Selected Publication

11.01.2016

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

Cell cycle phases are assigned to bright and dark field images from imaging flow cytometry by using machine learning algorithms (source: HMGU)

Imaging flow cytometry enables the acquisition of both fluorescence and brightfield images of biological cells in a high-throughput manner. An international team, including scientists from the Institute of Computational Biology (ICB), demonstrates that by applying machine learning algorithms on brightfield and darkfield images it is possible to detect cellular phenotypes without the need for fluorescent stains enabling label-free assays. Based on these findings the Broad Institute and the Helmholtz Zentrum München also filed a provisional patent application.
Thomas Blasi (first author) and Fabian J. Theis from the ICB are co-authors of this publication.

For more information see the original publication:
T. Blasi, H. Hennig, H.D. Summers, F.J. Theis, J. Cerveira, J.O. Patterson, D. Davies, A. Filby, A.E. Carpenter, P. Rees
Label-free cell cycle analysis for high-throughput imaging flow cytometry
Nature Communications; DOI: 10:1038/ncomms10256 (open access)
and the press release by the Helmholtz Zentrum.

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