Deep Learning-Based Automated Analysis of NK Cell Cytotoxicity in Single Cancer Cell Arrays

IF 5.5 3区 工程技术 Q1 BIOCHEMICAL RESEARCH METHODS BioChip Journal Pub Date : 2024-06-27 DOI:10.1007/s13206-024-00158-y
Dowon Moon, Seong-Eun Kim, Chuangqi Wang, Kwonmoo Lee, Junsang Doh
{"title":"Deep Learning-Based Automated Analysis of NK Cell Cytotoxicity in Single Cancer Cell Arrays","authors":"Dowon Moon, Seong-Eun Kim, Chuangqi Wang, Kwonmoo Lee, Junsang Doh","doi":"10.1007/s13206-024-00158-y","DOIUrl":null,"url":null,"abstract":"<p>The cytotoxicity assay of immune cells based on live cell imaging offers comprehensive information at the single cell-level information, but the data acquisition and analysis are labor-intensive. To overcome this limitation, we previously developed single cancer cell arrays that immobilize cancer cells in microwells as single cell arrays, thus allow high-throughput data acquisition. In this study, we utilize deep learning to automatically analyze NK cell cytotoxicity in the context of single cancer cell arrays. Defined cancer cell position and the separation of NK cells and cancer cells along distinct optical planes facilitate segmentation and classification by deep learning. Various deep learning models are evaluated to determine the most appropriate model. The results of the deep learning-based automated data analysis are consistent with those of the previous manual analysis. The integration of the microwell platform and deep learning would present new opportunities for the analysis of cell–cell interactions.</p>","PeriodicalId":8768,"journal":{"name":"BioChip Journal","volume":"16 1","pages":""},"PeriodicalIF":5.5000,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BioChip Journal","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s13206-024-00158-y","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

Abstract

The cytotoxicity assay of immune cells based on live cell imaging offers comprehensive information at the single cell-level information, but the data acquisition and analysis are labor-intensive. To overcome this limitation, we previously developed single cancer cell arrays that immobilize cancer cells in microwells as single cell arrays, thus allow high-throughput data acquisition. In this study, we utilize deep learning to automatically analyze NK cell cytotoxicity in the context of single cancer cell arrays. Defined cancer cell position and the separation of NK cells and cancer cells along distinct optical planes facilitate segmentation and classification by deep learning. Various deep learning models are evaluated to determine the most appropriate model. The results of the deep learning-based automated data analysis are consistent with those of the previous manual analysis. The integration of the microwell platform and deep learning would present new opportunities for the analysis of cell–cell interactions.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于深度学习的单个癌细胞阵列中 NK 细胞细胞毒性自动分析
基于活细胞成像的免疫细胞细胞毒性检测可提供单细胞水平的综合信息,但数据采集和分析需要大量人力。为了克服这一限制,我们之前开发了单个癌细胞阵列,将癌细胞固定在微孔中作为单细胞阵列,从而实现了高通量数据采集。在本研究中,我们利用深度学习自动分析单个癌细胞阵列中的 NK 细胞细胞毒性。确定的癌细胞位置以及 NK 细胞和癌细胞沿不同光学平面的分离有助于深度学习的分割和分类。对各种深度学习模型进行了评估,以确定最合适的模型。基于深度学习的自动数据分析结果与之前的人工分析结果一致。微孔平台与深度学习的整合将为细胞-细胞相互作用的分析带来新的机遇。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
BioChip Journal
BioChip Journal 生物-生化研究方法
CiteScore
7.70
自引率
16.30%
发文量
47
审稿时长
6-12 weeks
期刊介绍: BioChip Journal publishes original research and reviews in all areas of the biochip technology in the following disciplines, including protein chip, DNA chip, cell chip, lab-on-a-chip, bio-MEMS, biosensor, micro/nano mechanics, microfluidics, high-throughput screening technology, medical science, genomics, proteomics, bioinformatics, medical diagnostics, environmental monitoring and micro/nanotechnology. The Journal is committed to rapid peer review to ensure the publication of highest quality original research and timely news and review articles.
期刊最新文献
Advancing Blood–Brain Barrier-on-a-Chip Models Through Numerical Simulations Advanced Microfluidic Platform for Tumor Spheroid Formation and Cultivation Fabricated from OSTE+ Polymer Classification of DNA Mixtures by Nanoelectrokinetic Driftless Preconcentration Fabrication of Nephrotoxic Model by Kidney-on-a-Chip Implementing Renal Proximal Tubular Function In Vitro Development of Multi-HRP-Conjugated Branched PEI/Antibody-Functionalized Gold Nanoparticles for Ultra-Sensitive ELISA
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1