Interrogation and interpretation of protein kinase signaling dynamics at single-cell resolution

摘要 越来越多的证据表明，激酶信号传导的动力学行为，即，动力学模式 激活/去激活过程可以决定细胞对不同扰动的响应。理解这些 动力学可以帮助预测细胞命运，例如药物治疗的活/死结果。然而，这项任务 需要在细胞表型的背景下解决单细胞激酶信号传导动力学， 技术挑战。虽然基因编码报告系统可以解决一些需求， 实施限于容易转染的细胞系，从而限制了其翻译影响。到 为了克服这些挑战，我们的团队开发了一种研究激酶信号传导活性的化学方法， 在活的单细胞中，使用基于环肽的成像探针。在这个MIRA提案中，我们寻求扩大 化学工具包，包括基于多环肽的亲和标签，并开发高度特异性的 成像探头（项目1a）。我们还将探索化学策略，设计一种通用的探针传递标签 （项目1b）。通过将这些工具与其他成熟的单细胞技术和多变量 分析方法，我们计划解决两个突出的生物学问题： 蛋白激酶信号传导活动与表型异质性的联系（项目2）？不同的激酶 信号传导动力学协调细胞对外部扰动的反应，例如细胞的存活/死亡结果。 靶向激酶抑制剂（项目3）？为了回答这些问题，我们进行了初步研究，重点是 使用人胶质母细胞瘤细胞系的AKT信号传导。我们发现抑制AKT或其上游信号蛋白， （EGFR）均能显著改变AKT信号转导的动力学特征。我们还证明了一个神经 网络算法可以利用AKT的动力学模式来预测细胞是否能在AKT抑制下存活 信令配置文件作为输入。根据我们的数据，我们提出了两个假设：第一，激酶的模式 信号传导动力学由上游信号传导蛋白的丰度和活性形成。第二，早 激酶信号传导中的应答决定靶向激酶抑制剂的治疗结果。在项目2&3中，我们将 通过研究一组细胞系中的激酶信号动力学来测试这些假设，有和没有外部 扰动我们将启动研究，重点是AKT信号传导，并作为项目的新成像探针， 1成为可用，我们将扩大我们的工作，包括其他信令模块。成功执行 拟议的研究将提供一套新的化学探针和使能技术， 激酶信号动力学在单细胞分辨率。它还将深入了解这种动态如何连接 表型异质性和管理特定的细胞反应。

ABSTRACT Increasing evidence has demonstrated that the dynamical behaviors of kinase signaling, i.e., kinetic patterns of activation/deactivation process, can dictate the cells' responses to different perturbations. Understanding such dynamics can help to predict cell fates, such as live/dead outcomes of drug treatments. However, this task requires resolving single-cell kinase signaling dynamics in the context of cellular phenotypes, which is technologically challenging. Although genetically encoded reporting systems can address some of the needs, its implementation is restricted to easily transfectable cell lines, thereby limiting its translational impacts. To overcome those challenges, our group has developed a chemical method for studying kinase signaling activities in living single cells, using cyclic peptide-based imaging probes. In this MIRA proposal, we seek to expand the chemical toolkit to include multicyclic peptide-based affinity tags and develop a repertoire of highly specific imaging probes (Project 1a). We will also explore chemical strategies to devise a universal probe delivery tag (Project 1b). By combining these tools with other well-established single-cell technologies and multivariate analysis methods, we plan to address two outstanding biological questions: How do the unique kinetic features of protein kinase signaling activities link to phenotypical heterogeneity (Project 2)? How does diverse kinase signaling dynamics orchestrate cellular responses to external perturbations, such as live/dead outcomes of targeted kinase inhibitors (Project 3)? To answer those questions, we performed preliminary studies focusing on AKT signaling using a human glioblastoma cell line. We found that inhibiting AKT or its upstream signaling protein (EGFR) can both significantly change the kinetic features of AKT signaling. We also demonstrated that a neural network algorithm could predict whether a cell can survive AKT inhibition, using the kinetic patterns of AKT signaling profile as the input. Based on our data, we propose two hypotheses: First, the pattern of kinase signaling kinetics is shaped by the abundance and activities of upstream signaling proteins. Second, early responses in kinase signaling govern therapeutic outcomes of targeted kinase inhibitors. In projects 2&3, we will test those hypotheses by studying kinase signaling dynamics in a panel of cell lines, with and without external perturbations. We will initiate the studies with a focus on AKT signaling, and as new imaging probes from Project 1 become available, we will expand our work to include other signaling modules. Successful execution of the proposed research will provide a suite of novel chemical probes and enabling technologies for interrogating kinase signaling dynamics at single-cell resolution. It will also generate insights into how such dynamics connects to phenotypical heterogeneity and governs specific cellular responses.