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

Nat Commun
Authors
Keywords
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.

Year of Publication
2016
Journal
Nat Commun
Volume
7
Pages
10256
Date Published
2016 Jan 07
ISSN
2041-1723
URL
DOI
10.1038/ncomms10256
PubMed ID
26739115
PubMed Central ID
PMC4729834
Links
Grant list
093917 / 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