Unsupervised discovery of dynamic cell phenotypic states from transmitted light movies.

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, dec

SUBMITTER: Nguyen P 

PROVIDER: S-EPMC8754342 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature