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Nat Commun DOI:10.1038/ncomms10256

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

Publication TypeJournal Article
Year of Publication2016
AuthorsBlasi, T, Hennig, H, Summers, HD, Theis, FJ, Cerveira, J, Patterson, JO, Davies, D, Filby, A, Carpenter, AE, Rees, P
JournalNat Commun
Date Published2016 Jan 07
KeywordsCell Cycle, DNA, Flow Cytometry, Humans, Image Processing, Computer-Assisted, Jurkat Cells, Machine Learning, Schizosaccharomyces

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.


Alternate JournalNat Commun
PubMed ID26739115
PubMed Central IDPMC4729834
Grant List093917 / / Wellcome Trust / United Kingdom
/ / Medical Research Council / United Kingdom
/ / Cancer Research UK / United Kingdom
BB/N005163/1 / / Biotechnology and Biological Sciences Research Council / United Kingdom
/ / Wellcome Trust / United Kingdom