摘要
Genetically encoded biosensors based on fluorescence resonance energy transfer(FRET)have been widely applied to visualize the molecular activity in live cells with high spatiotemporal resolution.The enormous amount of video images and the complex dynamics of signaling events present tremendous challenges for data analysis and demand the development of intelligent and automated imaging analysis methods specifically envisioned for the studies of live cell imaging.We have developed a general correlative FRET imaging method(CFIM)to quantify the subcellular coupling between an enzymatic activity and a phenotypic response in live cells,e.g.at focal adhesions(FAs).CFIM quantitatively evaluated the cause-effect relation-
Genetically encoded biosensors based on fluorescence resonance energy transfer(FRET)have been widely applied to visualize the molecular activity in live cells with high spatiotemporal resolution.The enormous amount of video images and the complex dynamics of signaling events present tremendous challenges for data analysis and demand the development of intelligent and automated imaging analysis methods specifically envisioned for the studies of live cell imaging.We have developed a general correlative FRET imaging method(CFIM)to quantify the subcellular coupling between an enzymatic activity and a phenotypic response in live cells,e.g.at focal adhesions(FAs).CFIM quantitatively evaluated the cause-effect relation-
出处
《医用生物力学》
EI
CAS
CSCD
北大核心
2013年第S1期55-55,共1页
Journal of Medical Biomechanics
