Development of green fluorescent protein technology

点定位超分辨率技术，如光激活定位显微镜（PALM），使荧光蛋白嵌合体的成像能够在纳米尺度上揭示遗传表达蛋白的组织，分子密度足够高，可以提供结构背景。在PALM中，对许多光激活荧光蛋白分子的稀疏子集进行连续光激活和随后的漂白。然后，通过点扩散函数的统计拟合来确定单个分子的荧光发射中心，从而在近分子分辨率下定位。来自所有子集的聚合位置信息然后组装成超分辨率图像，其中单个荧光分子在高分子密度（高达10,000个分子/微米平方）下被隔离。

Point-localization superresolution techniques such as photoactivated localization microscopy (PALM) enable the imaging of fluorescent protein chimeras to reveal the organization of genetically-expressed proteins on the nanoscale with a density of molecules high enough to provide structural context. In PALM, serial photoactivation and subsequent bleaching of numerous sparse subsets of photoactivated fluorescent protein molecules is performed. Individual molecules are then localized at near molecular resolution by determining their centers of fluorescent emission via a statistical fit of their point-spread-function. The aggregate position information from all subsets is then assembled into a super-resolution image, in which individual fluorescent molecules are isolated at high molecular densities (up to 10,000 molecules/micron squared). While PALM is a powerful approach for investigating protein organization, tools for quantitative, spatial analysis of PALM datasets are largely missing. We continued to use and develop a pair-correlation analysis method with PALM (PC-PALM) that enables complex patterns of protein organization across the plasma membrane to be analyzed. The approach uses an algorithm to distinguish a single protein with multiple appearances from clusters of proteins. This enables quantification of different parameters of spatial organization, including the presence of protein clusters, their size, density and abundance in the plasma membrane. Using this method, we demonstrated distinct nanoscale organization of plasma-membrane proteins with different membrane anchoring and lipid partitioning characteristics. The ability to unambiguously distinguish more than a few different labels in a single fluorescence image has been severely hampered by the excitation cross-talk and emission bleed-through of fluorophores with highly overlapping spectra. To overcome this problem, we developed a cell labeling, image acquisition and image analysis approach to study the spatial distribution of six different organelles within eukaryotic cells. Cells were transfected with six fluorescent fusion protein markers of organelles or labeled with compartment-specific fluorescent chemical dyes to highlight the following six subcellular compartments: peroxisomes, lysosomes, ER, mitochondria, Golgi and lipid droplets. Live-cell, time-lapse images were acquired, and then linear unmixing algorithms were applied to every pixel in the image deconvolving spectrally-overlapping fluorophores. We also developed a novel image analysis pipeline for identifying regions within our images where two or more organelles contacted each other. We are using this approach to understand the full systems-level spatial organization of eukaryotic organelles under different physiological conditions.