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

Imaging flow cytometry enables high-throughput acquisition of fluorescence, brightfield and darkfield images of biological cells. Here, Blasi et al.demonstrate that applying machine learning algorithms on brightfield and darkfield images can detect cellular phenotypes without the need for fluorescen...

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Autores principales: Thomas Blasi, Holger Hennig, Huw D. Summers, Fabian J. Theis, Joana Cerveira, James O. Patterson, Derek Davies, Andrew Filby, Anne E. Carpenter, Paul Rees
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2016
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Acceso en línea:https://doaj.org/article/64eccd9285fd45858c93a445b1345a3c
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spelling oai:doaj.org-article:64eccd9285fd45858c93a445b1345a3c2021-12-02T15:33:49ZLabel-free cell cycle analysis for high-throughput imaging flow cytometry10.1038/ncomms102562041-1723https://doaj.org/article/64eccd9285fd45858c93a445b1345a3c2016-01-01T00:00:00Zhttps://doi.org/10.1038/ncomms10256https://doaj.org/toc/2041-1723Imaging flow cytometry enables high-throughput acquisition of fluorescence, brightfield and darkfield images of biological cells. Here, Blasi et al.demonstrate that applying machine learning algorithms on brightfield and darkfield images can detect cellular phenotypes without the need for fluorescent stains, enabling label-free assays.Thomas BlasiHolger HennigHuw D. SummersFabian J. TheisJoana CerveiraJames O. PattersonDerek DaviesAndrew FilbyAnne E. CarpenterPaul ReesNature PortfolioarticleScienceQENNature Communications, Vol 7, Iss 1, Pp 1-9 (2016)
institution DOAJ
collection DOAJ
language EN
topic Science
Q
spellingShingle Science
Q
Thomas Blasi
Holger Hennig
Huw D. Summers
Fabian J. Theis
Joana Cerveira
James O. Patterson
Derek Davies
Andrew Filby
Anne E. Carpenter
Paul Rees
Label-free cell cycle analysis for high-throughput imaging flow cytometry
description Imaging flow cytometry enables high-throughput acquisition of fluorescence, brightfield and darkfield images of biological cells. Here, Blasi et al.demonstrate that applying machine learning algorithms on brightfield and darkfield images can detect cellular phenotypes without the need for fluorescent stains, enabling label-free assays.
format article
author Thomas Blasi
Holger Hennig
Huw D. Summers
Fabian J. Theis
Joana Cerveira
James O. Patterson
Derek Davies
Andrew Filby
Anne E. Carpenter
Paul Rees
author_facet Thomas Blasi
Holger Hennig
Huw D. Summers
Fabian J. Theis
Joana Cerveira
James O. Patterson
Derek Davies
Andrew Filby
Anne E. Carpenter
Paul Rees
author_sort Thomas Blasi
title Label-free cell cycle analysis for high-throughput imaging flow cytometry
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
publisher Nature Portfolio
publishDate 2016
url https://doaj.org/article/64eccd9285fd45858c93a445b1345a3c
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