Project I: From Skull Shape to Cell Activity in Coronal Craniosynostosis

项目I，从头骨形状到细胞活动在冠状颅骨融合症中的作用 颅缝融合是一种常见的出生缺陷，可以作为综合征的一部分发生，也可以作为孤立的异常发生。 与颅缝融合障碍相关的颅骨畸形的分析通常集中在过早闭合 穹隆缝合和颅顶形状改变。我们有来自人类和老鼠的新数据证明 颅缝闭锁症的头颅表型包括所有的头骨，除颅顶外的缝合线， 和颅骨软组织。解剖全面性颅骨改变与软组织发育驱动 颅骨融合症颅骨表型，我们将在小鼠模型中量化骨形成中断的影响 在细胞水平上，使用双光子激光显微镜，结合多尺度计算模型 头骨生长。我们将首先确定成骨细胞系细胞(OLC)在产生特定颅骨中的作用。 通过表征增殖和增殖的时间和空间分布来描述畸形 小鼠颅骨发育过程中OLC的分化。这将通过开发一种新的转基因技术来实现 Runx2-RFP，将用于产生OSX-GFP；Runx2-RFP小鼠和双光子激光显微镜 观察颅骨胚胎发育过程中OLC分化的阶段。我们将开发一个分期系统来 定量比较包括小鼠在内的不同转基因系中OLC的增殖和分化模式 有冠状颅缝闭锁和未受影响的产仔鼠(特异性Aim1)。这将阐明细胞水平上的 颅骨发育中发生的变化为将细胞行为与3D形状变化相结合提供了基础 发生在个体发育过程中。要严格理解OLC分化的变化如何导致 全球颅骨变形学，我们将创建一个多尺度的颅骨形态发生计算模型 (具体目标2)。计算建模方法将加强对 颅缝融合表型的产生受实际测量参数的限制。中的单元格数量 初始‘骨化中心’，OLC分化和增殖率，颅内压梯度 生长诱导的颅骨-软组织相互作用和缝合速度可以在 模特。结果可以与我们广泛的骨骼图像档案进行持续的定量比较 发育过程中小鼠的特征和头颅器官形态。协同效应：本项目与 项目III将基于我们在典型的OLC增殖和分化模式中检测到的差异 发育中的小鼠和颅脑融合症小鼠，因为这可以直接有助于了解信号通路 参与OLC分化的时空调节，纳入OLC的网络分析 项目三：已知致病突变的小鼠颅骨形状的精确表型将 通知项目II完成的人类颅缝融合病例的形态计量学分析 计算模型可以用来排除或确定特定参数对严重程度的贡献 老鼠的头面部表型，进而在人类中也是如此。

PROJECT I, From Skull Shape to Cell Activity in Coronal Craniosynostosis Craniosynostosis is a common birth defect that can occur as part of a syndrome or as an isolated anomaly. Analysis of skull malformations associated with craniosynostosis disorders often focus on premature closure of vault sutures and change in cranial vault shape. We have novel data from humans and mice that demonstrate that craniosynostosis cranial phenotypes involve all skull bones, sutures other than those of the cranial vault, and cranial soft tissues. To dissect how global alteration of cranial bone and soft tissue development drive craniosynostosis cranial phenotypes, we will quantify the effects of disrupted bone formation in a mouse model at the cellular level using two-photon laser microscopy, combined with a multiscale computational model of skull growth. We will first establish the role of osteoblast lineage cell (OLC) activity in producing specific cranial dysmorphologies through characterization of the temporal and spatial distribution of proliferating and differentiating OLCs in developing mouse skulls. This will be accomplished by developing a new transgenic line, Runx2-RFP, that will be used to generate Osx-GFP;Runx2-RFP mice and two-photon laser microscopy to visualize stages in OLC differentiation during cranial embryogenesis. We will develop a staging system to quantitatively compare OLC proliferation and differentiation patterns in various transgenic lines including mice with coronal craniosynostosis and unaffected littermates (Specific Aim1). This will elucidate the cellular-level changes that occur in cranial development providing the basis for joining cell behavior with 3D shape changes that occur during ontogeny. To rigorously understand how changes in OLC differentiation can give rise to global skull dysmorphology, we will create a multiscale computational model of cranial morphogenesis (Specific Aim 2). The computational modeling approach will enhance a hypothesis driven investigation of the production of craniosynostosis phenotypes constrained by actual, measured parameters. Numbers of cells in initial 'ossification centers', rate of OLC differentiation and proliferation, intracranial pressure gradients from growth induced skull-soft tissue interaction, and rate of suture closure can be parameterized and modified in the model. The results can be continually quantitatively compared to our extensive image archive of bone characteristics and cranial organ shapes in developing mice. Synergy: Interaction between this project and Project III will be based on the differences we detect in OLC proliferation and differentiation patterns in typically developing and craniosynostosis mice as this can contribute directly to knowledge of signaling pathways involved in the spatiotemporal regulation of OLC differentiation to be incorporated in the network analysis of Project III. Precise phenotyping of cranial shapes in mice in which the disease causing mutation is known will inform the morphometric analyses of human craniosynostosis cases accomplished for Project II while the computational model can be used to rule out, or identify the contribution of specific parameters to severity of craniofacial phenotypes in mice, and by extension in humans.