We here propose an automated pipeline for the microscopy image-based characterization of catalytically active inclusion bodies (CatIBs), which includes a fully automatic experimental high-throughput workflow combined with a hybrid approach for multi-object microbial cell segmentation. For automated microscopy, a CatIB producer strain was cultivated in a microbioreactor from which samples were injected into a flow chamber. The flow chamber was fixed under a microscope and an integrated camera took a series of images per sample. To explore heterogeneity of CatIB development during the cultivation and track the size and quantity of CatIBs over time, a hybrid image processing pipeline approach was developed, which combines an ML-based detection of in-focus cells with model-based segmentation. The experimental setup in combination with an automated image analysis unlocks high-throughput screening of CatIB production, saving time and resources. Biotechnological relevance - CatIBs have wide application in synthetic chemistry and biocatalysis, but also could have future biomedical applications such as therapeutics. The proposed hybrid automatic image processing pipeline can be adjusted to treat comparable biological microorganisms, where fully data-driven ML-based segmentation approaches are not feasible due to the lack of training data. Our work is the first step towards image-based bioprocess control.
翻译:我们在此建议为催化活性包容机构(CatIBs)的显微镜图像定性自动管道,其中包括完全自动的实验性高通量工作流程,加上多球微生物细胞分解的混合方法;对于自动显微镜,CatIB生产商株式种植在微生物体中,样品从该微生物体中注入进入流动室;流动室在显微镜下固定,一个综合照相机对每个样本进行一系列图像;为了探索CatIB公司在种植期间开发的异质性,并跟踪CatIB公司的规模和数量,开发了一个混合式图像处理管道方法,将基于ML的局部细胞检测与基于模型的分解结合起来;对于自动图像分析结合,使对CatIB公司生产、节省时间和资源进行高通量筛选;生物技术相关性-CatIBs在合成化学和生物卡特解析中广泛应用,但也有可能在未来应用生物医学等生物医学应用;拟议的混合自动图像处理管道处理方法可以调整,以便处理可比较的生物微生物,而以ML公司进行完全以数据驱动的分级培训,而没有以适当的分级技术。