Bilateral NSF/BIO-BBSRC: The design logic of Hedgehog-based pattern formation

生物学中的一个关键挑战是了解细胞之间是如何交流和反应的。这在胚胎发育中尤其重要，因为在胚胎中，细胞通讯负责组织组织和创造细胞类型的模式，这些模式是形成功能器官的模板。在过去的几十年里，在识别与组织发育有关的基因和所谓的“信号通路”方面取得了快速进展。然而，我们对这些通路的设计逻辑和动力学知识相对较少，而且通常以可预测的方式干扰或控制这些通路的能力有限。这些途径是如何工作的，它们提供了哪些功能？而且，我们如何预测它们对扰动的反应，或者用它们来控制细胞行为呢？要实现对这些问题的更基本的理解，需要从定性的分子观点转向定量的系统分析。例如，为了了解发育模式的精确度和重复性是如何实现的，并最终为了了解如何在这些过程出错时控制它们或设计新的组织，获得洞察力是必要的。在这个建议中，我们旨在了解产生Hedgehog途径的动力学和基因反应的设计原理。这一途径提供了一个实验上容易处理的例子，说明了参与不同发育过程和病理过程的发育重要信号通路。然而，尽管我们对该途径中的蛋白质、相互作用和反馈环了解很多，但我们对该途径为什么会有这种结构，它在单个细胞中的动态行为，以及它的动力学如何确保组织的正常发育知之甚少。一个关键的限制是缺乏在单个细胞水平上直接读出和控制途径活动的能力，以及将这些数据与组织水平分析联系起来的能力。为了解决这一不足，我们计划结合我们实验室的专业知识，在定义明确的细胞模型系统中结合使用定量单细胞和发育生物学方法。Elowitz实验室在开发和分析来自一系列生物系统的单细胞定量数据方面拥有专业知识；而布里斯科实验室拥有分析神经组织中Hedgehog信号的经验和试剂，负责产生不同的神经元亚型。该项目将开发试剂和方法，以提供对Hedgehog途径的新的定量理解。获得这一通路的系统视野水平将提供对信号如何在发育中的组织中传递的洞察，并揭示Shh通路的哪些特征支持这一点。这些设计原则可能不仅与理解Hedgehog有关，而且可能与理解不同发育过程中涉及的其他信号通路有关。更广泛地说，将数量生物学和系统生物学的见解引入发育生物学和干细胞生物学将推动该领域的发展，并有助于巩固其未来在药物发现、疾病临床前模型以及最终临床应用中的应用。

A key challenge in biology is to understand how cells communicate and respond to one another. This is particularly important in developing embryos where cell communication is responsible for organizing tissues and creating the patterns of cell types that are the template for the formation of functioning organs. Over the last few decades rapid progress has been made in identifying the genes and so-called 'signaling pathways' involved in tissue development. However we have relatively little knowledge of the design logic and dynamics of these pathways, and usually limited ability to perturb or control these pathways in a predictable way. How do these pathways work, and what capabilities do they provide? And, how can we predict their response to perturbations or use them to control cellular behaviors? To achieve a more fundamental understanding of these issues requires a shift in approach from a qualitative molecular view to a quantitative systems analysis. Gaining insight is necessary in order to understand, for instance, how precision and reproducibility of developmental patterning is achieved and ultimately in order to understand how to control these processes when they go wrong or to engineer new tissues.In this proposal we aim to understand the design principles that produce the dynamics and gene responses of the Hedgehog pathway. This pathway provides an experimentally tractable example of a developmentally important signaling pathway involved in diverse developmental processes and pathologies. However, although we know a lot about the proteins, interactions and feedback loops in the pathway we have little understanding of why the pathway has this architecture, how it behaves dynamically in an individual cell, and how its dynamics ensure proper tissue development. A key limitation has been the lack of direct readout and control of pathway activity at the level of individual cells, and the ability to link these data to tissue level assays. To address this deficiency we plan to combine the expertise of our labs and use a combination of quantitative single-cell and developmental biology approaches in well-defined cellular model systems. The Elowitz lab has expertise in developing and analyzing single cell quantitative data from a range of biological systems; whereas the Briscoe lab has experience and reagents to analyse Hedgehog signaling in neural tissue, where it is responsible for generating different neuronal subtypes. The project will develop reagents and methods that provide a new quantitative understanding of the Hedgehog pathway. Obtaining this systems level of view of the pathway will provide insight into how signals are communicated in developing tissues and reveal what features of the Shh pathway support this. These design principles are likely to be relevant for understanding not only Hedgehog but other signaling pathways involved in diverse developmental processes. More generally, introducing insights from quantitative and system biology to developmental and stem cell biology will advance the field and help underpin its future use in drug discovery, preclinical models of disease, and ultimately clinical applications.