Hierarchical deep reinforcement learning reveals a modular mechanism of cell movement Journal Article
| Authors: | Wang, Z.; Xu, Y.; Wang, D.; Yang, J.; Bao, Z. |
| Article Title: | Hierarchical deep reinforcement learning reveals a modular mechanism of cell movement |
| Abstract: | Time-lapse images of cells and tissues contain rich information about dynamic cell behaviours, which reflect the underlying processes of proliferation, differentiation and morphogenesis. However, we lack computational tools for effective inference. Here we exploit deep reinforcement learning (DRL) to infer cell–cell interactions and collective cell behaviours in tissue morphogenesis from three-dimensional (3D) time-lapse images. We use hierarchical DRL (HDRL), known for multiscale learning and data efficiency, to examine cell migrations based on images with a ubiquitous nuclear label and simple rules formulated from empirical statistics of the images. When applied to Caenorhabditis elegans embryogenesis, HDRL reveals a multiphase, modular organization of cell movement. Imaging with additional cellular markers confirms the modular organization as a novel migration mechanism, which we term sequential rosettes. Furthermore, HDRL forms a transferable model that successfully differentiates sequential rosettes-based migration from others. Our study demonstrates a powerful approach to infer the underlying biology from time-lapse imaging without prior knowledge. © 2022, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply. |
| Keywords: | cytology; cell migration; cell movement; caenorhabditis elegans; tissue; cells; computational tools; cell-cell interaction; deep learning; modulars; reinforcement learning; cell behaviours; modular organizations; simple++; time lapse images |
| Journal Title: | Nature Machine Intelligence |
| Volume: | 4 |
| Issue: | 1 |
| ISSN: | 2522-5839 |
| Publisher: | Nature Publishing Group |
| Date Published: | 2022-01-01 |
| Start Page: | 73 |
| End Page: | 83 |
| Language: | English |
| DOI: | 10.1038/s42256-021-00431-x |
| PROVIDER: | scopus |
| PMCID: | PMC9387359 |
| PUBMED: | 35991585 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 1 March 2022 -- Source: Scopus |
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