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Label-free cell cycle analysis for high-throughput imaging flow cytometry.
Nat. Commun. 7:10256 (2016)
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
ISSN (print) / ISBN
2041-1723
e-ISSN
2041-1723
Journal
Nature Communications
Quellenangaben
Volume: 7,
Article Number: 10256
Publisher
Nature Publishing Group
Publishing Place
London
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)