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

IF 5.5 3区 工程技术 Q1 BIOCHEMICAL RESEARCH METHODS
Dowon Moon, Seong-Eun Kim, Chuangqi Wang, Kwonmoo Lee, Junsang Doh
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引用次数: 0

摘要

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。

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

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

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.

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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.
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