Principles of Mechanochemical Signal Integration Underlying Developmental Robustness

项目概要/摘要 我的研究项目旨在了解健壮胚胎组织的细胞机制 模式化尽管环境波动，组织发展几乎相同的模式，形状， 在不同阶段匹配的个体之间的大小。相比之下，在体外生长的人工组织是高度 变量，限制了它们对疾病建模的价值。单细胞如何可靠地感知和控制 组织参数在发育生物学和组织学中仍然是一个重要的开放问题 工程. 从传统的生物化学信号控制模式的焦点出发，我的研究目标是 了解细胞如何整合生化和机械信号，以检测和响应 组织模式和形状的变化。我们在活体成像方面的专业知识赋予了我们力量， 斑马鱼遗传学、计算建模和操纵组织的创新方法 生物化学和机械环境。利用这些多学科工具，我们将测试细胞如何 结合机械信息来检测组织规模的变化， 单独的形态发生素信号。首先，我们将确定细胞是否可以感知细胞类型的身份， 它们的邻居通过基于钙粘蛋白的粘附，并使用此信息来修改细胞命运 决策和纠正组织图案化中的错误。其次，我们将识别机械响应 形态发生素信号通路，并研究机械化学串扰是否允许细胞 以更准确地推断它们在经历形态发生的组织中的相对位置。 通过揭示组织参数是如何被生化和机械信号编码的， 细胞解码机械化学信号，以感知和控制组织参数的变化， 研究将阐明从组织规模变化到细胞反应的信息流。我们的工作 将建立细胞粘附和机械力作为组织信息引导的关键通道 在早期组织图案化事件中准确的细胞决定，这是一种新的范式，将影响我们的研究。 了解疾病的发展，机械后果和工程策略， 人造组织

Project Summary / Abstract My research program aims to understand cellular mechanisms underlying robust embryonic tissue patterning. Despite environmental fluctuations, tissues develop nearly identical patterns, shapes, and sizes among different stage-matched individuals. In contrast, artificial tissues grown in vitro are highly variable, limiting their value for disease modeling. How single cells can reliably sense and control tissue parameters remains an important open question in both developmental biology and tissue engineering. Departing from the traditional focus of patterning control by biochemical signaling, my research aims to understand how cells integrate biochemical and mechanical signals to detect and respond to changes in tissue pattern and shape. We are empowered by our expertise in in vivo live imaging, zebrafish genetics, computational modeling, and innovative approaches to manipulating tissues’ biochemical and mechanical environments. Using these multidisciplinary tools, we will test how cells incorporate mechanical information to detect tissue-scale changes that are challenging to detect by morphogen signaling alone. First, we will determine whether cells can sense the cell type identity of their neighbors through cadherin-based adhesion, and use this information to modify cell fate decisions and correct errors in tissue patterning. Second, we will identify mechano-responsive morphogen signaling pathways and investigate whether mechanochemical crosstalk allows the cells to infer their relative positions more accurately in tissues undergoing morphogenesis. By revealing how tissue parameters are encoded by biochemical and mechanical signals, and how cells decode mechanochemical signals to sense and control changes in tissue parameters, our research will elucidate the information flow from tissue-scale changes to cellular responses. Our work will establish cell adhesion and mechanical force as key channels of tissue information directing accurate cellular decisions in early tissue patterning events, a novel paradigm that will impact our understanding of development, mechanical consequences in diseases, and engineering strategies for artificial tissues.