NCS-FO: Identifying Design Principles of Neural Cells

PI: Qutub， Amina提案号：1533708该提案旨在发展一个关于单个神经细胞如何形成电活性网络的强大理论。该项目整合了计算机科学、系统生物学、神经工程学和发育生物学的新兴方法，以深入了解大脑的组织。在这个项目中进行的实验结果有可能影响新的计算设备的设计，这是一个价值3000亿美元的产业。研究人员介绍的方法可以被科学家广泛用于快速表征脑细胞，并有助于发现神经系统疾病的新疗法，全球有六分之一的人受到神经系统疾病的影响。神经分化，即神经祖细胞转化为神经元的过程，是理解大脑自我修复能力的关键。了解这个复杂的过程可以告诉我们神经网络的结构与功能之间的关系，这是神经工程中一个重要的未解决的问题。这个项目的最终目标是建立一个关于神经网络是如何由单个神经祖细胞做出决定而形成的机械详细理论。研究人员将整合来自三个学科（系统生物学、纳米技术和发育生物学）的方法，确定对网络形成至关重要的单细胞特征，并预测细胞群体中的异质性和噪声如何定义网络的功能。研究人员将采用三种新兴方法（蛋白质组学条形码，图像组学和E-phFACS）来定义神经细胞表型作为化学信号，形态和电活动的功能（Aim 1）。观察到的这些单细胞表型的变化将通过耦合状态机模型和基于图的分析映射到神经网络的形成（目标2）。细胞异质性的影响将使用研究人员开发的新框架进行计算探索，并与体外活体成像分析和体内分析进行迭代比较。这里介绍的技术实现了化学、形态和电测量的紧密耦合，可以广泛应用于综合科学研究。了解细胞如何相互作用形成神经网络与生物体发育和组织工程有关。状态机可以在组织和物种之间进行调整，以发展细胞决策理论。将电生理学和蛋白质表达与多细胞网络拓扑相关联的表型工具将为神经生物学家提供强大的资源。此外，E-phFACS设备提供了一种高通量的方法来记录电活动和分类细胞。为了进一步吸引科学界，研究人员将(1)提供一个开源的ImageOmics平台，(2)举办一个国际众包神经网络设计挑战赛。

PI: Qutub, Amina Proposal Number: 1533708This proposal seeks to develop a robust theory of how single neural cells form electrically active networks. The project integrates emerging methods in computer science, systems biology, neuroengineering and developmental biology to offer insight into the brain's organization. Results of experiments performed in this project have the potential to impact the design of new computing devices, a $300B industry. Methods introduced by the investigators can be used broadly by scientists to rapidly characterize brain cells, and can aid in the discovery of new therapies for neurological diseases, which affect 1 in 6 people worldwide.Neural differentiation, the process of neural progenitor cells transforming into neurons, holds the key to understanding the brain's ability to self-repair. Understanding this complex process can inform us how the structure of neural networks relates to their function, which is an important unsolved problem in neuroengineering. This project's ultimate goal is a mechanistically-detailed theory of how neural networks form as a result of decisions made by single neural progenitor cells. Integrating methods from three disciplines (systems biology, nanotechnology and developmental biology), the investigators will identify single cell features critical to network formation, and predict how heterogeneity and noise in the cell population defines the network's function. The investigators will employ three emerging methods (proteomic barcoding, ImageOmics, and E-phFACS) to define neural cell phenotypes as a function of chemical signaling, morphology and electrical activity (Aim 1). Observed changes in these single cell phenotypes will be mapped to neural network formation by coupling a state machine model with a graph-based analysis (Aim 2). The effects of cell heterogeneity will be explored computationally using a new framework developed by the investigators and iteratively compared to in vitro live-imaging assays, and in vivo assays. The tight coupling of chemical, morphological and electrical measurements enabled by the technologies introduced here, can be broadly applicable paradigm for integrative scientific research. Understanding how cells interact to form neural networks has relevance to organism development and tissue engineering. State-machines can be adapted across tissues and species to develop theories of cell decision-making. The phenotyping tools that correlate electrophysiology and protein expression with multi-cellular network topology will provide a powerful resource for neurobiologists. Furthermore, the E-phFACS device provides a high-throughput way to record electrical activity and sort cells. To further engage the scientific community, the investigators will (1) provide an open source ImageOmics platform and (2) host an international crowd-sourced neuronal network Design Challenge.