental disorders Mentors Mentorship Molecular Mus Nervous System Neurobiology Neurons Neuropeptides Neurosciences Pattern Phase Play Pyramidal Cells Reporter Genes Research Research Personnel Resolution Rodent Rodent Model Role Schizophrenia Signal Transduction Specific qualifier value Stereotyping Synapses System Techniques Testing Tissue-Specific Gene Expression Training Transgenic Organisms Universities Validation Visualization autism spectrum disorder combinatorial computational neuroscience computerized tools connectome design genetic effector in vivo monoamine mouse model multi-photon mutant neural neural circuit neuroregulation novel postsynaptic presynaptic presynaptic neurons professor programs reconstruction single-cell RNA sequencing skills theories therapeutic target tool transcriptome transcriptomics virtual environment wireless

项目摘要

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.

抽象的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，我们的对突触多样性及其遗传基础的了解仍然有限。解码基因负责神经结构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目标提供切入点与大脑连接相关的心理健康障碍。基于广泛神经系统中神经结构守恒的证据和强大的根据秀丽隐杆线虫的初步结果，我假设突触连接是基因编码的。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元合作伙伴（类似于“钥匙和锁”的组合）。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检验。我将使用以下两个并行目标来测试这个假设我首创的计算网络差异基因表达（nDGE）工具。该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接性以分配统计对突触连接神经元中丰富的组合遗传模式具有重要意义。跨越两个目标，我将研究秀丽隐杆线虫结构和功能连接组的转录编码（目标 1）以及啮齿动物海马 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 中的功能连接组。线虫（目标 1b）并将空间转录组数据与啮齿动物的功能钙成像数据相协调海马体（目标 2a）。总之，这些目标将为阐明跨多个动物神经系统的神经结构的遗传编程。此外，这些目标将为分子和系统神经科学界带来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稳健性和生物学有效性我将向神经科学界介绍的模型和工具。在该奖项的 K99 阶段，在哥伦比亚充满活力的神经科学界进行，我将由 Liam Paninski 博士、Oliver Hobert 博士和 Attila Losonczy 博士指导，同时咨询 Larry Abbott 博士，和 Ashok Litwin-Kumar 博士。这些教授代表了计算、分子和生物领域的不同专业知识。线虫和啮齿动物模型的系统级神经科学。他们将指导我磨练我的计算能力进一步提高技能，并在我过渡到成为一名医生的过程中提供分子和回路神经生物学所需的培训分子和系统神经科学领域的独立计算研究员。