Dataset Information

Hierarchical deep reinforcement learning reveals novel mechanism of cell movement.

ABSTRACT: Time-lapse images of cells and tissues contain rich information of dynamic cell behaviors, 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 behaviors in tissue morphogenesis from 3D, time-lapse images. We used Hierarchical DRL (HDRL), known for multiscale learning and data efficiency, to examine cell migrations based on images with ubiquitous nuclear label and simple rules formulated from empirical statistics of the images. When applied to C. elegans embryogenesis, HDRL reveals a multi-phase, 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.

SUBMITTER: Wang Z

PROVIDER: S-EPMC9387359 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Publications

Hierarchical deep reinforcement learning reveals novel mechanism of cell movement.

Wang Zi Z Xu Yichi Y Wang Dali D Yang Jiawei J Bao Zhirong Z

Nature machine intelligence 20220110 1

Time-lapse images of cells and tissues contain rich information of dynamic cell behaviors, 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 behaviors in tissue morphogenesis from 3D, time-lapse images. We used Hierarchical DRL (HDRL), known for multiscale learning and data efficiency, to exa ...[more]

PMID: 35991585

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Project description:Motivation:Cell movement in the early phase of Caenorhabditis elegans development is regulated by a highly complex process in which a set of rules and connections are formulated at distinct scales. Previous efforts have demonstrated that agent-based, multi-scale modeling systems can integrate physical and biological rules and provide new avenues to study developmental systems. However, the application of these systems to model cell movement is still challenging and requires a comprehensive understanding of regulatory networks at the right scales. Recent developments in deep learning and reinforcement learning provide an unprecedented opportunity to explore cell movement using 3D time-lapse microscopy images. Results:We present a deep reinforcement learning approach within an agent-based modeling system to characterize cell movement in the embryonic development of C.elegans. Our modeling system captures the complexity of cell movement patterns in the embryo and overcomes the local optimization problem encountered by traditional rule-based, agent-based modeling that uses greedy algorithms. We tested our model with two real developmental processes: the anterior movement of the Cpaaa cell via intercalation and the rearrangement of the superficial left-right asymmetry. In the first case, the model results suggested that Cpaaa's intercalation is an active directional cell movement caused by the continuous effects from a longer distance (farther than the length of two adjacent cells), as opposed to a passive movement caused by neighbor cell movements. In the second case, a leader-follower mechanism well explained the collective cell movement pattern in the asymmetry rearrangement. These results showed that our approach to introduce deep reinforcement learning into agent-based modeling can test regulatory mechanisms by exploring cell migration paths in a reverse engineering perspective. This model opens new doors to explore the large datasets generated by live imaging. Availability and implementation:Source code is available at https://github.com/zwang84/drl4cellmovement. Supplementary information:Supplementary data are available at Bioinformatics online.

Dataset Information

Hierarchical deep reinforcement learning reveals novel mechanism of cell movement.

Publications

Hierarchical deep reinforcement learning reveals novel mechanism of cell movement.

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