Live-cell imaging and analysis reveal cell phenotypic transition dynamics inherently missing in snapshot data.

Autor:	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.; UPMC-Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA.; Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA 15232, USA.
Jazyk:	angličtina
Zdroj:	Science advances [Sci Adv] 2020 Sep 04; Vol. 6 (36). Date of Electronic Publication: 2020 Sep 04 (Print Publication: 2020).
DOI:	10.1126/sciadv.aba9319
Abstrakt:	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. (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu