Analyzing Neural Stem Cell Clonal Development

分析神经干细胞克隆发育

• 批准号: 8219679
• 负责人: andrew Robert cohen
• 金额: $ 35.47万
• 依托单位: UNIVERSITY OF WISCONSIN MILWAUKEE
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8219679
• 关键词: Address; Algorithms; Anterior; Appearance; Area; Autistic Disorder; Behavior; Behavioral; Biological; Blood; Blood Cells; Brain; Brain Neoplasms; Cancer Biology; Cell Cycle; Cell Shape; Cell division; Cells; Cellular Morphology; Cerebral cortex; Characteristics; Communities; Computer software; Computing Methodologies; Data; Development; Developmental Biology; Disease; Disease model; Embryo; Environmental Risk Factor; Exogenous Factors; FGF10 gene; Fibroblast Growth Factor; Future; Genealogical Tree; Goals; Hematological Disease; Hematopoietic stem cells; Image; Image Analysis; In Vitro; Individual; Information Theory; Lead; Life; Manuals; Mental Retardation; Microscopy; Morphology; Motion; Mus; Neurobiology; Neuroglia; Neurons; Parent-Child Relations; Pattern; Phenotype; Play; Process; Property; Reproduction; Research; Resolution; Role; Shapes; Software Tools; Stem cells; System; Techniques; Time; Transplantation; Trees; Validation; Variant; base; brain tissue; cell behavior; cellular imaging; computerized tools; design; developmental disease; high throughput analysis; in vivo; insight; nerve stem cell; novel; open source; research study; retinal progenitor cell; spatiotemporal; statistics; stem; tool; tumorigenic

DESCRIPTION (provided by applicant): The goal of this research is to develop a new class of computational tools to analyze the spatiotemporal dynamics of clonal (family tree) development of neural stem cells (NSCs) by analyzing time lapse microscopy images. These computational tools will automatically segment or delineate individual cells in each image frame. The cells will be tracked to establish temporal correspondences. Lineaging will establish parent-child relationships among the cells and generate the lineage tree, a powerful representation that captures many of the important properties of clonal development. A user interface will be developed for viewing and editing the results of the automatic segmentation, tracking and lineaging. A new immunostaining multiplexing tool will provide detailed information on the types of neurons produced after all of the stem cells have terminally differentiated. Computational analyses of the segmentation, tracking, lineaging, and immunostaining results have the potential to enable a variety of important new experiments in developmental and cancer biology. In previous research, we developed uniquely sensitive tools based on algorithmic information theory that use a multivariate, multiresolution approach to analyzing biological image sequence data. In one exciting discovery, these software tools established that the fate of retinal stem cells can be accurately predicted from their dynamic behaviors. Here, we will apply these novel "computational sensing" approaches to studying stem cell clonal development. The project will proceed by capturing image sequence data showing clonal development from a single neural stem cell through multiple rounds of cell division to the ultimate differentiated progeny. The software tools will be developed in the context of three important biological questions. First, by imaging stem cells from different regions of the mouse cerebral cortex, we will identify whether region specification is encoded in lineage trees. Second, we will consider the role of FGF 10, an environmental factor related to regional specification, in imparting area specific developmental patterns to neurons from the cerebral cortex. Finally, we will analyze the role that syndecan1, an in vivo niche factor that causes lineage progression of NSCs and is upregulated in brain tumors, plays on NSC clonal development. These are fundamental questions in neurobiology that cannot be addressed without these new computational tools. The ultimate goal of this project is to develop a set of tools that is widely and generally applicable and that enables a new high throughput analysis approach for studying stem and tumorigenic cell clonal development. These tools will be developed in the context of three important questions from developmental biology. These questions concerning the fundamental dynamic behavior of NSCs and the influence of specific exogenous factors on this behavior require analysis of hundreds of lineage trees to define statistically meaningful differences. This was not possible in the past using manually constructed lineage trees. With the development of the computational tools described here, such analyses will now be within reach. PUBLIC HEALTH RELEVANCE: The goal of this research is to develop computational tools for analyzing how neural stem cells develop into brain tissue. By analyzing image sequence data obtained from live cell time-lapse microscopy, we will follow individual stem cells until they complete the process of differentiating into neurons and analyze their patterns of motion, shape, association and reproduction under different conditions of disease and development. Understanding how cell diversity is generated during development has important implications in developmental disorders such as autism and mental retardation and will be critical to produce specific types of neurons or glia for disease modeling and for future transplantation therapies.

描述(申请人提供)：这项研究的目标是开发一类新的计算工具，通过分析时间推移显微镜图像来分析神经干细胞(NSCs)克隆(家谱)发育的时空动力学。这些计算工具将自动分割或描绘每个图像帧中的单个单元。这些细胞将被追踪以建立时间上的对应。线性化将在细胞之间建立亲子关系，并生成谱系树，这是一种强大的代表，捕捉到了克隆发育的许多重要属性。将开发一个用户界面，用于查看和编辑自动分割、跟踪和划线的结果。一种新的免疫染色多路传输工具将提供有关所有干细胞终末分化后产生的神经元类型的详细信息。对分割、跟踪、谱系和免疫染色结果的计算分析有可能使发育和癌症生物学中的各种重要的新实验成为可能。在之前的研究中，我们基于算法信息理论开发了独特的敏感工具，使用多变量、多分辨率方法来分析生物图像序列数据。在一个令人兴奋的发现中，这些软件工具确立了可以根据视网膜干细胞的动态行为准确地预测它们的命运。在这里，我们将应用这些新的“计算传感”方法来研究干细胞克隆发育。该项目将继续捕获图像序列数据，显示从单个神经干细胞经过多轮细胞分裂到最终分化的后代的克隆发育。这些软件工具将在三个重要的生物学问题的背景下开发。首先，通过对小鼠大脑皮层不同区域的干细胞成像，我们将识别区域规范是否编码在谱系树中。其次，我们将考虑成纤维细胞生长因子10的作用，这是一个与区域规格相关的环境因素，在将区域特定的发育模式传递给大脑皮层的神经元方面。最后，我们将分析Syndecan1在NSC克隆发育中的作用。Syndecan1是一种体内利基因子，可导致NSCs的谱系进展，并在脑肿瘤中上调。这些都是神经生物学中的基本问题，没有这些新的计算工具是无法解决的。该项目的最终目标是开发一套广泛和普遍适用的工具，并使研究干细胞和肿瘤细胞克隆发展的新的高通量分析方法成为可能。这些工具将在发育生物学的三个重要问题的背景下开发。这些问题涉及神经干细胞的基本动态行为以及特定外源因素对这种行为的影响，需要对数百棵谱系树进行分析，以确定有统计学意义的差异。这在过去使用人工构建的谱系树是不可能的。随着这里描述的计算工具的发展，这样的分析现在将是触手可及的。 公共卫生相关性：这项研究的目标是开发用于分析神经干细胞如何发育成脑组织的计算工具。通过分析活细胞时间推移显微镜获得的图像序列数据，我们将跟踪单个干细胞，直到它们完成分化为神经元的过程，并分析它们在不同疾病和发育条件下的运动、形状、联想和繁殖模式。了解细胞多样性是如何在发育过程中产生的，对自闭症和智力低下等发育障碍具有重要意义，对于产生特定类型的神经元或神经胶质细胞用于疾病建模和未来的移植治疗至关重要。