| Summary: | Studying the pathobiology of the fungus Aspergillus fumigatus has gained a lot of attention in recentyears. This is due to the fact that this fungus is a human pathogen that can cause severe diseases, likeinvasive pulmonary aspergillosis in immunocompromised patients. Because alveolar macrophagesbelong to the first line of defense against the fungus, here, we conducted an image-based study on thehost-pathogen interaction between murine alveolar macrophages and A. fumigatus. This is achievedby an automated image analysis approach that uses a combination of thresholding, watershedsegmentation and feature-based object classification. In contrast to previous approaches, ouralgorithm allows for the segmentation of individual macrophages in the images and this enables us tocompute the distribution of phagocytosed and macrophage-adherent conidia over all macrophages.The automated imaged-based analysis provides access to all cell-cell interactions in the assay andthereby represents a framework that enables comprehensive computation of diverse characteristicparameters and comparative investigation for different strains. We here applied automated imageanalysis to confocal laser scanning microscopy images of the two wild-type strains ATCC 46645 andCEA10 of A. fumigatus and investigated the ability of macrophages to phagocytose the respectiveconidia. It is found that the CEA10 strain triggers a stronger response of the macrophages as revealedby a higher phagocytosis ratio and a larger portion of the macrophages being active in thephagocytosis process.
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