首页> 美国卫生研究院文献>other >NSOM/QD-based fluorescence-topographic image fusion directly reveals nano-spatial peak-valley polarities of CD69 and CD71 activation molecules on cell-membrane fluctuations during T-cell activation
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NSOM/QD-based fluorescence-topographic image fusion directly reveals nano-spatial peak-valley polarities of CD69 and CD71 activation molecules on cell-membrane fluctuations during T-cell activation

机译:基于NSOM / QD的荧光 - 地形图像融合直接显示CD69和CD71激活分子的纳米空间峰值极性在T细胞活化期间细胞膜波动

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摘要

Nano-spatial distribution of cell surface molecules on cell membrane fluctuations during T-cell activation has not been reported. In this study, we innovated application of near-field scanning optical microscopy (NSOM)/quantum dots (QD)-based nanotechnology through three-dimensional image fusion algorithm to merge the simultaneously-obtained dual-color fluorescence information and three-dimensional topography. This novel imaging system made it possible to visualize nano-spatial distribution and organization of early-activation molecules CD69 and late-activation molecules CD71 on cell-membrane fluctuations during T-cell activation. Interestingly, most CD69 molecules were clustered to form 250–500 nm nano-domains polarizing predominantly in the peak of the cell-membrane fluctuations. In contrast, although CD71 molecules were also clustered as 250–500 nm nano-domains, they polarized dominantly in the valley of the cell-membrane fluctuations. The peak-valley polarities of CD69 nano-domains and CD71 nano-domains implied their different functions. CD69 nano-domains polarizing on membrane-peak fluctuations might serve as transient platforms driving TCR/CD3-induced signaling and activation, whereas CD71 nano-domains distributing in the membrane-valley fluctuations appeared to facilitate iron uptake for increased metabolisms in T-cell activation. Importantly, this NSOM/QD-based fluorescence-topographic image fusion provides a powerful tool to visualize nano-spatial distribution of cell-surface molecules on cell-membrane fluctuations and enable better understanding of distribution-function relationship.

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