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Eulenberg, P. ; Köhler, N. ; Blasi, T. ; Filby, A.* ; Carpenter, A.E.* ; Rees, P.* ; Theis, F.J. ; Wolf, F.A.

Reconstructing cell cycle and disease progression using deep learning.

Nat. Commun. 8:463 (2017)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Microscopy Images; Flow-cytometry; Classification; Regulators
Language english
Publication Year 2017
HGF-reported in Year 2017
ISSN (print) / ISBN 2041-1723
e-ISSN 2041-1723
Journal Nature Communications
Quellenangaben Volume: 8, Issue: 1, Pages: , Article Number: 463 Supplement: ,
Publisher Nature Publishing Group
Publishing Place London
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-503800-001
PubMed ID 28878212
DOI 10.1038/s41467-017-00623-3
WOS ID WOS:000409458000017
Scopus ID 85028914209
Erfassungsdatum 2017-09-13
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