Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:3874-3877. doi: 10.1109/EMBC48229.2022.9871325.
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.
我们在这里提出了一种基于显微镜图像的催化活性包含体（CatIB）特征分析的自动化流水线，该流水线包括一个全自动的实验高通量工作流程，以及一种用于多目标微生物细胞分割的混合方法。对于自动化显微镜，将 CatIB 产生菌在微生物反应器中培养，从该反应器中注入样品到流动室中。流动室固定在显微镜下，集成相机为每个样品拍摄一系列图像。为了探索培养过程中 CatIB 发育的异质性，并跟踪 CatIB 随时间的大小和数量，开发了一种混合图像处理流水线方法，该方法将基于 ML 的聚焦细胞检测与基于模型的分割相结合。实验装置与自动化图像分析相结合，实现了 CatIB 生产的高通量筛选，节省了时间和资源。生物技术相关性-CatIB 在合成化学和生物催化中有广泛的应用，但也可能在未来的生物医学应用中具有治疗作用。所提出的混合自动图像处理流水线可以调整以处理类似的生物微生物，由于缺乏训练数据，完全基于数据驱动的 ML 分割方法是不可行的。我们的工作是朝着基于图像的生物过程控制迈出的第一步。
