DynaCLR: Contrastive Learning of Cellular Dynamics with Temporal Regularization

• URL: http://arxiv.org/abs/2410.11281v2
• Date: Tue, 01 Jul 2025 01:20:03 GMT
• Title: DynaCLR: Contrastive Learning of Cellular Dynamics with Temporal Regularization
• Authors: Eduardo Hirata-Miyasaki, Soorya Pradeep, Ziwen Liu, Alishba Imran, Taylla Milena Theodoro, Ivan E. Ivanov, Sudip Khadka, See-Chi Lee, Michelle Grunberg, Hunter Woosley, Madhura Bhave, Carolina Arias, Shalin B. Mehta,
• Abstract summary: DynaCLR is a self-supervised method for embedding cell and organelle Dynamics via Contrastive Learning of Representations of time-lapse images.<n>We integrate single-cell tracking and time-aware contrastive sampling to learn robust, temporally regularized representations of cell dynamics.
• Score: 0.5385157903509136
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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