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

描述（由申请人提供）：大脑是一个极其复杂的网络，由数十亿个神经元由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的连接方式。广泛的大脑功能，包括感觉知觉、学习、记忆、决策、认知、推理和交流，因此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，都与神经元连通性的异常变化有关。因此，了解神经回路是一项极其重要的任务。尽管使用微观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，对神经元连接的理解一直停滞不前。在这里，我们建议为一种新技术提供计算和理论基础，该技术将极大地加快我们用单个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中急剧下降，这些技术的效率正在经历爆炸式的增长，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，在我们的提案中，我们提供了关于在包含约1200个网络节点的培养小鼠神经元网络中重建连通性的初步数据，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短序列引入网络中的每个神经元。因为这些短序列唯一地标记了单个细胞，我们称之为基因条形码。使用特别设计的病毒，我们让这些条形码跨越突触连接。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入侵细胞的条形码连接起来，这些条形码通过突触成对地传播。这些条形码对携带有关网络连通性的信息，这些信息可以通过DNA测序获得。从测序结果重建神经元连接提出了前所未有的计算和理论挑战。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，并使用由此产生的专业知识来设计准确高效的计算算法。因为需要理论、计算算法和实验之间的反馈，我们的项目是高度协作的。本提案的具体目标包括：目标1：开发一种利用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重组酶产生大量条形码序列的方法，该重组酶可以将DNA洗牌。目标2：开发细胞内条形码处理的生物物理现实模型。在这个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的误差校正。SA 3: To

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.