| Abstract: | 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. |
