Multiparameter image cytometry: From confocal micrographs to subcellular fluorograms

Denis Demandolx, Jean Davoust
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Abstract

Multifluorescence labeling is routinely performed to detect the spatial coincidence between several markers within biological specimens. We have recently developed image correlation methods to identify double fluorescent structures by virtue of local similarities between fluorescence distributions. We extend this approach here to analyze statistically the fluorescence distribution of structures of interest on micrographs. This digital cytometry relies mainly on the segmentation of multifluorescence images. Once identified, all objects are analyzed through a range of attributes estimating size, morphology, fluorescence content and mean colocalization level between fluorescence channels. The data sets which are saved in flow cytometry standard (FCS) files, allow multiparameter classification of objects and subpopulation counting. The combination of fluorescence, morphometric and local image correlation attributes has been applied here to compare the frequency of single- and multiple-labeled structures at the subcellular level.

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多参数图像细胞术:从共聚焦显微图到亚细胞荧光图
多荧光标记通常用于检测生物标本中几个标记之间的空间重合。我们最近开发了图像相关方法,利用荧光分布之间的局部相似性来识别双荧光结构。我们在这里扩展了这种方法,以统计分析显微照片上感兴趣的结构的荧光分布。这种数字细胞术主要依赖于多荧光图像的分割。一旦被识别,所有的物体通过一系列属性来分析,估计大小、形态、荧光含量和荧光通道之间的平均共定位水平。数据集保存在流式细胞术标准(FCS)文件中,允许对对象进行多参数分类和亚群计数。结合荧光、形态计量学和局部图像相关属性,在亚细胞水平上比较了单标记和多标记结构的频率。
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