Open Access

Frontiers in Cellular Neuroscience, volume 14

Convolutional Neural Networks Can Predict Retinal Differentiation in Retinal Organoids

Kegeles Evgenii ^{1, 2}

Naumov Anton ³

Karpulevich Evgeny A ^{2, 3, 4}

Volchkov Pavel ^{2, 5}

Baranov Petr ¹

Hide authors affiliations Show authors affiliations: 5 affiliations

Department of Ophthalmology, The Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, United States |

Genome Technologies and Bioinformatics Research Centre, Moscow Institute of Physics and Technology, Russia |

Department of Information Systems, Ivannikov Institute for System Programming of the Russian Academy of Sciences, Russia |

⁴

National Research Center “Kurchatov Institute”, Russia |

⁵

Endocrinology Research Centre, Institute for Personalized Medicine, Russia |

Publication type: Journal Article

Publication date: 2020-07-03

Frontiers Media S.A.

Journal: Frontiers in Cellular Neuroscience

Quartile SCImago

Quartile WOS

Impact factor: 5.3

ISSN: 16625102

DOI: 10.3389/fncel.2020.00171

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PubMed ID: 32719585

Cellular and Molecular Neuroscience

Abstract

We have developed a deep learning-based computer algorithm to recognize and predict retinal differentiation in stem cell-derived organoids based on bright-field imaging. The three-dimensional “organoid” approach for the differentiation of pluripotent stem cells (PSC) into retinal and other neural tissues has become a major in vitro strategy to recapitulate development. We decided to develop a universal, robust, and non-invasive method to assess retinal differentiation that would not require chemical probes or reporter gene expression. We hypothesized that basic-contrast bright-field (BF) images contain sufficient information on tissue specification, and it is possible to extract this data using convolutional neural networks (CNNs). Retina-specific Rx-green fluorescent protein mouse embryonic reporter stem cells have been used for all of the differentiation experiments in this work. The BF images of organoids have been taken on day 5 and fluorescent on day 9. To train the CNN, we utilized a transfer learning approach: ImageNet pre-trained ResNet50v2, VGG19, Xception, and DenseNet121 CNNs had been trained on labeled BF images of the organoids, divided into two categories (retina and non-retina), based on the fluorescent reporter gene expression. The best-performing classifier with ResNet50v2 architecture showed a receiver operating characteristic-area under the curve score of 0.91 on a test dataset. A comparison of the best-performing CNN with the human-based classifier showed that the CNN algorithm performs better than the expert in predicting organoid fate (84% vs. 67 ± 6% of correct predictions, respectively), confirming our original hypothesis. Overall, we have demonstrated that the computer algorithm can successfully recognize and predict retinal differentiation in organoids before the onset of reporter gene expression. This is the first demonstration of CNN’s ability to classify stem cell-derived tissue in vitro.

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Citations by journals

	1 2
Scientific Reports	Scientific Reports, 2, 5.71% Scientific Reports 2 publications, 5.71%
Development (Cambridge)	Development (Cambridge), 1, 2.86% Development (Cambridge) 1 publication, 2.86%
Bio-Design and Manufacturing	Bio-Design and Manufacturing, 1, 2.86% Bio-Design and Manufacturing 1 publication, 2.86%
International Journal of Molecular Sciences	International Journal of Molecular Sciences, 1, 2.86% International Journal of Molecular Sciences 1 publication, 2.86%
Frontiers in Cellular Neuroscience	Frontiers in Cellular Neuroscience, 1, 2.86% Frontiers in Cellular Neuroscience 1 publication, 2.86%
Frontiers in Molecular Biosciences	Frontiers in Molecular Biosciences, 1, 2.86% Frontiers in Molecular Biosciences 1 publication, 2.86%
Frontiers in Bioengineering and Biotechnology	Frontiers in Bioengineering and Biotechnology, 1, 2.86% Frontiers in Bioengineering and Biotechnology 1 publication, 2.86%
Journal of the Korean Physical Society	Journal of the Korean Physical Society, 1, 2.86% Journal of the Korean Physical Society 1 publication, 2.86%
Archives of Toxicology	Archives of Toxicology, 1, 2.86% Archives of Toxicology 1 publication, 2.86%
Seminars in Cell and Developmental Biology	Seminars in Cell and Developmental Biology, 1, 2.86% Seminars in Cell and Developmental Biology 1 publication, 2.86%
PLoS ONE	PLoS ONE, 1, 2.86% PLoS ONE 1 publication, 2.86%
Developmental Biology	Developmental Biology, 1, 2.86% Developmental Biology 1 publication, 2.86%
SLAS Discovery	SLAS Discovery, 1, 2.86% SLAS Discovery 1 publication, 2.86%
Developmental Neurobiology	Developmental Neurobiology, 1, 2.86% Developmental Neurobiology 1 publication, 2.86%
Advanced Science	Advanced Science, 1, 2.86% Advanced Science 1 publication, 2.86%
Lab on a Chip	Lab on a Chip, 1, 2.86% Lab on a Chip 1 publication, 2.86%
Kidney Research and Clinical Practice	Kidney Research and Clinical Practice, 1, 2.86% Kidney Research and Clinical Practice 1 publication, 2.86%
Frontiers in Immunology	Frontiers in Immunology, 1, 2.86% Frontiers in Immunology 1 publication, 2.86%
Tissue Engineering and Regenerative Medicine	Tissue Engineering and Regenerative Medicine, 1, 2.86% Tissue Engineering and Regenerative Medicine 1 publication, 2.86%
Bioactive Materials	Bioactive Materials, 1, 2.86% Bioactive Materials 1 publication, 2.86%
Proceedings of the National Academy of Sciences of the United States of America	Proceedings of the National Academy of Sciences of the United States of America, 1, 2.86% Proceedings of the National Academy of Sciences of the United States of America 1 publication, 2.86%
Current Stem Cell Reports	Current Stem Cell Reports, 1, 2.86% Current Stem Cell Reports 1 publication, 2.86%
Medicine in Novel Technology and Devices	Medicine in Novel Technology and Devices, 1, 2.86% Medicine in Novel Technology and Devices 1 publication, 2.86%
Computers in Biology and Medicine	Computers in Biology and Medicine, 1, 2.86% Computers in Biology and Medicine 1 publication, 2.86%
Bioengineering & Translational Medicine	Bioengineering & Translational Medicine, 1, 2.86% Bioengineering & Translational Medicine 1 publication, 2.86%
Advanced Drug Delivery Reviews	Advanced Drug Delivery Reviews, 1, 2.86% Advanced Drug Delivery Reviews 1 publication, 2.86%
Progress in Retinal and Eye Research	Progress in Retinal and Eye Research, 1, 2.86% Progress in Retinal and Eye Research 1 publication, 2.86%
Prostate	Prostate, 1, 2.86% Prostate 1 publication, 2.86%
Life Sciences	Life Sciences, 1, 2.86% Life Sciences 1 publication, 2.86%
	1 2

Citations by publishers

	1 2 3 4 5 6 7
Springer Nature	Springer Nature, 7, 20% Springer Nature 7 publications, 20%
Elsevier	Elsevier, 7, 20% Elsevier 7 publications, 20%
Frontiers Media S.A.	Frontiers Media S.A., 4, 11.43% Frontiers Media S.A. 4 publications, 11.43%
Wiley	Wiley, 4, 11.43% Wiley 4 publications, 11.43%
The Company of Biologists	The Company of Biologists, 1, 2.86% The Company of Biologists 1 publication, 2.86%
Multidisciplinary Digital Publishing Institute (MDPI)	Multidisciplinary Digital Publishing Institute (MDPI), 1, 2.86% Multidisciplinary Digital Publishing Institute (MDPI) 1 publication, 2.86%
Public Library of Science (PLoS)	Public Library of Science (PLoS), 1, 2.86% Public Library of Science (PLoS) 1 publication, 2.86%
SAGE	SAGE, 1, 2.86% SAGE 1 publication, 2.86%
Royal Society of Chemistry (RSC)	Royal Society of Chemistry (RSC), 1, 2.86% Royal Society of Chemistry (RSC) 1 publication, 2.86%
The Korean Society of Nephrology	The Korean Society of Nephrology, 1, 2.86% The Korean Society of Nephrology 1 publication, 2.86%
KeAi Communications Co.	KeAi Communications Co., 1, 2.86% KeAi Communications Co. 1 publication, 2.86%
Proceedings of the National Academy of Sciences (PNAS)	Proceedings of the National Academy of Sciences (PNAS), 1, 2.86% Proceedings of the National Academy of Sciences (PNAS) 1 publication, 2.86%
American Chemical Society (ACS)	American Chemical Society (ACS), 1, 2.86% American Chemical Society (ACS) 1 publication, 2.86%
	1 2 3 4 5 6 7

We do not take into account publications that without a DOI.
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Kegeles E. et al. Convolutional Neural Networks Can Predict Retinal Differentiation in Retinal Organoids // Frontiers in Cellular Neuroscience. 2020. Vol. 14.

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Kegeles E., Naumov A., Karpulevich E. A., Volchkov P., Baranov P. Convolutional Neural Networks Can Predict Retinal Differentiation in Retinal Organoids // Frontiers in Cellular Neuroscience. 2020. Vol. 14.

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RIS Copy

TY - JOUR

DO - 10.3389/fncel.2020.00171

UR - https://doi.org/10.3389%2Ffncel.2020.00171

TI - Convolutional Neural Networks Can Predict Retinal Differentiation in Retinal Organoids

T2 - Frontiers in Cellular Neuroscience

AU - Kegeles, Evgenii

AU - Naumov, Anton

AU - Karpulevich, Evgeny A

AU - Volchkov, Pavel

AU - Baranov, Petr

PY - 2020

DA - 2020/07/03 00:00:00

PB - Frontiers Media S.A.

VL - 14

PMID - 32719585

SN - 1662-5102

ER -

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BibTex Copy

@article{2020_Kegeles,

author = {Evgenii Kegeles and Anton Naumov and Evgeny A Karpulevich and Pavel Volchkov and Petr Baranov},

title = {Convolutional Neural Networks Can Predict Retinal Differentiation in Retinal Organoids},

journal = {Frontiers in Cellular Neuroscience},

year = {2020},

volume = {14},

publisher = {Frontiers Media S.A.},

month = {jul},

url = {https://doi.org/10.3389%2Ffncel.2020.00171},

doi = {10.3389/fncel.2020.00171}

}

Found error?

Publisher

Frontiers Media S.A.

Journal

Frontiers in Cellular Neuroscience

Quartile SCImago

Quartile WOS

Impact factor

5.3

ISSN

16625102 (Print)

Labs

MIPT Laboratory of Genomic Engineering

Profiles

Volchkov, Pavel Y