1
|
Nguyen P, Chien S, Dai J, Monnat RJ, Becker PS, Kueh HY. Unsupervised discovery of dynamic cell phenotypic states from transmitted light movies. PLoS Comput Biol 2021; 17:e1009626. [PMID: 34968384 PMCID: PMC8754342 DOI: 10.1371/journal.pcbi.1009626] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 01/12/2022] [Accepted: 11/09/2021] [Indexed: 11/26/2022] Open
Abstract
Identification of cell phenotypic states within heterogeneous populations, along with elucidation of their switching dynamics, is a central challenge in modern biology. Conventional single-cell analysis methods typically provide only indirect, static phenotypic readouts. Transmitted light images, on the other hand, provide direct morphological readouts and can be acquired over time to provide a rich data source for dynamic cell phenotypic state identification. Here, we describe an end-to-end deep learning platform, UPSIDE (Unsupervised Phenotypic State IDEntification), for discovering cell states and their dynamics from transmitted light movies. UPSIDE uses the variational auto-encoder architecture to learn latent cell representations, which are then clustered for state identification, decoded for feature interpretation, and linked across movie frames for transition rate inference. Using UPSIDE, we identified distinct blood cell types in a heterogeneous dataset. We then analyzed movies of patient-derived acute myeloid leukemia cells, from which we identified stem-cell associated morphological states as well as the transition rates to and from these states. UPSIDE opens up the use of transmitted light movies for systematic exploration of cell state heterogeneity and dynamics in biology and medicine.
Collapse
Affiliation(s)
- Phuc Nguyen
- Department of Bioengineering, University of Washington, Seattle, Washington, United States of America
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, Washington, United States of America
| | - Sylvia Chien
- Division of Hematology, University of Washington, Seattle, Washington, United States of America
| | - Jin Dai
- Division of Hematology, University of Washington, Seattle, Washington, United States of America
| | - Raymond J. Monnat
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, Washington, United States of America
| | - Pamela S. Becker
- Division of Hematology, University of Washington, Seattle, Washington, United States of America
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- Division of Hematology/Oncology, Department of Medicine, University of California, Irvine, California, United States of America
- Chao Family Comprehensive Cancer Center Cancer Research Institute, University of California, Irvine, California, United States of America
| | - Hao Yuan Kueh
- Department of Bioengineering, University of Washington, Seattle, Washington, United States of America
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, Washington, United States of America
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, Washington, United States of America
| |
Collapse
|