Fӧrster resonance energy transfer (FRET) has been described for more than a century. FRET has become a mainstay for the study of protein localization in living cells and tissues. It has also become widely used in the fields that comprise cellular signaling. FRET-based probes have been developed to monitor second messenger signals, the phosphorylation state of peptides and proteins, and subsequent cellular responses. Here, we discuss the milestones that led to FRET becoming a widely used tool for the study of biological systems: the theoretical description of FRET, the insight to use FRET as a molecular ruler, and the isolation and genetic modification of green fluorescent protein (GFP). Each of these milestones were critical to the development of a myriad of FRET-based probes and reporters in common use today. FRET-probes offer a unique opportunity to interrogate second messenger signals and subsequent protein phosphorylation – and perhaps the most effective approach for study of cAMP/PKA pathways. As such, FRET probes are widely used in the study of intracellular signaling pathways. Yet, somehow, the potential of FRET-based probes to provide windows through which we can visualize complex cellular signaling systems has not been fully reached. Hence we conclude by discussing the technical challenges to be overcome if FRET-based probes are to live up to their potential for the study of complex signaling networks.

Fӧrster 共振能量转移 (FRET) 已经被描述了一个多世纪。FRET 已成为研究活细胞和组织中蛋白质定位的中流砥柱。它也已广泛用于包括细胞信号传导的领域。已经开发出基于 FRET 的探针来监测第二信使信号、肽和蛋白质的磷酸化状态以及随后的细胞反应。在这里，我们讨论了导致 FRET 成为广泛使用的生物系统研究工具的里程碑：FRET 的理论描述、使用 FRET 作为分子标尺的见解以及绿色荧光蛋白 (GFP) 的分离和遗传修饰）。这些里程碑中的每一个对于当今普遍使用的无数基于 FRET 的探针和报告器的开发都至关重要。FRET 探针提供了一个独特的机会来询问第二信使信号和随后的蛋白质磷酸化——也许是研究 cAMP/PKA 通路的最有效方法。因此，FRET 探针广泛用于细胞内信号通路的研究。然而，不知何故，基于 FRET 的探针为我们提供可视化复杂细胞信号系统的窗口的潜力尚未完全实现。因此，我们最后讨论了如果基于 FRET 的探针要发挥其研究复杂信号网络的潜力需要克服的技术挑战。不知何故，基于 FRET 的探针为我们提供可视化复杂细胞信号系统的窗口的潜力尚未完全实现。因此，我们最后讨论了如果基于 FRET 的探针要发挥其研究复杂信号网络的潜力需要克服的技术挑战。不知何故，基于 FRET 的探针为我们提供可视化复杂细胞信号系统的窗口的潜力尚未完全实现。因此，我们最后讨论了如果基于 FRET 的探针要发挥其研究复杂信号网络的潜力需要克服的技术挑战。