Studies of bacterial communities, biofilms and microbiomes, are multiplying due to their impact on health and ecology. Live imaging of microbial communities requires new tools for the robust identification of bacterial cells in dense and often inter-species populations, sometimes over very large scales. Here, we developed MiSiC, a general deep-learning-based 2D segmentation method that automatically segments single bacteria in complex images of interacting bacterial communities with very little parameter adjustment, independent of the microscopy settings and imaging modality. Using a bacterial predator-prey interaction model, we demonstrate that MiSiC enables the analysis of interspecies interactions, resolving processes at subcellular scales and discriminating between species in millimeter size datasets. The simple implementation of MiSiC and the relatively low need in computing power make its use broadly accessible to fields interested in bacterial interactions and cell biology.

中文翻译：

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MiSiC，一种基于深度学习的通用方法，用于复杂细菌群落的高通量细胞分割

由于对健康和生态的影响，对细菌群落、生物膜和微生物组的研究正在成倍增加。微生物群落的实时成像需要新的工具来可靠地识别密集且通常是跨物种种群中的细菌细胞，有时甚至是非常大规模的。在这里，我们开发了 MiSiC，这是一种基于深度学习的通用 2D 分割方法，可自动分割相互作用细菌群落复杂图像中的单个细菌，只需很少的参数调整，独立于显微镜设置和成像模式。使用细菌捕食者-猎物相互作用模型，我们证明 MiSiC 能够分析种间相互作用、解决亚细胞尺度的过程并区分毫米大小数据集中的物种。