da_Tracker: Automated workflow for high throughput single cell and single phagosome tracking in infected cells.
da_Tracker: Automated workflow for high throughput single cell and single phagosome tracking in infected cells.
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da_Tracker：受感染细胞中高通量单细胞和单吞噬体跟踪的自动化工作流程。
DOI：
10.1101/2024.04.10.588863
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发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Briken,Volker
中科院分区：
文献类型：
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作者：
Augenstreich,Jacques;Poddar,Anushka;Belew,AshtonT;El-Sayed,NajibM;Briken,Volker
时间推移显微镜已经成为细胞生物学中的一个重要工具，有助于更深入地了解细胞的动态过程。虽然随着时间的推移，现有的跟踪工具已被证明在检测和监测物体方面是有效的，但这些被追踪物体内的信号的量化往往面临实施限制。在传染病的背景下，对细胞内和细胞内病原体周围局部隔间的信号进行量化可以更深入地了解病原体和宿主细胞细胞器之间的相互作用。现有的单噬菌体水平的定量分析仍然有限，并且依赖于人工追踪方法。我们开发了一种近乎全自动化的工作流程，在多通道、z堆叠、延时共焦显微镜视频中执行有限偏差、高通量细胞分割和对单个细胞和单个细菌/吞噬小体的定量跟踪。我们利用了PyImageJ库将斐济的功能引入到一个Python环境中，并将来自Cellpose的基于深度学习的分割与来自Trackmate的跟踪算法相结合。‘da_tracker’工作流程提供了一个多功能工具包，用于在单细胞水平(如速度或细菌负荷)和在单噬菌体水平(即，对吞噬体随时间成熟的评估)测量相关信号参数。它在单细胞和单噬菌体量化方面的能力、其灵活性和开源性质应有助于旨在破译例如细菌的致病性和毒力因素的机制的研究，这些可能为开发创新的治疗方法铺平道路。
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 (ie 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.