Decoding the Logic of Cellular Signaling Through the Integration of Dynamic, Single-Cell and Multiplexed Methods

项目总结 细胞通过信号通路对多种刺激做出反应。这些途径调节转录。 因子活性、靶基因的表达以及细胞状态和决策的变化。现在是很好的- 证实了通路活动的时间动力学在信号转导中起着关键作用。然而， 破译这些动态模式决定细胞反应的逻辑仍然是一个具有挑战性的目标。这个 当这些反应是：(I)受制于由多个 由共同或不同的配体-受体相互作用编码的通路，(Ii)由多种 独立或共同调节的转录因子，以及(Iii)受细胞环境改变，例如分化 州政府。这些挑战，尽管对细胞信号机制的了解有所增加，但 使我们准确预测细胞对压力、配体和药物反应的能力变得复杂。我们的长期合作 目标是了解细胞如何从严格调控的信号组合中处理动态信息 调节下游转录因子动态的途径，以及这种动态如何协调两者 “上下文相关”和“特定于刺激”的反应。我们建议的研究计划侧重于激活器 蛋白1(AP-1)，转录因子的经典范例，细胞利用它来协调对 环境变化的多样性，从而决定是划分、区分、适应环境，还是 去死吧。虽然AP-1因子的分子调控已经被广泛研究，但它们是如何发挥作用的 作为一个动态网络，以及这个网络如何整合ERK、JNK和p38信号模式来调节 驱动不同的和上下文相关的细胞决定的基因表达程序仍然不清楚。 知识差距在很大程度上是由于缺乏系统范围的测量、单细胞精度、 以及以前对AP-1动力学研究中的计算建模，其中 AP-1家族蛋白(包括Jun、Fos和密切相关的ATF亚家族)，其 互动、翻译后修改、上游监管机构及其合作伙伴 没有完全规划好。在这个研究项目中，我们将开发一个综合平台，结合高性能的 吞吐量、高度多路复用测量、活细胞和固定细胞中的单细胞技术、全基因组 分析和计算建模，作为克服这些差距和挑战的一种手段。我们将使用这些 工具用于：(1)揭示AP-1动力学的不同组合模式如何调节不同范围的 看似无关的功能，(2)解码ERK中的刺激特定信息编码的逻辑， JNK和p38通路动力学被传递到AP-1网络，以及(3)定义了 该网络将这些信息与细胞内在因素相结合，以推动上下文相关的决策。从一个 更好地了解这些基本机制，我们就可以学会提高健康的反应 细胞对有害刺激的反应，并制定策略，在必要时诱导对不健康细胞的选择性杀伤。

PROJECT SUMMARY Cells respond to a wide range of stimuli through signaling pathways. These pathways modulate transcription factor activities, expression of target genes and changes in cellular states and decisions. It is now well- established that the temporal dynamics of pathway activities play a key role in signal transduction. However, decoding the logic by which these dynamic patterns determine cellular response is still a challenging goal. The challenge is particularly formidable when these responses are: (i) subject to combinatorial control by multiple pathways encoded by common or distinct ligand-receptor interactions, (ii) mediated by a multiplicity of independent or co-regulated transcription factors, and (iii) altered by the cellular context, e.g. differentiation state. These challenges, despite an increased understanding of cellular signaling mechanisms, have complicated our ability to accurately predict the response of cells to stress, ligands and drugs. Our long-term goal is to understand how cells process dynamic information from combinations of tightly regulated signaling pathways to modulate downstream transcription factor dynamics, and how such dynamics coordinate both “context-dependent” and “stimulus-specific” responses. Our proposed research program focuses on Activator Protein 1 (AP-1), a classical paradigm for transcription factors, which cells utilize to orchestrate responses to a variety of environmental changes, and thereby decide whether to divide, differentiate, adapt to environment, or die. While the molecular regulation of the AP-1 factors have been extensively investigated, how they function as a dynamic network, and how this network integrates patterns of ERK, JNK and p38 signaling to regulate gene expression programs that drive diverse and context-dependent cell decisions, have remained unclear. The gap in knowledge has been largely due to the lack of system-wide measurements, single-cell precision, and computational modeling in the previous studies of AP-1 dynamics, in which interdependencies between a whole array of AP-1 family proteins (including Jun, Fos and closely related ATF sub-families), their interactions, post-translational modifications, upstream regulators and their partners have remained incompletely mapped out. In this research program, we will develop an integrated platform, combining high- throughput, highly multiplexed measurements, single-cell technologies in live and fixed cells, genome-wide analysis and computational modeling, as a means to overcome these gaps and challenges. We will use these tools to: (1) uncover how distinct combinatorial patterns of AP-1 dynamics mediate a diverse range of seemingly unrelated functions, (2) decode the logic by which stimulus-specific information encoded in ERK, JNK and p38 pathway dynamics is transmitted to the AP-1 network, and (3) define the mechanisms by which the network integrates this information with cell-intrinsic factors to drive context-dependent decisions. From a better understanding of these fundamental mechanisms, we can learn to improve the responses of healthy cells to harmful stimuli, and develop strategies to induce selective killing in unhealthy cells when necessary.