Automated cell tracking using StarDist and TrackMate.
Automated cell tracking using StarDist and TrackMate.
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DOI：
10.12688/f1000research.27019.1
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Jacquemet G
中科院分区：
文献类型：
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作者：
Fazeli E;Roy NH;Follain G;Laine RF;von Chamier L;Hänninen PE;Eriksson JE;Tinevez JY;Jacquemet G
细胞的迁移能力是胚胎发育、组织动态平衡、免疫监视和伤口愈合的基本生理过程。因此，在过去的50年里，控制细胞运动的机制一直受到严格的审查。这种检查的主要工具之一是活细胞定量成像，研究人员通过使用记录的图像跟踪细胞，随着时间的推移对细胞进行成像，研究它们的迁移并定量分析它们的动态。尽管有计算工具可用，但由于难以建立强大的自动细胞跟踪和大规模分析，人工跟踪仍然在研究人员中广泛使用。在这里，我们提供了一个详细的分析管道，说明如何将深度学习网络StarDist与流行的跟踪软件Trackmate相结合，以执行2D自动细胞跟踪并提供完全定量的读数。我们提出的协议对荧光图像和广域图像都是兼容的。它只需要免费提供的开源软件(ZeroCostDL4Mic和斐济)，不需要用户的任何编码知识，使其成为该领域的通用和强大的工具。我们通过使用荧光和Brightfield图像自动跟踪癌细胞和T细胞，展示了这条管道的可用性。重要的是，作为补充信息，我们提供了一个详细的循序渐进的方案，以允许研究人员用他们的图像来实施它。
The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance, and wound healing. Therefore, the mechanisms governing cellular locomotion have been under intense scrutiny over the last 50 years. One of the main tools of this scrutiny is live-cell quantitative imaging, where researchers image cells over time to study their migration and quantitatively analyze their dynamics by tracking them using the recorded images. Despite the availability of computational tools, manual tracking remains widely used among researchers due to the difficulty setting up robust automated cell tracking and large-scale analysis. Here we provide a detailed analysis pipeline illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts. Our proposed protocol is compatible with both fluorescent and widefield images. It only requires freely available and open-source software (ZeroCostDL4Mic and Fiji), and does not require any coding knowledge from the users, making it a versatile and powerful tool for the field. We demonstrate this pipeline's usability by automatically tracking cancer cells and T cells using fluorescent and brightfield images. Importantly, we provide, as supplementary information, a detailed step-by-step protocol to allow researchers to implement it with their images.