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Blasi, T. ; Hennig, H.* ; Summers, H.D.* ; Theis, F.J. ; Cerveira, J.* ; Patterson, J.O.* ; Davies, D.* ; Filby, A.* ; Carpenter, A.E.* ; Rees, P.*

Label-free cell cycle analysis for high-throughput imaging flow cytometry.

Nat. Commun. 7:10256 (2016)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Imaging flow cytometry combines the high-throughput capabilities of conventional flow cytometry with single-cell imaging. Here we demonstrate label-free prediction of DNA content and quantification of the mitotic cell cycle phases by applying supervised machine learning to morphological features extracted from brightfield and the typically ignored darkfield images of cells from an imaging flow cytometer. This method facilitates non-destructive monitoring of cells avoiding potentially confounding effects of fluorescent stains while maximizing available fluorescence channels. The method is effective in cell cycle analysis for mammalian cells, both fixed and live, and accurately assesses the impact of a cell cycle mitotic phase blocking agent. As the same method is effective in predicting the DNA content of fission yeast, it is likely to have a broad application to other cell types.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Fixed Cells; Software; Microscopy; Division; Dynamics; Feedback; Mitosis; Growth
Language english
Publication Year 2016
HGF-reported in Year 2016
ISSN (print) / ISBN 2041-1723
e-ISSN 2041-1723
Journal Nature Communications
Quellenangaben Volume: 7, Issue: , Pages: , Article Number: 10256 Supplement: ,
Publisher Nature Publishing Group
Publishing Place London
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-503800-001
DOI 10.1038/ncomms10256
WOS ID WOS:000369020300006
Scopus ID 84953896921
PubMed ID 26739115
Erfassungsdatum 2016-01-12
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