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

Thomas Blasi, Holger Hennig, Huw Summers Orcid Logo, Fabian J. Theis, Joana Cerveira, James O. Patterson, Derek Davies, Andrew Filby, Anne E. Carpenter, Paul Rees Orcid Logo

Nature Communications, Volume: 7

Swansea University Authors: Huw Summers Orcid Logo, Paul Rees Orcid Logo

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DOI (Published version): 10.1038/ncomms10256

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 ext...

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Published in: Nature Communications
ISSN: 2041-1723 2041-1723
Published: 2016
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URI: https://cronfa.swan.ac.uk/Record/cronfa26064
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last_indexed 2020-12-18T03:41:11Z
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title Label-free cell cycle analysis for high-throughput imaging flow cytometry
spellingShingle Label-free cell cycle analysis for high-throughput imaging flow cytometry
Huw Summers
Paul Rees
title_short Label-free cell cycle analysis for high-throughput imaging flow cytometry
title_full Label-free cell cycle analysis for high-throughput imaging flow cytometry
title_fullStr Label-free cell cycle analysis for high-throughput imaging flow cytometry
title_full_unstemmed Label-free cell cycle analysis for high-throughput imaging flow cytometry
title_sort Label-free cell cycle analysis for high-throughput imaging flow cytometry
author_id_str_mv a61c15e220837ebfa52648c143769427
537a2fe031a796a3bde99679ee8c24f5
author_id_fullname_str_mv a61c15e220837ebfa52648c143769427_***_Huw Summers
537a2fe031a796a3bde99679ee8c24f5_***_Paul Rees
author Huw Summers
Paul Rees
author2 Thomas Blasi
Holger Hennig
Huw Summers
Fabian J. Theis
Joana Cerveira
James O. Patterson
Derek Davies
Andrew Filby
Anne E. Carpenter
Paul Rees
format Journal article
container_title Nature Communications
container_volume 7
publishDate 2016
institution Swansea University
issn 2041-1723
2041-1723
doi_str_mv 10.1038/ncomms10256
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Biomedical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Biomedical Engineering
document_store_str 1
active_str 0
description 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.
published_date 2016-01-07T03:55:42Z
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