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实现对活细胞时空特性的精细表征，这对于从生物医学应用到生物加工等生命科学领域的研究进展至关重要。具备数十至数百个并行细胞培养单元的高通量实验装置为获得稳健且可重复的研究发现提供了潜力。然而，这些发现被每次实验所记录的海量LCI数据所掩盖。目前，用于细胞分割与追踪的先进深度学习方法的最新进展，使得对此类大规模数据的自动化分析成为可能，为系统性地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整合到易于访问、灵活且用户友好的工作流中，以支持其在生物学研究中的常规应用。本工作中，我们提出了acia-workflows平台，该平台整合了三个关键组件：(1) 自动化活细胞成像分析（acia）Python库，其支持模块化设计的图像分析流程，提供了八种深度学习分割与追踪方法；(2) 将图像分析流程、其软件依赖、文档及可视化结果集成于单一Jupyter Notebook中的工作流，从而形成易于访问、可复现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与定制能力。具体而言，我们提出了三个工作流，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至分钟级分辨率定量分析等多种类型的微流控LCI实验。我们包含十余个应用工作流的集合是开源的，并公开发布于 https://github.com/JuBiotech/acia-workflows。