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

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-dim...

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Autores principales: Wang, Weikang, Douglas, Diana, Zhang, Jingyu, Kumari, Sangeeta, Enuameh, Metewo Selase, Dai, Yan, Wallace, Callen T., Watkins, Simon C., Shu, Weiguo, Xing, Jianhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7473671/
https://www.ncbi.nlm.nih.gov/pubmed/32917609
http://dx.doi.org/10.1126/sciadv.aba9319
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author Wang, Weikang
Douglas, Diana
Zhang, Jingyu
Kumari, Sangeeta
Enuameh, Metewo Selase
Dai, Yan
Wallace, Callen T.
Watkins, Simon C.
Shu, Weiguo
Xing, Jianhua
author_facet Wang, Weikang
Douglas, Diana
Zhang, Jingyu
Kumari, Sangeeta
Enuameh, Metewo Selase
Dai, Yan
Wallace, Callen T.
Watkins, Simon C.
Shu, Weiguo
Xing, Jianhua
author_sort Wang, Weikang
collection PubMed
description 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.
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spelling pubmed-74736712020-09-17 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 Sci Adv Research Articles 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. American Association for the Advancement of Science 2020-09-04 /pmc/articles/PMC7473671/ /pubmed/32917609 http://dx.doi.org/10.1126/sciadv.aba9319 Text en 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). https://creativecommons.org/licenses/by-nc/4.0/ https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
spellingShingle Research Articles
Wang, Weikang
Douglas, Diana
Zhang, Jingyu
Kumari, Sangeeta
Enuameh, Metewo Selase
Dai, Yan
Wallace, Callen T.
Watkins, Simon C.
Shu, Weiguo
Xing, Jianhua
Live-cell imaging and analysis reveal cell phenotypic transition dynamics inherently missing in snapshot data
title Live-cell imaging and analysis reveal cell phenotypic transition dynamics inherently missing in snapshot data
title_full Live-cell imaging and analysis reveal cell phenotypic transition dynamics inherently missing in snapshot data
title_fullStr Live-cell imaging and analysis reveal cell phenotypic transition dynamics inherently missing in snapshot data
title_full_unstemmed Live-cell imaging and analysis reveal cell phenotypic transition dynamics inherently missing in snapshot data
title_short Live-cell imaging and analysis reveal cell phenotypic transition dynamics inherently missing in snapshot data
title_sort live-cell imaging and analysis reveal cell phenotypic transition dynamics inherently missing in snapshot data
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7473671/
https://www.ncbi.nlm.nih.gov/pubmed/32917609
http://dx.doi.org/10.1126/sciadv.aba9319
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