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bioRxiv · 10.1101/2021.09.19.460933

pcnaDeep: A Fast and Robust Single-Cell Tracking Method Using Deep-Learning Mediated Cell Cycle Profiling

Gui, Y.·Xie, S. S.·Wang, Y.·Wang, P.·Yao, R.·Gao, X.·Dong, Y.·Wang, G.·Chan, K. Y.
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Abstract

MotivationComputational methods that track single-cells and quantify fluorescent biosensors in time-lapse microscopy images have revolutionised 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. ResultsHere, 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 PCNA 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. Availability and ImplementationpcnaDeep 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 InformationSupplementary data are available online.

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Gui, Y., Xie, S. S., Wang, Y., Wang, P., Yao, R., Gao, X., Dong, Y., Wang, G., Chan, K. Y.. 2021-09-20. pcnaDeep: A Fast and Robust Single-Cell Tracking Method Using Deep-Learning Mediated Cell Cycle Profiling. https://doi.org/10.1101/2021.09.19.460933

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