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In Cell Biology, it is quite frequent that within a population not all cells show the same phenotype, a phenomenon known as 'penetrance' that has to be met by statistical approaches. Quantitative morphometry precisely aims at the statistically validated numerical characterization of various objects such as the different cell types or particular sub-cellular structures (for example the organelles). It includes counting objects, calculation of their diameter, surface, volume, level of the co-localization of different antigens etc. The recognition of the cellular and sub-cellular structures can either be done on the basis of their particular morphology (histochemistry) or on the basis of their fluorescence (using protein or RNA reporters). A key aspect of our work consists in the segmentation of the images (see the illustration), i.e. the exact determination of the parameters used by the imaging software for the autonomous recognition and discrimination of the objects of interest. By combining automated analysis techniques (high-content analysis), which allow to analyze numerous samples without the intervention of a human operator, with software trained to perform automatic recognition and morphometric analysis, we will be in a position to, for example, (i) determine the number of bacteria in the cytoplasm of macrophages, (ii) test the effects of dozens of synthetic molecules ('drug design') on stem cell differentiation or (iii) on the sub-cellular localization pattern of antigens of interest (for ex. the dynamic re-localization of a membrane receptor in the cytoplasm). Macroscopic applications such as lytic plaque analysis or the calculation of the relative distribution of several species of pathogenic organisms are possible. The development of working protocols is done in collaboration with the RNA metabolism laboratory of the Université Libre de Bruxelles.
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