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: 8692549
• 负责人: ALEXEI KOULAKOV
• 金额: $ 42.53万
• 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 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8692549
• 关键词: Affect; Algorithmic Software; Algorithms; Alzheimer' s Disease; Award; Bioinformatics; Biological Assay; Biological Process; Biology; Biophysics; Brain; Cells; Cellular Neurobiology; Cognition; Combinatorics; Communication; Complex; Computational algorithm; Computing Methodologies; DNA; DNA Sequence; DNA Shuffling; Data; Decision Making; Drops; Environment; Enzymes; Feedback; Foundations; Generations; Genetic; Genetic Recombination; Genome; Genomic DNA; Goals; Growth; High-Throughput DNA Sequencing; Individual; Label; Learning; Link; Maps; Medical; Memory; Methods; Microscopic; Modeling; Morphologic artifacts; Mus; Nervous system structure; Neurodegenerative Disorders; Neurons; Neurosciences; Parkinson Disease; Perception; Postdoctoral Fellow; Preparation; Process; Research; Research Personnel; Resolution; Schizophrenia; Science; Sensory; Site; Societies; Structure; Synapses; System; Techniques; Technology; Testing; Training; Travel; Virus; autism spectrum disorder; base; cost; design; experimental analysis; mathematical model; neural circuit; neuronal circuitry; neuropsychiatry; new technology; next generation; novel; novel strategies; recombinase; reconstruction; research study; 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（由申请人提供）：大脑是一个极其复杂的网络，由数十亿个神经元和数万亿个突触连接而成。 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，包括感官知觉、学习、记忆、决策、认知、推理和交流在内的广泛的大脑功能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、阿尔茨海默病和帕金森病，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非常重要的任务。尽管通过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复杂性，理解神经元连接一直停滞不前。在这里，我们建议提供一种新技术的计算和理论基础，这将大大加快我们的能力，以确定神经元的连接与单神经元的分辨率。 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中急剧下降，这些技术的效率正在经历爆炸性增长，以足够低的成本获得足够大的网络的连接现在是可行的。在我们的proposl中，例如，我们提出了初步的数据重建的培养小鼠神经元网络内的连接包含约1200个网络节点，这是迄今为止最大的神经元网络重建。为了完成这项任务，我们将独特的短DNA序列引入网络中的每个神经元。因为这些短序列独特地标记单个细胞，我们称它们为遗传条形码。使用专门设计的病毒，我们使这些条形码在突触连接处跳跃。利用一种叫做DNA重组酶的酶，我们将来自宿主细胞的条形码与穿过突触的入侵者条形码连接起来。条形码对携带可以通过DNA测序获得的关于网络连接的信息。从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，我们将建立数学模型，描述潜在的生物过程，测试这些模型对实验数据，并使用由此产生的专业知识来设计准确和有效的计算算法。由于理论、计算算法和实验之间需要反馈，我们的项目是高度合作的。该提案的具体目标（SA）包括：SA 1：开发使用shufflon系统在基因组DNA中生成随机条形码的方法。在这里，我们将从理论上和实验上研究使用Rci重组酶生成大量条形码序列的方法，该重组酶可以将DNA作为一副牌进行洗牌。SA 2：开发用于细胞内条形码处理的生物药理学现实模型。在本SA中，我们将研究跨越突触的条形码的数学模型及其后处理，目的是识别潜在的伪影。这些模型的结果将用于SA3中的误差校正。SA 3：到 开发用于从测序数据重建连通性的计算管道。在这里，我们将构建一组算法，用于从条形码对有效重建神经元回路。智力上的优点。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，允许用单神经元分辨率分析神经元连接。第二，我们将建立描述性模型的生物物理学和组合学的DNA重组，可用于神经科学和超越。最后，我们将设计一套生物信息学算法，专门用于重建神经元连接的任务。更广泛的影响。该项目基于理论科学，新颖的计算方法和细胞神经生物学前沿实验之间的协同作用。该奖项将为年轻神经科学家的培训提供一个独特的跨学科环境。我们预计，两名博士后研究员，专门从事理论和实验方法，将通过这个奖项接受培训。对于更广泛的社会：神经回路的重建对于脑的基础研究和脑功能异常的研究都具有重要意义。很难，如果不是不可能的话，确定一个涉及神经系统的医疗条件，不会影响神经元连接。