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Nat Commun. 2016 Jan 7;7:10256. doi: 10.1038/ncomms10256.

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

Author information

1
Imaging Platform at the Broad Institute of Harvard and MIT, 415 Main St, Cambridge, Massachusetts 02142, USA.
2
Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany.
3
Department of Mathematics, Technische Universität München, Boltzmannstraße 3, 85748 Garching, Germany.
4
College of Engineering, Swansea University, Singleton Park, Swansea SA2 8PP, UK.
5
Flow Cytometry Facility, The Francis Crick Institute, Lincoln's Inn Fields Laboratory, 44 Lincoln's Inn Fields, London WC2A 3LY, UK.
6
Cell Cycle Laboratory, The Francis Crick Institute, 44 Lincoln's Inn Fields, Holborn WC2A 3LY, UK.
7
Newcastle Upon Tyne University, Faculty of Medical Sciences, Bioscience Centre, International Centre for life, Newcastle Upon Tyne NE1 7RU, UK.

Abstract

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.

PMID:
26739115
PMCID:
PMC4729834
DOI:
10.1038/ncomms10256
[Indexed for MEDLINE]
Free PMC Article

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