Quantitative fluorescence co-localization to study protein-receptor complexes.
Quantitative fluorescence co-localization to study protein-receptor complexes.
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DOI：
10.1007/978-1-62703-398-5_16
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发表时间：
2013
影响因子：
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通讯作者：
Shanica Pompey;P. Michaely;K. Luby‐Phelps
中科院分区：
文献类型：
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作者：
Shanica Pompey;P. Michaely;K. Luby‐Phelps
荧光显微镜可用于通过共定位分析定量评估配体与其受体之间、两个大分子之间或大分子与特定细胞内区室之间的相互作用。一般来说，这种分析涉及用不同的荧光团标记潜在的相互作用伙伴——通过直接标记小配体、通过荧光 cDNA 构建体的表达、通过免疫荧光标记或通过这些方法的某种组合。样本的双通道数字图像中每个像素处的两种荧光团的荧光强度的成对比较揭示了两种荧光团都存在的区域。通过适当的协议，可以解释图像数据以指示潜在的交互伙伴共定位的位置。请记住光学显微镜的分辨率有限，共定位通常用于支持两个分子相互作用的说法。评估共定位的所有定量方法都从识别两个颜色通道的强度均高于背景的像素开始。通常，这涉及两个连续的图像分割步骤：第一个排除两个通道均不高于背景的像素，第二个设置重叠阈值以排除仅存在一个颜色通道的像素。分割之后，可以计算各种定量测量来描述两个颜色通道重叠的剩余像素子集。这些指标的范围从简单计算发生重叠的像素分数到更复杂的图像相关指标。可以采用附加约束来区分真正的共定位和随机重叠。最后，仅显示共定位像素的图像图可以显示为附加图像通道，以便可视化共定位像素的空间分布。一些商业和开源软件解决方案提供了这种类型的共定位分析，使得图像分割和指标计算相对简单。作为一个例子，我们提供了荧光标记脂蛋白与 LDL 受体 (LDLR) 和早期内体标记 EEA1 的时间依赖性共定位的方案。
Fluorescence microscopy can be used to assess quantitatively the interaction between a ligand and its receptor, between two macromolecules, or between a macromolecule and a particular intracellular compartment by co-localization analysis. In general, this analysis involves tagging potential interacting partners with distinct fluorophores—by direct labeling of a small ligand, by expression of fluorescent cDNA constructs, by immunofluorescence labeling, or by some combination of these methods. Pairwise comparison of the fluorescence intensity of the two fluorophores at each pixel in a two channel digital image of the sample reveals regions where both are present. With appropriate protocols, the image data can be interpreted to indicate where the potential interacting partners are co-localized. Keeping in mind the limited resolution of the light microscope, co-localization is often used to support the claim that two molecules are interacting.All quantitative methods for evaluating co-localization begin with identifying the pixels where the intensities of both color channels are above background. Typically this involves two sequential image segmentation steps: the first to exclude pixels where neither channel is above background, and the second to set overlap thresholds that exclude pixels where only one color channel is present. Following segmentation, various quantitative measures can be computed to describe the remaining subset of pixels where the two color channels overlap. These metrics range from simple calculation of the fraction of pixels where overlap occurs to more sophisticated image correlation metrics. Additional constraints may be employed to distinguish true co-localization from random overlap. Finally, an image map showing only the co-localized pixels may be displayed as an additional image channel in order to visualize the spatial distribution of co-localized pixels. Several commercial and open source software solutions provide this type of co-localization analysis, making image segmentation and calculation of metrics relatively straightforward. As an example, we provide a protocol for the time-dependent co-localization of fluorescently tagged lipoproteins with LDL receptor (LDLR) and with the early endosome marker EEA1.