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
Paul Rees
10779/crick.12601139.v1
https://crick.figshare.com/articles/journal_contribution/Label-free_cell_cycle_analysis_for_high-throughput_imaging_flow_cytometry/12601139
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.
2020-07-15 08:51:19
Cell Cycle
DNA
Flow Cytometry
Humans
Image Processing, Computer-Assisted
Jurkat Cells
Machine Learning
Schizosaccharomyces
Nurse FC001121
FC