Grateful to our incredible collaborators! This work wouldn’t have been possible without the deep interdisciplinary collaboration across CZ Biohub teams.
These fast iterations and bold ideas are only possible because of you! 🙏 #CZBiohub @czbiohub.bsky.social

#science #CellBiology #AI #microscopy

We provide a:
- PyTorch code for training & inference: github.com/mehta-lab/Vi...
- A GUI to visualize trajectories in real and embedding space
- Tools for efficient human-in-the-loop annotation

DynaCLR’s temporally consistent embeddings enable in silico synchronization of asynchronous dynamics like infection & mitosis, along with organelle responses, enabling pseudotime estimation across multiple imaging channels. 5/⏳

We introduce a strategy to accelerate label-free cell state analysis: cross-modal knowledge distillation transfers labels from easy-to-annotate fluorescence sensors to unlabeled phase images, unlocking multi-state measurements from label-free data. 4/🧑‍🏫

DynaCLR embeddings enable efficient classification of cell states, including mitosis and viral infection, with a few thousand point annotations of cell state, which speeds up measurement of complex cell state dynamics. 3/🦠

DynaCLR learns embeddings from 3D multi-channel movies and enables:
1) cell state classification
2) fluorescence-to-label-free label transfer
3) alignment of dynamics.

This update adds knowledge distillation and temporal alignment for even richer analysis. 2/🔬