Multicolour analysis and local image correlation in confocal microscopy
Multicolour analysis and local image correlation in confocal microscopy
复制标题
DOI：
10.1046/j.1365-2818.1997.1470704.x
复制
发表时间：
1997-01-01
影响因子：
2
通讯作者：
Davoust, J
中科院分区：
文献类型：
--
作者：
Demandolx, D;Davoust, J
多参数荧光显微镜通常用于根据细胞类型和亚细胞细胞器的不同标记来识别细胞类型和亚细胞细胞器，对于较厚的对象，不同多次标记的样品的定量比较需要共焦激光扫描显微镜的三维(3D)采样能力，可用于生成假彩色图像。为了分析这样的三维数据集，我们创建了像素荧光照片表示法，这是对连接多个荧光分布的联合概率密度的估计，这种像素荧光照片还提供了分析图像获取噪声、荧光串扰、荧光光漂白和细胞运动的强大手段。为了识别真正的荧光共定位，我们开发了一种基于局部图像相关图的新方法。通过校正每个荧光通道中背景强度的对比度和局部变化，我们认为像素荧光照片最适合于多荧光图像采集的质量控制，局部图像相关方法更适合于在细胞或亚细胞水平上识别共同定位的结构。这些相关图的阈值可以进一步用于根据多荧光属性来识别和分类生物结构。
Multiparameter fluorescence microscopy is often used to identify cell types and subcellular organelles according to their differential labelling, For thick objects, the quantitative comparison of different multiply labelled specimens requires the three-dimensional (3-D) sampling capacity of confocal laser scanning microscopy, which can be used to generate pseudocolour images. To analyse such 3-D data sets, we have created pixel fluorogram representations, which are estimates of the joint probability densities linking multiple fluorescence distributions, Such pixel fluorograms also provide a powerful means of analysing image acquisition noise, fluorescence cross-talk, fluorescence photobleaching and cell movements. To identify true fluorescence co-localization, we have developed a novel approach based on local image correlation maps. These maps discriminate the coincident fluorescence distributions from the superimposition of noncorrelated fluorescence profiles on a local basis, by correcting for contrast and local variations in background intensity in each fluorescence channel, We believe that the pixel fluorograms are best suited to the quality control of multifluorescence image acquisition, The local image correlation methods are more appropriate for identifying co-localized structures at the cellular or subcellular level. The thresholding of these correlation maps can further be used to recognize and classify biological structures according to multifluorescence attributes.