Mapping cell state dynamics with AI-enabled live cell imaging
Dynamic imaging of cells and organelles is a promising approach to understanding causal relationships between perturbations and responses. Annotating and analyzing dynamic cell states in movies is prohibitively expensive. We report a time-aware dimensionality reduction method that leverages temporal relationships between cell shapes observed in high-resolution live cell imaging. Our approach allows the measurement of cell state dynamics in a low-dimensional embedding space and real space. We utilize this method to analyze the responses of cells and organelles to viral infection.
Authors
- Ziwen Liu (ORCID: https://orcid.org/0000-0001-7482-1299)
- Shalin B. Mehta (ORCID: https://orcid.org/0000-0002-2542-3582)
- Eduardo Hirata-Miyasaki (ORCID: https://orcid.org/0000-0002-1016-2447)
- Carolina Arias (ORCID: https://orcid.org/0000-0002-4445-0826)
- Sudip Khadka (ORCID: https://orcid.org/0000-0002-5551-0417)
- Ivan E. Ivanov (ORCID: https://orcid.org/0000-0001-6389-0213)
- Madhura Bhave (ORCID: https://orcid.org/0000-0001-7446-9256)
- Soorya Pradeep (ORCID: https://orcid.org/0000-0002-0926-1480)
- Hunter Woosley (ORCID: https://orcid.org/0009-0002-8097-8561)
- Alishba Imran
- Taylla Milena Theodoro
Institutions
- Chan Zuckerberg Initiative (United States) (US)
- University of California, Berkeley (US)
Publication Details
- Published
- 2025-03-19
- DOI
- https://doi.org/10.1117/12.3051857
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00