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

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.

SUBMITTER: Blasi T

PROVIDER: S-EPMC4729834 | biostudies-other | 2016 Jan

REPOSITORIES: biostudies-other

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

Blasi Thomas T Hennig Holger H Summers Huw D HD Theis Fabian J FJ Cerveira Joana J Patterson James O JO Davies Derek D Davies Derek D Filby Andrew A Carpenter Anne E AE Rees Paul P

Nature communications 20160107

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 ...[more]

PMID: 26739115

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Project description:Exposure to respirable fractions of crystalline silica quartz dust particles is associated with silicosis, cancer and the development of autoimmune conditions. Early cellular interactions are not well understood, partly due to a lack of suitable technological methods. Improved techniques are needed to better quantify and study high-level respirable crystalline silica exposure in human populations. Techniques that can be applied to complex biological matrices are pivotal to understanding particle-cell interactions and the impact of particles within real, biologically complex environments. In this study, we investigated whether imaging flow cytometry could be used to assess the interactions between cells and crystalline silica when present within complex biological matrices. Using the respirable-size fine quartz crystalline silica dust Min-u-sil® 5, we first validated previous reports that, whilst associating with cells, crystalline silica particles can be detected solely through their differential light scattering profile using conventional flow cytometry. This same property reliably identified crystalline silica in association with primary monocytic cells in vitro using an imaging flow cytometry assay, where darkfield intensity measurements were able to detect crystalline silica concentrations as low as 2.5 ?g/mL. Finally, we ultilised fresh whole blood as an exemplary complex biological matrix to test the technique. Even after the increased sample processing required to analyse cells within whole blood, imaging flow cytometry was capable of detecting and assessing silica-association to cells. As expected, in fresh whole blood exposed to crystalline silica, neutrophils and cells of the monocyte/macrophage lineage phagocytosed the particles. In addition to the use of this technique in in vitro exposure models, this method has the potential to be applied directly to ex vivo diagnostic studies and research models, where the identification of crystalline silica association with cells in complex biological matrices such as bronchial lavage fluids, alongside additional functional and phenotypic cellular readouts, is required.

Dataset Information

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

Publications

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

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