The Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MA, USA.
Bioessays. 2012 May;34(5):396-405. doi: 10.1002/bies.201200022. Epub 2012 Mar 23.
Pointillistic based super-resolution techniques, such as photoactivated localization microscopy (PALM), involve multiple cycles of sequential activation, imaging, and precise localization of single fluorescent molecules. A super-resolution image, having nanoscopic structural information, is then constructed by compiling all the image sequences. Because the final image resolution is determined by the localization precision of detected single molecules and their density, accurate image reconstruction requires imaging of biological structures labeled with fluorescent molecules at high density. In such image datasets, stochastic variations in photon emission and intervening dark states lead to uncertainties in identification of single molecules. This, in turn, prevents the proper utilization of the wealth of information on molecular distribution and quantity. A recent strategy for overcoming this problem is pair-correlation analysis applied to PALM. Using rigorous statistical algorithms to estimate the number of detected proteins, this approach allows the spatial organization of molecules to be quantitatively described.
基于点扩散的超分辨率技术，如光激活定位显微镜（PALM），涉及多个连续激活、成像和单个荧光分子精确定位的循环。通过编译所有图像序列，构建具有纳米级结构信息的超分辨率图像。由于最终图像分辨率取决于检测到的单个分子的定位精度及其密度，因此需要以高荧光分子密度对生物结构进行成像，以实现准确的图像重建。在这样的图像数据集中，光子发射的随机变化和中间暗态会导致单个分子的识别出现不确定性。这反过来又妨碍了对分子分布和数量的丰富信息的充分利用。最近的一种克服此问题的策略是将对关联分析应用于 PALM。通过使用严格的统计算法来估计检测到的蛋白质数量，该方法可以定量描述分子的空间组织。
