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Analyzing Neural Stem Cell Clonal Development

分析神经干细胞克隆发育

基本信息

批准号：
8913708
负责人：
andrew Robert cohen
金额：
$ 35.09万
依托单位：
DREXEL UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-09-01 至 2017-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8913708
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

项目摘要

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.

描述（由申请人提供）：本研究的目标是开发一类新的计算工具，通过分析延时显微镜图像来分析神经干细胞（NSCs）克隆（家谱）发育的时空动态。这些计算工具将自动分割或描绘每个图像帧中的单个细胞。将跟踪细胞以建立时间对应关系。谱系化将在细胞之间建立父子关系，并生成谱系树，这是一种强大的表示方法，可以捕获克隆发育的许多重要特性。将开发一个用户界面，用于查看和编辑自动分割、跟踪和谱系化的结果。一种新的免疫染色多重工具将提供所有干细胞终末分化后产生的神经元类型的详细信息。分割，跟踪，谱系和免疫染色结果的计算分析有可能使各种重要的新实验在发育和癌症生物学。在以前的研究中，我们开发了基于算法信息论的独特敏感工具，该工具使用多变量，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，这些软件工具确定了视网膜干细胞的命运可以从它们的动态行为中准确预测。在这里，我们将应用这些新的“计算传感”的方法来研究干细胞克隆发展。该项目将通过捕获图像序列数据来进行，这些图像序列数据显示从单个神经干细胞通过多轮细胞分裂到最终分化的后代的克隆发育。软件工具将在三个重要的生物学问题的背景下开发。首先，通过对来自小鼠大脑皮层不同区域的干细胞进行成像，我们将确定区域规范是否在谱系树中编码。其次，我们将考虑FGF 10的作用，一个环境因素有关的区域规范，在赋予区域特定的发展模式，从大脑皮层的神经元。最后，我们将分析syndecan1的作用，syndecan1是一种体内小生境因子，导致神经干细胞的谱系进展，并在脑肿瘤中上调，对神经干细胞克隆发育起作用。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就无法解决。该项目的最终目标是开发一套广泛适用的工具，并为研究干细胞和致瘤细胞克隆发育提供一种新的高通量分析方法。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力学行为和特定外源性因素对这种行为的影响，需要分析数百个谱系树，以确定统计学上有意义的差异。这在过去使用手动构建的谱系树是不可能的。随着这里所描述的计算工具的发展，这种分析现在将触手可及。