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

Author

Listed:
  • Thomas Blasi

    (Imaging Platform at the Broad Institute of Harvard and MIT
    Helmholtz Zentrum München—German Research Center for Environmental Health, Institute of Computational Biology
    Technische Universität München)

  • Holger Hennig

    (Imaging Platform at the Broad Institute of Harvard and MIT)

  • Huw D. Summers

    (College of Engineering, Swansea University)

  • Fabian J. Theis

    (Helmholtz Zentrum München—German Research Center for Environmental Health, Institute of Computational Biology
    Technische Universität München)

  • Joana Cerveira

    (Flow Cytometry Facility, The Francis Crick Institute, Lincoln's Inn Fields Laboratory)

  • James O. Patterson

    (Cell Cycle Laboratory, The Francis Crick Institute)

  • Derek Davies

    (Flow Cytometry Facility, The Francis Crick Institute, Lincoln's Inn Fields Laboratory)

  • Andrew Filby

    (Newcastle Upon Tyne University, Faculty of Medical Sciences, Bioscience Centre, International Centre for life)

  • Anne E. Carpenter

    (Imaging Platform at the Broad Institute of Harvard and MIT)

  • Paul Rees

    (Imaging Platform at the Broad Institute of Harvard and MIT
    College of Engineering, Swansea University)

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.

Suggested Citation

  • Thomas Blasi & Holger Hennig & Huw D. Summers & Fabian J. Theis & Joana Cerveira & James O. Patterson & Derek Davies & Andrew Filby & Anne E. Carpenter & Paul Rees, 2016. "Label-free cell cycle analysis for high-throughput imaging flow cytometry," Nature Communications, Nature, vol. 7(1), pages 1-9, April.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms10256
    DOI: 10.1038/ncomms10256
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    Cited by:

    1. Dennis Pischel & Jörn H Buchbinder & Kai Sundmacher & Inna N Lavrik & Robert J Flassig, 2018. "A guide to automated apoptosis detection: How to make sense of imaging flow cytometry data," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-17, May.

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