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

Thomas Blasi, Holger Hennig, Huw D. Summers, Fabian J. Theis, Joana Cerveira, James O. Patterson, Derek Davies, Andrew Filby, Anne E. Carpenter () and Paul Rees ()
Additional contact information
Thomas Blasi: Imaging Platform at the Broad Institute of Harvard and MIT
Holger Hennig: Imaging Platform at the Broad Institute of Harvard and MIT
Huw D. Summers: College of Engineering, Swansea University
Fabian J. Theis: Helmholtz Zentrum München—German Research Center for Environmental Health, Institute of Computational Biology
Joana Cerveira: Flow Cytometry Facility, The Francis Crick Institute, Lincoln's Inn Fields Laboratory
James O. Patterson: Cell Cycle Laboratory, The Francis Crick Institute
Derek Davies: Flow Cytometry Facility, The Francis Crick Institute, Lincoln's Inn Fields Laboratory
Andrew Filby: Newcastle Upon Tyne University, Faculty of Medical Sciences, Bioscience Centre, International Centre for life
Anne E. Carpenter: Imaging Platform at the Broad Institute of Harvard and MIT
Paul Rees: Imaging Platform at the Broad Institute of Harvard and MIT

Nature Communications, 2016, vol. 7, issue 1, 1-9

Abstract: 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.

Date: 2016
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.nature.com/articles/ncomms10256 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms10256

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/ncomms10256

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().