Live-cell imaging (LCI) technology enables the detailed spatio-temporal characterization of living cells at the single-cell level, which is critical for advancing research in the life sciences, from biomedical applications to bioprocessing. High-throughput setups with tens to hundreds of parallel cell cultivations offer the potential for robust and reproducible insights. However, these insights are obscured by the large amount of LCI data recorded per experiment. Recent advances in state-of-the-art deep learning methods for cell segmentation and tracking now enable the automated analysis of such large data volumes, offering unprecedented opportunities to systematically study single-cell dynamics. The next key challenge lies in integrating these powerful tools into accessible, flexible, and user-friendly workflows that support routine application in biological research. In this work, we present acia-workflows, a platform that combines three key components: (1) the Automated live-Cell Imaging Analysis (acia) Python library, which supports the modular design of image analysis pipelines offering eight deep learning segmentation and tracking approaches; (2) workflows that assemble the image analysis pipeline, its software dependencies, documentation, and visualizations into a single Jupyter Notebook, leading to accessible, reproducible and scalable analysis workflows; and (3) a collection of application workflows showcasing the analysis and customization capabilities in real-world applications. Specifically, we present three workflows to investigate various types of microfluidic LCI experiments ranging from growth rate comparisons to precise, minute-resolution quantitative analyses of individual dynamic cells responses to changing oxygen conditions. Our collection of more than ten application workflows is open source and publicly available at https://github.com/JuBiotech/acia-workflows.

翻译：活细胞成像（LCI）技术能够在单细胞水平上对活细胞进行详细的时空表征，这对于推动生命科学研究（从生物医学应用到生物加工）至关重要。具有数十至数百个平行细胞培养的高通量设置为获得稳健且可重复的见解提供了潜力。然而，这些见解被每次实验记录的大量LCI数据所掩盖。用于细胞分割和跟踪的最先进深度学习方法的最新进展，现在使得能够对此类海量数据进行自动化分析，为系统研究单细胞动力学提供了前所未有的机遇。下一个关键挑战在于将这些强大工具集成到可访问、灵活且用户友好的工作流中，以支持生物学研究中的常规应用。在本工作中，我们提出了acia-workflows平台，该平台结合了三个关键组件：（1）Automated live-Cell Imaging Analysis (acia) Python库，它支持图像分析流程的模块化设计，提供了八种深度学习分割与跟踪方法；（2）将图像分析流程、其软件依赖、文档和可视化集成到单个Jupyter Notebook中的工作流，从而形成可访问、可重复且可扩展的分析工作流；（3）一系列应用工作流，展示了在实际应用中的分析和定制能力。具体而言，我们提出了三种工作流，用于研究从生长速率比较到对单个动态细胞响应变化氧气条件的精确分钟级分辨率定量分析的各种微流控LCI实验。我们包含十余个应用工作流的集合是开源的，可在https://github.com/JuBiotech/acia-workflows公开获取。