CRCNS: Theory and experiment of neural circuit mapping by DNA sequencing

CRCNS：DNA测序神经回路图谱的理论与实验

• 批准号: 9246516
• 负责人: ALEXEI KOULAKOV
• 金额: $ 43.2万
• 依托单位: COLD SPRING HARBOR LABORATORY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9246516
• 关键词: Algorithmic Software; Algorithms; Alzheimer' s Disease; Award; Bioinformatics; Biological Assay; Biological Process; Biology; Biophysics; Brain; Cells; Cellular Neurobiology; Cognition; Collaborations; Combinatorics; Communication; Complex; Computational algorithm; Computing Methodologies; DNA; DNA Sequence; DNA Shuffling; DNA mapping; DNA sequencing; Data; Data Analyses; Decision Making; Drops; Electrophysiology (science); Environment; Enzymes; Feedback; Foundations; Generations; Genetic; Genetic Recombination; Genome; Genomic DNA; Goals; Growth; High-Throughput DNA Sequencing; Individual; Interneurons; Label; Learning; Link; Medical; Memory; Methods; Microscopic; Modeling; Morphologic artifacts; Mus; Nervous system structure; Neurodegenerative Disorders; Neurons; Neurosciences; Parkinson Disease; Perception; Postdoctoral Fellow; Preparation; Research; Research Personnel; Resolution; Schizophrenia; Science; Sensory; Site; Societies; Software Design; Structure; Synapses; System; Techniques; Technology; Testing; Training; Travel; Virus; autism spectrum disorder; base; biophysical model; brain abnormalities; computer studies; cost; design; experimental analysis; experimental study; mathematical model; neural circuit; neuronal circuitry; neuropsychiatric disorder; new technology; next generation; novel; novel strategies; recombinase; reconstruction; synergism; theories; transmission process

DESCRIPTION (provided by applicant): The brain is an extremely complex network consisting of billions of neurons linked by trillions of synapses. Neuronal function depends on how these neurons are connected within this network. A wide range of brain functions, including sensory perception, learning, memory, decision making, cognition, reasoning, and communication, is therefore related to the details of this neuronal connectivity. Many neuropsychiatric and neurodegenerative disorders, including schizophrenia, autism spectrum disorders, Alzheimer's and Parkinson's diseases, are linked to abnormal changes in neuronal connectivity. Understanding neuronal circuitry is therefore a task of enormous importance. Despite the progress made by using microscopic and electrophysiological approaches, especially for small networks, understanding neuronal connectivity has been stalled by astronomical complexity of this task. Here we propose to provide the computational and theoretical foundations for a novel technology which will dramatically accelerate our capacity to determine neuronal connectivity with single-neuron resolution. We are adapting the techniques of high-throughput next-generation DNA sequencing for the purposes of obtaining the structure of neuronal connectivity. We argue that because the cost of DNA sequencing has dropped precipitously over the last few years and the efficiency of these techniques is undergoing explosive growth, obtaining connectivity of sufficiently large networks is now feasible at sufficiently low cost. In our proposl, for example, we present preliminary data on the reconstruction of connectivity within a network of cultured mouse neurons containing about 1200 network nodes, which is the largest neuronal network reconstructed to date. To accomplish this task, we introduce unique short sequences of DNA into every neuron in the network. Because these short sequences uniquely label individual cells, we call them genetic barcodes. Using specifically designed viruses, we made these barcodes jump across synaptic junctions. Using enzymes called DNA recombinases, we connect barcodes from the host cell to the invader barcodes that travel across synapses into pairs. The barcode pairs carry information about network connectivity that can be obtained by DNA sequencing. Reconstructing neuronal connections from sequencing results presents unique computational and theoretical challenges that have never been dealt with before. In this project, we will build mathematical models that describe the underlying biological processes, test these models against experimental data, and use the resulting expertise to design accurate and efficient computational algorithms. Because of the need for feedback between theory, computational algorithms, and experiments, our project is intensely collaborative. The specific aims (SA) of this proposal include: SA 1: To develop a method of generating random barcodes in genomic DNA using the shufflon system. Here we will study, both theoretically and experimentally, the method of generating a large ensemble of barcode sequences using Rci recombinase that can shuffle DNA as a deck of cards. SA 2: To develop a biophysically realistic model for barcode processing within cells. In this SA we will study the mathematical models of barcodes jumping across synapses and their post-processing with the goal of identifying potential artifacts. The results of these models will be used for error correction in SA3. SA 3: To develop the computational pipeline for the reconstruction of connectivity from sequencing data. Here we will build a set of algorithms for efficient reconstruction of neuronal circuits from barcode pairs. Intellectual merit. The proposed research will contribute to biology on several levels. First, we will develop a novel set of technologies that will allow assaying neuronal connections with the single-neuron resolution. Second, we will build descriptive models for biophysics and combinatorics of DNA recombination that can be used in neuroscience and beyond. Finally, we will design a set of bioinformatics algorithms that are specific for the task o reconstructing neuronal connectivity. Broader impact. This project is based on the synergy between theoretical sciences, novel computational methods, and cutting-edge experiments in cellular neurobiology. The award will provide a unique cross-disciplinary environment for training of young neuroscientists. We expect that two postdoctoral fellows, specializing in theoretical and in experimental approaches, will receive training through this award. To broader society: Reconstructing neural circuits has significance for both fundamental studies of the brain and the studies of abnormalities of brain function. It is hard, if not impossible, to identify a medical condition involving the nervous system that would not affect neuronal connections.

描述(申请人提供)：大脑是一个极其复杂的网络，由数万亿个突触连接的数十亿个神经元组成。神经元的功能取决于这些神经元在这个网络中是如何连接的。因此，大脑的广泛功能，包括感觉、学习、记忆、决策、认知、推理和交流，都与这种神经元连接的细节有关。许多神经精神和神经退行性疾病，包括精神分裂症、自闭症谱系障碍、阿尔茨海默氏症和帕金森氏症，都与神经元连接的异常变化有关。因此，了解神经元回路是一项极其重要的任务。尽管使用显微镜和电生理学方法取得了进展，特别是对于小型网络，但由于这项任务的天文数字复杂性，对神经元连接的理解一直停滞不前。在这里，我们建议为一种新的技术提供计算和理论基础，该技术将极大地提高我们以单神经元分辨率确定神经元连接的能力。我们正在采用高通量下一代DNA测序技术，以获得神经元连接的结构。我们认为，由于DNA测序的成本在过去几年里急剧下降，而这些技术的效率正在经历爆炸性的增长，现在以足够低的成本获得足够大的网络连接是可行的。例如，在我们的提议中，我们提供了关于包含大约1200个网络节点的培养的小鼠神经元网络内连通性重建的初步数据，这是迄今为止重建的最大的神经元网络。为了完成这项任务，我们将独特的DNA短序列引入到网络中的每个神经元中。因为这些短序列唯一地标记单个细胞，我们称它们为遗传条形码。使用专门设计的病毒，我们让这些条形码跳过突触连接。使用被称为DNA重组酶的酶，我们将宿主细胞的条形码与入侵的条形码连接在一起，这些条形码通过突触成对传播。条形码对携带可通过DNA测序获得的有关网络连接的信息。根据测序结果重建神经元连接提出了以前从未处理过的独特的计算和理论挑战。在这个项目中，我们将建立描述潜在生物过程的数学模型，根据实验数据测试这些模型，并使用产生的专业知识来设计准确和高效的计算算法。由于理论、计算算法和实验之间需要反馈，我们的项目具有强烈的协作性。该方案的具体目标包括：SA 1：开发一种使用改组系统在基因组DNA中生成随机条形码的方法。在这里，我们将从理论和实验上研究使用RCI重组酶生成大量条形码序列的方法，该重组酶可以将DNA作为一副牌洗牌。SA 2：开发一种在细胞内处理条形码的生物物理模型。在这个SA中，我们将研究条形码跨越突触的数学模型及其后处理，目的是识别潜在的人工制品。这些模型的结果将用于SA3的纠错。SA 3：至 开发用于根据测序数据重建连通性的计算管道。在这里，我们将构建一套用于从条形码对高效重建神经元电路的算法。智力上的优点。这项拟议的研究将在几个层面上对生物学做出贡献。首先，我们将开发一套新的技术，允许使用单神经元分辨率来分析神经元连接。其次，我们将为生物物理学和DNA重组的组合学建立描述性模型，这些模型可用于神经科学和其他领域。最后，我们将设计一套专门用于重建神经元连接的生物信息学算法。更广泛的影响。这个项目是基于理论科学、新的计算方法和细胞神经生物学的前沿实验之间的协同作用。该奖项将为培养年轻的神经科学家提供一个独特的跨学科环境。我们预计，两名博士后研究员将通过这一奖项接受培训，他们专门从事理论和实验方法。对于更广泛的社会：重建神经回路对于大脑的基础研究和大脑功能异常的研究都具有重要意义。即使不是不可能，也很难识别出一种涉及神经系统的不会影响神经元连接的疾病。

项目成果

Network cloning using DNA barcodes.

使用 DNA 条形码的网络克隆。

• DOI: 10.1073/pnas.1706012116
• 发表时间: 2019
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1
• 作者: Shuvaev,SergeyA;Başerdem,Batuhan;Zador,AnthonyM;Koulakov,AlexeiA
• 通讯作者: Koulakov,AlexeiA

Mathematical Model of Evolution of Brain Parcellation.

• DOI: 10.3389/fncir.2016.00043
• 发表时间: 2016
• 期刊: Frontiers in neural circuits
• 影响因子: 3.5
• 作者: Ferrante DD;Wei Y;Koulakov AA
• 通讯作者: Koulakov AA