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，我们的了解突触的多样性及其遗传基础仍然有限。解码遗传负责形成和维护神经体系结构的计划可以帮助我们了解突触在大脑中的功能作用，并提供指向设计治疗的遗传靶标与大脑连通性有关的心理健康障碍。基于在广泛的神经系统中保护神经体系结构的证据秀丽隐杆线虫的初步结果，我假设突触连通性是遗传编码的。具体来说，我假设免费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“钥匙和锁组合”）。单细胞RNA测序和单细胞分辨率连接数据集使该假设可检验。我将使用两个平行目的测试该假设我已经开创了计算网络差异基因表达（NDGE）工具。该技术集成了具有单细胞分辨率连接性的单细胞分辨率基因表达数据分配统计对富含突触连接神经元的结合遗传模式的意义。在两个目标中，我将研究秀丽隐杆线虫的结构和功能连接组的转录编码（AIM 1）以及在啮齿动物海马的CA1区域的锥体细胞和中间神经元的微连接性（AIM 2）。为了实现这些目标，我将构建其他计算工具，以在C中提取功能连接组。秀丽隐杆线虫（AIM 1B），并将空间转录数据与啮齿动物的功能性钙成像数据进行协调海马（AIM 2A）。这些目标在一起将为阐明跨多个动物神经系统神经体系结构的基因编程。此外，这些目标将生成有价值的计算工具，以使分子和系统神经科学社区受益整体。多种动物方法将确保计算的鲁棒性和生物学有效性我将向神经科学社区介绍的模型和工具。在该奖项的K99阶段，发生在哥伦比亚充满活力的神经科学社区中，我将是 Liam Paninski博士，Oliver Hobert博士和Attila Losonczy博士的指导，并在与Larry Abbott博士咨询时和Ashok Litwin-Kumar博士。这些教授代表了计算，分子和秀丽隐杆线虫和啮齿动物模型中的系统级神经科学。他们将指导我磨练我的计算进一步的技能，并在我过渡到成为分子和电路神经生物学的所需培训分子和系统神经科学界面的独立计算研究者。