Systematic Multi-scale Analysis of Tissue Morphogenesis

组织形态发生的系统多尺度分析

• 批准号: 9311707
• 负责人: Zhirong Bao
• 金额: $ 36.42万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2021-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9311707
• 关键词: Address; Algorithms; Alpha Cell; Behavior; Biological; Biology; Caenorhabditis elegans; Cell Communication; Cell Nucleus; Cell division; Cells; Color; Complement; Complex; Computational Science; Computer software; Congenital Abnormality; Data Set; Development; Embryo; Embryonic Development; Event; Future; Genes; Genetic; Human; Image; Individual; Investigation; Knowledge; Label; Mediating; Methods; Microscopy; Mitotic; Modeling; Molecular; Morphogenesis; Morphology; Movement; Neighborhoods; Noise; Nuclear; Optics; Organism; Pathway interactions; Positioning Attribute; Property; Resolution; Shapes; Statistical Models; System; Systems Biology; Techniques; Temperature; Testing; Time; Tissues; To specify; Work; base; cell behavior; cell motility; computerized tools; density; design; directional cell; genetic analysis; improved; in vivo; insight; interdisciplinary approach; molecular dynamics; novel; novel strategies; permissiveness; phenotypic data; programs; single cell analysis; technology development; temperature sensitive mutant; tool

Systematic Multi-scale Analysis of Tissue Morphogenesis Recent progress in live imaging offers unprecedented opportunities to examine cellular behaviors and how cell- cell interactions give rise to complex tissues in vivo. We propose to develop novel computational approaches to analyze and synthesize the complex phenotypic data from live imaging and apply them to study how collective cell behaviors mediate cell movement in tissue morphogenesis and achieve robust cell positioning. We use C. elegans embryogenesis as our model, where we have developed techniques for high throughput imaging and automated cell tracking that allow us to perturb hundreds of genes and conduct detailed lineage analysis in thousands of embryos. We propose three aims. First, we will develop new algorithms for accurate cell tracking in dense tissues, including a new method based on multi-color labeling of nuclei. Accuracy in cell tracking is a major bottleneck in systematic analysis of individual cell behaviors, especially in dense tissues. This effort will provide novel tools with improved accuracy, which in turn allows more effective analysis of individual cell behaviors in large image datasets. Second, We will examine novel mechanisms that mediate cell movement in tissue morphogenesis and achieve robust cell positioning. These include a novel form of multicellular rosette where sequential edge contraction and resolution events mediate directional cell movement. We also propose a novel model of robust cell positioning where cells assess their neighborhoods and activate movement when a desired neighbor is missing. We will elucidate the underlying molecular and cellular mechanisms combining genetic perturbations, systematic single-cell analysis and a novel method for real-time tracking and optical manipulation of single cells. This study will broaden our understanding of developmental noise control at the cellular and tissue levels. Third, We will develop a novel agent-based modeling framework to integrate complex phenotypic data for multi-scale analysis of complex tissues. We will develop a software package for general use beyond C. elegans. We will then apply it to examine lineage differentiation and tissue morphogenesis in C. elegans embryogenesis based on the thousands of perturbed embryos collected in this and our previous studies. In particular, we will further examine robustness in cell positioning by integrating the model above with a PCP-like model of Wnt-based spindle control. This work will provide a powerful tool to examine complex tissues across molecular, cellular and tissue levels, and further insights on the robustness of tissue morphogenesis.

组织形态发生的系统多尺度分析 活体成像的最新进展为研究细胞行为以及细胞如何在体内存活提供了前所未有的机会。 细胞相互作用在体内产生复杂的组织。我们建议开发新的计算方法 分析和综合来自实时成像的复杂表型数据，并将其应用于研究 集体细胞行为介导组织形态发生中的细胞运动并实现稳健的细胞定位。 我们使用C。线虫胚胎发生作为我们的模型，在那里我们已经开发了高通量的技术 成像和自动化细胞跟踪，使我们能够干扰数百个基因， 分析了数千个胚胎。我们提出三个目标。首先，我们将开发新的算法， 致密组织中的细胞跟踪，包括一种基于细胞核多色标记的新方法。单元格中的准确度 跟踪是系统分析单个细胞行为，特别是在致密组织中的单个细胞行为的主要瓶颈。 这一努力将提供具有更高准确性的新工具，从而可以更有效地分析 单个细胞的行为在大型图像数据集。其次，我们将研究新的机制，介导细胞 组织形态发生中的运动，并实现稳健的细胞定位。其中包括一种新颖的 多细胞玫瑰花结，其中连续的边缘收缩和消退事件介导定向细胞 运动我们还提出了一个新的模型，强大的细胞定位细胞评估他们的邻居 并且当期望的邻居丢失时激活移动。我们将阐明潜在的分子和 细胞机制结合遗传扰动，系统的单细胞分析和新的方法， 单细胞的实时跟踪和光学操作。这项研究将扩大我们对 在细胞和组织水平上的发育噪音控制。第三，我们将开发一种新颖的基于Agent的 建模框架，以整合复杂的表型数据，用于复杂组织的多尺度分析。我们将 开发一个通用的C语言以外的软件包。优美的然后我们将应用它来检查血统 分化和组织形态建成。线虫胚胎发生的基础上数以千计的扰动 在这个和我们以前的研究中收集的胚胎。特别地，我们将进一步研究单元格中的鲁棒性。 通过将上述模型与基于Wnt的主轴控制的类似PCP的模型相结合来实现定位。这项工作将 提供了一个强大的工具来检查分子，细胞和组织水平的复杂组织，并进一步 对组织形态发生稳健性的见解。