Prediction of Stem Cell State Using Cell Image‐Based Deep Learning

Author:

Kim Minjae123,Namkung Yong134,Hyun Donghun134,Hong Sunghoi15ORCID

Affiliation:

1. Laboratory of Stem Cell and NeuroRegeneration School of Biosystems and Biomedical Sciences Korea University 145 Anam-ro Seongbuk-gu Seoul 02841 Republic of Korea

2. Department of Biomedical Engineering Interdisciplinary Program in Precision Public Health Korea University 145 Anam-ro Seongbuk-gu Seoul 02841 Republic of Korea

3. Institution of Stem Cell R&D iNStemCARE Inc Seoul Forest A Center 6th Floor, 13-209 Seongsu-dong 1ga Seongdong-gu Seoul 04790 Republic of Korea

4. Department of Integrated Biomedical and Life Science Korea University 145 Anam-ro Seongbuk-gu Seoul 02841 Republic of Korea

5. Interdisciplinary Program in Precision Public Health BK21 Four Institute of Precision Public Health Korea University 145 Anam-ro Seongbuk-gu Seoul 02841 Republic of Korea

Abstract

Stem cells represent an ideal source for regenerative medicine; however, longitudinal assessment of stem cell phenotype and function is challenging. Contrastingly, a convolutional neural network (CNN) algorithm can automatically extract the image features and produce highly accurate image recognition. Thus, this study implements CNN to establish stable and reproducible cell culture experiments by predicting a unique morphology of pluripotent stem cell (PSC) lines. Interestingly, the algorithm distinguishes the PSC lines cultured in the different cell culture conditions, such as the presence or absence of small molecules and/or the long‐ or short‐term culture in our induced PSC (iPSC) models, which include iPSC lines with abnormal gene expression patterns and genomic abnormalities. Our deep learning technology accurately classifies the various cell lines with or without genetic defects using only the cell images, without any labeling process. This suggests that the CNN system may simplify the various tasks involving stable cell cultures and their differentiation.

Funder

Institute for Information and Communications Technology Promotion

Korea National Institute of Health

Publisher

Wiley

Subject

General Medicine

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3