Mechano-transduction and coordinated dynamics in epithelial cells in the amnioserosa of Drosophila

上皮细胞能够感知邻近细胞产生的或通过邻近细胞传递的力和运动并作出反应。背侧闭合是果蝇胚胎中一个主要的形态发生转变，它严重依赖于被称为羊膜膜的鳞状上皮的动力学。最近的研究发现，羊膜上皮细胞（AS细胞）的动力学是解剖上皮细胞如何协调其机械活动的一个有希望的模型系统。AS细胞(i)表现出依赖于组织状态的机械行为，并且在细胞之间表现出协调一致，（ii）易于遗传干预和定量活细胞成像，以及（iii）表现出在长时间内统计不变的协调活动，这是适用于数据驱动的随机建模技术的基本数学条件。我们的初步数据表明，在xit突变胚胎中，e -钙粘蛋白簇出现异常移动，细胞间协调受到严重干扰。这表明基于e -钙粘蛋白的信号复合物的机械转导对细胞间协调至关重要。基于这些结果，利用AS的可及性，本项目旨在结合果蝇发育遗传学和体内延时成像（JG），以及随机动力系统数学理论和大规模图像分析（FW）的方法，确定并定量模拟AS细胞间细胞连接处的机械转导机制。特别是，我们的目标是(1)确定将邻近细胞的机械活动和状态转化为细胞内化学信号的机械转导机制的分子基础，(2)使用大量的实时成像数据来确定细胞内化学信号编码机械刺激的定量模型，以及(3)通过遗传和光学干扰AS细胞动力学和转导机制来探索AS细胞机械转导的功能。我们期望这些研究将揭示上皮细胞机械转导的关键分子成分，并阐明这种机械指导其主动机械行为的原理。在方法上，该项目旨在展示和优化一种广泛适用的方法，用于从完整组织的活细胞成像中获得细胞机械转导的定量模型。

Epithelial cells are capable of sensing and reacting to forces and movements generated by or transmitted through their neighbors. Dorsal closure is a major morphogenetic transformation in Drosophila embryos that critically depends on the dynamics of a squamous epithelium called the amnioserosa. Recent findings identified the dynamics of epithelial cells in the amnioserosa (AS cells) as a promising model system for dissecting how epithelial cells coordinate their mechanical activities. AS cells (i) exhibit mechanical behaviors that depend on tissue state and appear coordinated between cells, (ii) are highly accessible to genetic intervention and quantitative live cell imaging, and (iii) exhibit coordinated activity that is statistically invariant over extended periods of time, a fundamental mathematical condition of applicability for data-driven stochastic modelling techniques. Our preliminary data indicate that in xit mutant embryos, in which E-cadherin clusters appear abnormally mobile, intercellular coordination is profoundly disturbed. This suggests that mechano-transduction by E-cadherin based signaling complexes is critical for intercellular coordination. Based on these results and taking advantage of the accessibility of the AS, the current project aims to identify and quantitatively model mechano-transduction mechanisms operating at intercellular junctions between AS cells by combining fly developmental genetics, and in vivo time-lapse imaging (JG), with methods from the mathematical theory of stochastic dynamical systems and large-scale image analysis (FW). In particular, we aim (1) to identify the molecular basis of mechano-transduction mechanisms that turn the mechanical activity and state of neighboring cells into intracellular chemical signals, (2) to use massive live-imaging data to determine quantitative models for the encoding of mechanical stimuli by intracellular chemical signals, and (3) to probe the function of AS cell mechano-transduction by genetically and optically perturbing AS cell dynamics and transduction machinery. We expect that these studies will reveal key molecular components of mechano-transduction in epithelial cells and elucidate the principles by which this machinery guides their active mechanical behavior. Methodologically, the project is designed to demonstrate and optimize a widely applicable approach for obtaining quantitative models of cellular mechano-transduction from live cell imaging in intact tissues.