【报告题目】Correlative FRET imaging for studying molecular interaction in living cells

【报 告 人】Dr. Yingxiao Wang（王英晓）

美国加利福尼亚大学圣迭戈分校（University of California, San Diego，UCSD）

【报告时间】2013年6月5日  下午 1：30

【报告地点】生物药学楼3-105

【联 系 人】力学生物学研究所 齐颖新， This e-mail address is being protected from spambots. You need JavaScript enabled to view it.

【报告人简介】王英晓博士现任加利福尼亚大学圣迭戈分校（UCSD）生物工程系（Dept. of Bioengineering, University of California, San Diego）副教授，从事力学生物学、整合生物学、新型分子传感器、分子工程和细胞分子生物学研究，在荧光共振能量转移（FRET） 技术发展和应用方面有卓越的贡献，在Nature、Nature Communications和PNAS等国际著名期刊发表论文60余篇，获美国NSF青年科学家奖、NIH独立科学家奖、Grainger奖和Xerox奖等。

【报告内容（摘要）】The spatiotemporal coupling between molecular activities and local functional outcomes at subcellular regions largely governs cellular physiology. However, the dynamic coordination between kinase activities and functional outcomes such as focal adhesion (FA) disassembly at the subcellular FA sites remains elusive. Here, we have developed a correlative FRET imaging method to quantify the subcellular coupling between Src kinase activation and FA disassembly at cell periphery locations in live cells. Regression analysis demonstrated that the amount of FA disassembled at cell periphery was linearly dependent on the level of Src kinase activation in fibroblasts stimulated with the platelet-derived growth factor (PDGF). Further analysis revealed that the amount of FA disassembly per unit of Src activation, and the dynamic coupling between Src activation and FA disassembly, was regulated by the concentration of fibronectin (FN) where cells were seeded. The coupling between Src activation and FA disassembly increased when integrin was inhibited, evidenced by an increased amount of FA disassembly per unit of Src activation as well as shortening of the time lag between Src activation and FA disassembly. In contrast, the inhibition of v caused a decoupling of Src activation and FA disassembly. Thus, distinct spatiotemporal coupling between Src activation and the disassembly of two FA populations mediated by different integrin subtypes was revealed by our live-cell correlation analysis approach. Our work hence highlights the power of computational bio-imaging in single live cells in deciphering the complex and noisy signaling network underlying dynamic cellular processes.