Lo Teresa W, Cutler Kevin J, Choi H James, Wiggins Paul A
bioRxiv. 2024 Nov 28:2024.11.25.625259. doi: 10.1101/2024.11.25.625259.
Time-lapse microscopy is a powerful tool for studying the cell biology of bacterial cells. The development of pipelines that facilitate the automated analysis of these datasets is a long-standing goal of the field. In this paper, we describe , an updated version of our pipeline, developed as an open-source, modular, and holistic suite of algorithms whose input is raw microscopy images and whose output is a wide range of quantitative cellular analyses, including dynamical cell cytometry data and cellular visualizations. The updated version described in this paper introduces two principal refinements: (i) robustness to cell morphologies and (ii) support for a range of common imaging modalities. To demonstrate robustness to cell morphology, we present an analysis of the proliferation dynamics of treated with a drug that induces filamentation. To demonstrate extended support for new image modalities, we analyze cells imaged by five distinct modalities: phase-contrast, two brightfield modalities, and cytoplasmic and membrane fluorescence. Together, this pipeline should greatly increase the scope of tractable analyses for bacterial microscopists.
延时显微镜是研究细菌细胞生物学的强大工具。开发便于对这些数据集进行自动分析的流程是该领域长期以来的目标。在本文中,我们描述了[具体名称未给出],这是我们[具体名称未给出]流程的更新版本,它被开发为一套开源、模块化且全面的算法套件,其输入是原始显微镜图像,输出是广泛的定量细胞分析结果,包括动态细胞流式数据和细胞可视化结果。本文描述的更新版本引入了两个主要改进:(i)对细胞形态的鲁棒性,以及(ii)对一系列常见成像模式的支持。为了证明对细胞形态的鲁棒性,我们展示了对用诱导丝状化的药物处理后的[具体名称未给出]增殖动力学的分析。为了证明对新图像模式的扩展支持,我们分析了通过五种不同模式成像的细胞:相差、两种明场模式以及细胞质和膜荧光模式。总之,这个流程应该会大大增加细菌显微镜学家可处理分析的范围。