Your browser doesn't support javascript.
loading
Unsupervised discovery of dynamic cell phenotypic states from transmitted light movies.
Nguyen, Phuc; Chien, Sylvia; Dai, Jin; Monnat, Raymond J; Becker, Pamela S; Kueh, Hao Yuan.
Afiliação
  • Nguyen P; Department of Bioengineering, University of Washington, Seattle, Washington, United States of America.
  • Chien S; Molecular Engineering and Sciences Institute, University of Washington, Seattle, Washington, United States of America.
  • Dai J; Division of Hematology, University of Washington, Seattle, Washington, United States of America.
  • Monnat RJ; Division of Hematology, University of Washington, Seattle, Washington, United States of America.
  • Becker PS; Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America.
  • Kueh HY; Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America.
PLoS Comput Biol ; 17(12): e1009626, 2021 12.
Article em En | MEDLINE | ID: mdl-34968384
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Células Sanguíneas / Imagem com Lapso de Tempo / Análise de Célula Única / Aprendizado de Máquina não Supervisionado / Microscopia Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Células Sanguíneas / Imagem com Lapso de Tempo / Análise de Célula Única / Aprendizado de Máquina não Supervisionado / Microscopia Idioma: En Ano de publicação: 2021 Tipo de documento: Article