Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA.
Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA.
Biol Open. 2024 Sep 15;13(9). doi: 10.1242/bio.060555. Epub 2024 Sep 19.
Time-lapse microscopy has emerged as a crucial tool in cell biology, facilitating a deeper understanding of dynamic cellular processes. While existing tracking tools have proven effective in detecting and monitoring objects over time, the quantification of signals within these tracked objects often faces implementation constraints. In the context of infectious diseases, the quantification of signals at localized compartments within the cell and around intracellular pathogens can provide even deeper insight into the interactions between the pathogen and host cell organelles. Existing quantitative analysis at a single-phagosome level remains limited and dependent on manual tracking methods. We developed a near-fully automated workflow that performs with limited bias, high-throughput cell segmentation and quantitative tracking of both single cell and single bacterium/phagosome within multi-channel, z-stack, time-lapse confocal microscopy videos. We took advantage of the PyImageJ library to bring Fiji functionality into a Python environment and combined deep-learning-based segmentation from Cellpose with tracking algorithms from Trackmate. The 'da_tracker' workflow provides a versatile toolkit of functions for measuring relevant signal parameters at the single-cell level (such as velocity or bacterial burden) and at the single-phagosome level (i.e. assessment of phagosome maturation over time). Its capabilities in both single-cell and single-phagosome quantification, its flexibility and open-source nature should assist studies that aim to decipher for example the pathogenicity of bacteria and the mechanism of virulence factors that could pave the way for the development of innovative therapeutic approaches.
延时显微镜已成为细胞生物学中的重要工具，有助于更深入地了解动态细胞过程。虽然现有的跟踪工具在检测和监测随时间变化的物体方面已经被证明是有效的，但在这些跟踪物体中对信号的定量分析往往面临实施限制。在传染病的背景下，对细胞内局部隔间和细胞内病原体周围信号的定量分析可以更深入地了解病原体与宿主细胞细胞器之间的相互作用。现有的单吞噬体水平的定量分析仍然受到限制，并且依赖于手动跟踪方法。我们开发了一种近乎完全自动化的工作流程，该流程具有较低的偏差、高通量的细胞分割以及对多通道、z 堆叠、延时共聚焦显微镜视频中单细胞和单个细菌/吞噬体的定量跟踪。我们利用 PyImageJ 库将 Fiji 的功能引入 Python 环境，并将基于深度学习的 Cellpose 分割与 Trackmate 的跟踪算法相结合。“da_tracker”工作流程提供了一个功能齐全的工具包，用于测量单细胞水平（例如速度或细菌负荷）和单个吞噬体水平（即评估吞噬体随时间的成熟度）的相关信号参数。它在单细胞和单个吞噬体定量方面的功能、灵活性和开源性质应该有助于研究例如细菌的致病性和毒力因子的作用机制，为开发创新的治疗方法铺平道路。
