10779/crick.12601139.v1 Thomas Blasi Thomas Blasi Holger Hennig Holger Hennig Huw D Summers Huw D Summers Fabian J Theis Fabian J Theis Joana Cerveira Joana Cerveira James O Patterson James O Patterson Derek Davies Derek Davies Andrew Filby Andrew Filby Anne E Carpenter Anne E Carpenter Paul Rees Paul Rees Label-free cell cycle analysis for high-throughput imaging flow cytometry The Francis Crick Institute 2020 Cell Cycle DNA Flow Cytometry Humans Image Processing, Computer-Assisted Jurkat Cells Machine Learning Schizosaccharomyces Nurse FC001121 FC 2020-07-15 08:51:19 Journal contribution 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.