Reconstructing cell cycle and disease progression using deep learning

Philipp Eulenberg, Niklas Köhler, Thomas Blasi, Andrew Filby, Anne E. Carpenter, Paul Rees, Fabian J. Theis () and F. Alexander Wolf ()
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Philipp Eulenberg: Institute of Computational Biology
Niklas Köhler: Institute of Computational Biology
Thomas Blasi: Institute of Computational Biology
Andrew Filby: Newcastle University
Anne E. Carpenter: Imaging Platform at the Broad Institute of Harvard and Massachusetts Institute of Technology
Paul Rees: Imaging Platform at the Broad Institute of Harvard and Massachusetts Institute of Technology
Fabian J. Theis: Institute of Computational Biology
F. Alexander Wolf: Institute of Computational Biology

Nature Communications, 2017, vol. 8, issue 1, 1-6

Abstract: Abstract We show that deep convolutional neural networks combined with nonlinear dimension reduction enable reconstructing biological processes based on raw image data. We demonstrate this by reconstructing the cell cycle of Jurkat cells and disease progression in diabetic retinopathy. In further analysis of Jurkat cells, we detect and separate a subpopulation of dead cells in an unsupervised manner and, in classifying discrete cell cycle stages, we reach a sixfold reduction in error rate compared to a recent approach based on boosting on image features. In contrast to previous methods, deep learning based predictions are fast enough for on-the-fly analysis in an imaging flow cytometer.

Date: 2017
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DOI: 10.1038/s41467-017-00623-3

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