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Nat Commun. 2017 Sep 6;8(1):463. doi: 10.1038/s41467-017-00623-3.

Reconstructing cell cycle and disease progression using deep learning.

Author information

1
Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany.
2
Department of Physics, Arnold Sommerfeld Center for Theoretical Physics, LMU München, Munich, Germany.
3
Imaging Platform at the Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA.
4
Flow Cytometry Core Facility, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.
5
College of Engineering, Swansea University, Singleton Park, Swansea, UK.
6
Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany. fabian.theis@helmholtz-muenchen.de.
7
Department of Mathematics, TU München, Munich, Germany. fabian.theis@helmholtz-muenchen.de.
8
Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany. alex.wolf@helmholtz-muenchen.de.

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.The interpretation of information-rich, high-throughput single-cell data is a challenge requiring sophisticated computational tools. Here the authors demonstrate a deep convolutional neural network that can classify cell cycle status on-the-fly.

PMID:
28878212
PMCID:
PMC5587733
DOI:
10.1038/s41467-017-00623-3
[Indexed for MEDLINE]
Free PMC Article

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