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Live-cell imaging and analysis reveal cell phenotypic transition dynamics inherently missing in snapshot data.
Wang, Weikang; Douglas, Diana; Zhang, Jingyu; Kumari, Sangeeta; Enuameh, Metewo Selase; Dai, Yan; Wallace, Callen T; Watkins, Simon C; Shu, Weiguo; Xing, Jianhua.
Afiliação
  • Wang W; Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15232, USA.
  • Douglas D; ATCC Cell Systems, Gaithersburg, MD 20877, USA.
  • Zhang J; Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15232, USA.
  • Kumari S; ATCC Cell Systems, Gaithersburg, MD 20877, USA.
  • Enuameh MS; ATCC Cell Systems, Gaithersburg, MD 20877, USA.
  • Dai Y; Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15232, USA.
  • Wallace CT; Department of Cell Biology, University of Pittsburgh, Pittsburgh, PA 15232, USA.
  • Watkins SC; Department of Cell Biology, University of Pittsburgh, Pittsburgh, PA 15232, USA.
  • Shu W; ATCC Cell Systems, Gaithersburg, MD 20877, USA.
  • Xing J; Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15232, USA. xing1@pitt.edu.
Sci Adv ; 6(36)2020 09.
Article em En | MEDLINE | ID: mdl-32917609
Recent advances in single-cell techniques catalyze an emerging field of studying how cells convert from one phenotype to another, in a step-by-step process. Two grand technical challenges, however, impede further development of the field. Fixed cell-based approaches can provide snapshots of high-dimensional expression profiles but have fundamental limits on revealing temporal information, and fluorescence-based live-cell imaging approaches provide temporal information but are technically challenging for multiplex long-term imaging. We first developed a live-cell imaging platform that tracks cellular status change through combining endogenous fluorescent labeling that minimizes perturbation to cell physiology and/or live-cell imaging of high-dimensional cell morphological and texture features. With our platform and an A549 VIM-RFP epithelial-to-mesenchymal transition (EMT) reporter cell line, live-cell trajectories reveal parallel paths of EMT missing from snapshot data due to cell-cell dynamic heterogeneity. Our results emphasize the necessity of extracting dynamical information of phenotypic transitions from multiplex live-cell imaging.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sci Adv Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sci Adv Ano de publicação: 2020 Tipo de documento: Article