Jena Microbial Resource Collection, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Jena, Germany.
Institute of Microbiology, Faculty of Biological Sciences, Friedrich Schiller University Jena, Jena, Germany.
Environ Microbiol. 2019 Dec;21(12):4563-4581. doi: 10.1111/1462-2920.14752. Epub 2019 Aug 5.
Mucormycoses are life-threatening infections that affect patients suffering from immune deficiencies. We performed phagocytosis assays confronting various strains of Lichtheimia species with alveolar macrophages, which form the first line of defence of the innate immune system. To investigate 17 strains from four different continents in a comparative fashion, transmitted light and confocal fluorescence microscopy was applied in combination with automated image analysis. This interdisciplinary approach enabled the objective and quantitative processing of the big volume of image data. Applying machine-learning supported methods, a spontaneous clustering of the strains was revealed in the space of phagocytic measures. This clustering was not driven by measures of fungal morphology but rather by the geographical origin of the fungal strains. Our study illustrates the crucial contribution of machine-learning supported automated image analysis to the qualitative discovery and quantitative comparison of major factors affecting host-pathogen interactions. We found that the phagocytic vulnerability of Lichtheimia species depends on their geographical origin, where strains within each geographic region behaved similarly, but strongly differed amongst the regions. Based on this clustering, we were able to also classify clinical isolates with regard to their potential geographical origin.
毛霉病是一种危及生命的感染,影响免疫功能低下的患者。我们进行吞噬作用测定,用肺泡巨噬细胞来对抗各种光细菌属菌株,这些巨噬细胞构成先天免疫系统的第一道防线。为了以比较的方式研究来自四大洲的 17 株菌株,我们将透射光和共聚焦荧光显微镜与自动图像分析相结合。这种跨学科的方法能够客观和定量地处理大量的图像数据。应用机器学习支持的方法,在吞噬作用测量的空间中揭示了菌株的自发聚类。这种聚类不是由真菌形态学的措施驱动的,而是由真菌菌株的地理来源驱动的。我们的研究说明了机器学习支持的自动图像分析对定性发现和定量比较影响宿主-病原体相互作用的主要因素的重要贡献。我们发现,光细菌属的吞噬易感性取决于它们的地理来源,每个地理区域内的菌株表现相似,但在不同区域之间差异很大。基于这种聚类,我们还能够根据其潜在的地理来源对临床分离株进行分类。