DynaCLR: Contrastive Learning of Cellular Dynamics with Temporal Regularization
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909667634970624 |
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| author | Hirata-Miyasaki, Eduardo Pradeep, Soorya Liu, Ziwen Imran, Alishba Theodoro, Taylla Milena Ivanov, Ivan E. Khadka, Sudip Lee, See-Chi Grunberg, Michelle Woosley, Hunter Bhave, Madhura Arias, Carolina Mehta, Shalin B. |
| author_facet | Hirata-Miyasaki, Eduardo Pradeep, Soorya Liu, Ziwen Imran, Alishba Theodoro, Taylla Milena Ivanov, Ivan E. Khadka, Sudip Lee, See-Chi Grunberg, Michelle Woosley, Hunter Bhave, Madhura Arias, Carolina Mehta, Shalin B. |
| contents | We report DynaCLR, a self-supervised method for embedding cell and organelle Dynamics via Contrastive Learning of Representations of time-lapse images. DynaCLR integrates single-cell tracking and time-aware contrastive sampling to learn robust, temporally regularized representations of cell dynamics. DynaCLR embeddings generalize effectively to in-distribution and out-of-distribution datasets, and can be used for several downstream tasks with sparse human annotations. We demonstrate efficient annotations of cell states with a human-in-the-loop using fluorescence and label-free imaging channels. DynaCLR method enables diverse downstream biological analyses: classification of cell division and infection, clustering heterogeneous cell migration patterns, cross-modal distillation of cell states from fluorescence to label-free channel, alignment of asynchronous cellular responses and broken cell tracks, and discovering organelle response due to infection. DynaCLR is a flexible method for comparative analyses of dynamic cellular responses to pharmacological, microbial, and genetic perturbations. We provide PyTorch-based implementations of the model training and inference pipeline (https://github.com/mehta-lab/viscy) and a GUI (https://github.com/czbiohub-sf/napari-iohub) for the visualization and annotation of trajectories of cells in the real space and the embedding space. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11281 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DynaCLR: Contrastive Learning of Cellular Dynamics with Temporal Regularization Hirata-Miyasaki, Eduardo Pradeep, Soorya Liu, Ziwen Imran, Alishba Theodoro, Taylla Milena Ivanov, Ivan E. Khadka, Sudip Lee, See-Chi Grunberg, Michelle Woosley, Hunter Bhave, Madhura Arias, Carolina Mehta, Shalin B. Computer Vision and Pattern Recognition Quantitative Methods I.2.6; J.3 We report DynaCLR, a self-supervised method for embedding cell and organelle Dynamics via Contrastive Learning of Representations of time-lapse images. DynaCLR integrates single-cell tracking and time-aware contrastive sampling to learn robust, temporally regularized representations of cell dynamics. DynaCLR embeddings generalize effectively to in-distribution and out-of-distribution datasets, and can be used for several downstream tasks with sparse human annotations. We demonstrate efficient annotations of cell states with a human-in-the-loop using fluorescence and label-free imaging channels. DynaCLR method enables diverse downstream biological analyses: classification of cell division and infection, clustering heterogeneous cell migration patterns, cross-modal distillation of cell states from fluorescence to label-free channel, alignment of asynchronous cellular responses and broken cell tracks, and discovering organelle response due to infection. DynaCLR is a flexible method for comparative analyses of dynamic cellular responses to pharmacological, microbial, and genetic perturbations. We provide PyTorch-based implementations of the model training and inference pipeline (https://github.com/mehta-lab/viscy) and a GUI (https://github.com/czbiohub-sf/napari-iohub) for the visualization and annotation of trajectories of cells in the real space and the embedding space. |
| title | DynaCLR: Contrastive Learning of Cellular Dynamics with Temporal Regularization |
| topic | Computer Vision and Pattern Recognition Quantitative Methods I.2.6; J.3 |
| url | https://arxiv.org/abs/2410.11281 |