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Kim S, Lee J, Ko J, Park S, Lee SR, Kim Y, Lee T, Choi S, Kim J, Kim W, Chung Y, Kwon OH, Jeon NL. Angio-Net: deep learning-based label-free detection and morphometric analysis of in vitro angiogenesis. LAB ON A CHIP 2024; 24:751-763. [PMID: 38193617 DOI: 10.1039/d3lc00935a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
Despite significant advancements in three-dimensional (3D) cell culture technology and the acquisition of extensive data, there is an ongoing need for more effective and dependable data analysis methods. These concerns arise from the continued reliance on manual quantification techniques. In this study, we introduce a microphysiological system (MPS) that seamlessly integrates 3D cell culture to acquire large-scale imaging data and employs deep learning-based virtual staining for quantitative angiogenesis analysis. We utilize a standardized microfluidic device to obtain comprehensive angiogenesis data. Introducing Angio-Net, a novel solution that replaces conventional immunocytochemistry, we convert brightfield images into label-free virtual fluorescence images through the fusion of SegNet and cGAN. Moreover, we develop a tool capable of extracting morphological blood vessel features and automating their measurement, facilitating precise quantitative analysis. This integrated system proves to be invaluable for evaluating drug efficacy, including the assessment of anticancer drugs on targets such as the tumor microenvironment. Additionally, its unique ability to enable live cell imaging without the need for cell fixation promises to broaden the horizons of pharmaceutical and biological research. Our study pioneers a powerful approach to high-throughput angiogenesis analysis, marking a significant advancement in MPS.
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Affiliation(s)
- Suryong Kim
- Department of Mechanical Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Jungseub Lee
- Department of Mechanical Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Jihoon Ko
- Department of BioNano Technology, Gachon University, Gyeonggi, 13120, Republic of Korea
| | - Seonghyuk Park
- Department of Mechanical Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Seung-Ryeol Lee
- Department of Mechanical Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Youngtaek Kim
- Department of Mechanical Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Taeseung Lee
- Department of Mechanical Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Sunbeen Choi
- Department of Mechanical Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Jiho Kim
- Department of Mechanical Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Wonbae Kim
- Department of Mechanical Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Yoojin Chung
- Division of Computer Engineering, Hankuk University of Foreign Studies, Yongin, 17035, Republic of Korea
| | - Oh-Heum Kwon
- Department of IT convergence and Applications Engineering, Pukyong National University, Busan, 48513, Republic of Korea
| | - Noo Li Jeon
- Department of Mechanical Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Institute of Advanced Machines and Design, Seoul National University, Seoul, 08826, Republic of Korea
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