Institute of Biochemistry and Molecular Cell Biology, Medical School, RWTH Aachen University, Aachen, Germany.
Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany.
PLoS One. 2022 Jul 7;17(7):e0270923. doi: 10.1371/journal.pone.0270923. eCollection 2022.
Live-cell imaging has become state of the art to accurately identify the nature of mitotic and cell cycle defects. Low- and high-throughput microscopy setups have yield huge data amounts of cells recorded in different experimental and pathological conditions. Tailored semi-automated and automated image analysis approaches allow the analysis of high-content screening data sets, saving time and avoiding bias. However, they were mostly designed for very specific experimental setups, which restricts their flexibility and usability. The general need for dedicated experiment-specific user-annotated training sets and experiment-specific user-defined segmentation parameters remains a major bottleneck for fully automating the analysis process. In this work we present LiveCellMiner, a highly flexible open-source software tool to automatically extract, analyze and visualize both aggregated and time-resolved image features with potential biological relevance. The software tool allows analysis across high-content data sets obtained in different platforms, in a quantitative and unbiased manner. As proof of principle application, we analyze here the dynamic chromatin and tubulin cytoskeleton features in human cells passing through mitosis highlighting the versatile and flexible potential of this tool set.
活细胞成像已成为一种先进的技术，可准确识别有丝分裂和细胞周期缺陷的性质。低通量和高通量显微镜设置产生了大量的细胞数据，这些数据是在不同的实验和病理条件下记录的。经过精心设计的半自动和自动化图像分析方法可用于分析高通量筛选数据集，从而节省时间并避免偏差。但是，它们大多是为非常特定的实验设置而设计的，这限制了它们的灵活性和可用性。专用的实验特定用户注释训练集和实验特定用户定义的分割参数的普遍需求仍然是完全自动化分析过程的主要瓶颈。在这项工作中，我们提出了 LiveCellMiner，这是一种高度灵活的开源软件工具，可用于自动提取、分析和可视化具有潜在生物学相关性的聚合和时变图像特征。该软件工具允许以定量且无偏倚的方式分析在不同平台上获得的高内涵数据集。作为原理验证应用，我们在这里分析了人类细胞通过有丝分裂时动态染色质和微管细胞骨架特征，突出了该工具集的多功能和灵活性潜力。
