Department of Breast Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310029, P. R. China.
Zhejiang University-University of Edinburgh Institute (ZJE), Zhejiang University School of Medicine, Zhejiang University, Haining 314400, P. R. China.
Bioinformatics. 2022 Oct 14;38(20):4846-4847. doi: 10.1093/bioinformatics/btac602.
Computational methods that track single cells and quantify fluorescent biosensors in time-lapse microscopy images have revolutionized our approach in studying the molecular control of cellular decisions. One barrier that limits the adoption of single-cell analysis in biomedical research is the lack of efficient methods to robustly track single cells over cell division events. Here, we developed an application that automatically tracks and assigns mother-daughter relationships of single cells. By incorporating cell cycle information from a well-established fluorescent cell cycle reporter, we associate mitosis relationships enabling high fidelity long-term single-cell tracking. This was achieved by integrating a deep-learning-based fluorescent proliferative cell nuclear antigen signal instance segmentation module with a cell tracking and cell cycle resolving pipeline. The application offers a user-friendly interface and extensible APIs for customized cell cycle analysis and manual correction for various imaging configurations.
pcnaDeep is an open-source Python application under the Apache 2.0 licence. The source code, documentation and tutorials are available at https://github.com/chan-labsite/PCNAdeep.
Supplementary data are available at Bioinformatics online.
在延时显微镜图像中追踪单细胞并定量荧光生物传感器的计算方法，彻底改变了我们研究细胞决策的分子控制的方法。限制单细胞分析在生物医学研究中应用的一个障碍是缺乏稳健的方法来在细胞分裂事件中可靠地追踪单细胞。在这里，我们开发了一种自动追踪和分配单细胞母女关系的应用程序。通过整合来自成熟的荧光细胞周期报告基因的细胞周期信息，我们关联有丝分裂关系，从而实现高保真度的长期单细胞追踪。这是通过将基于深度学习的荧光增殖细胞核抗原信号实例分割模块与细胞追踪和细胞周期解析管道集成来实现的。该应用程序提供了用户友好的界面和可扩展的 API，用于各种成像配置的定制细胞周期分析和手动校正。
pcnaDeep 是一个基于 Apache 2.0 许可证的开源 Python 应用程序。源代码、文档和教程可在 https://github.com/chan-labsite/PCNAdeep 上获得。
补充数据可在生物信息学在线获得。
