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

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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Bibliographic Details
Main Authors: Thomas Blasi, Holger Hennig, Huw D. Summers, Fabian J. Theis, Joana Cerveira, James O. Patterson, Derek Davies, Andrew Filby, Anne E. Carpenter, Paul Rees
Format: Article
Language:English
Published: Nature Publishing Group 2016-01-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/ncomms10256
Description
Summary: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.
ISSN:2041-1723

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