Development of a single-detector imaging flow cytometer for light scattering and fluorescence dual-modality analysis with deep learning

Zhiwen Wang; Jie Zhou; Qiao Liu; Xuantao Su

doi:10.1117/12.3001191

12 March 2024 Development of a single-detector imaging flow cytometer for light scattering and fluorescence dual-modality analysis with deep learning

Zhiwen Wang, Jie Zhou, Qiao Liu, Xuantao Su

Author Affiliations +

Proceedings Volume 12846, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XXII; 1284608 (2024) https://doi.org/10.1117/12.3001191
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

Imaging flow cytometry (IFC) has been widely applied in biomedical research due to its numerous advantages, including multiparametric analysis, microscopic imaging and high-throughput detection. Previous research in our lab has demonstrated the effectiveness of two-dimensional light scattering (LS) and brightfield (BF) dual-modality imaging techniques for detecting and distinguishing unlabeled cells. As fluorescence (FL) imaging techniques are sensitive to specifically labeled cells, here we introduce a single-detector IFC enabling simultaneous imaging of LS signals and BF/FL signals for automatic single-cell analysis with deep learning. The special optical design with a knife-edge right angle (KERA) prism is adopted to simultaneously capture corresponding LS patterns in defocus and BF/FL patterns in focus on a single detector. The LS and BF dual-modality flow imaging results of 2 μm and 3.87 μm unlabeled microspheres can be obtained by our system, which can also simultaneously acquire LS and FL results for fluorescent microspheres of 2 μm and 4 μm in diameter. The results of these beads demonstrate excellent agreement between LS patterns and Mie scattering simulations. The obtained LS and BF dual-modality cell images of A2780 and Hey cells are analyzed using a visual geometry group 19 (VGG19) deep learning method through feature extraction and fusion to show accurate classification of ovarian cancer cell subtypes. In conclusion, our development of a single-detector imaging flow cytometer enables the simultaneous capture of two-dimensional light-scattering and fluorescence/brightfield images, where an automatic analysis with deep learning can be performed, showcasing potential wide applications in biomedicine.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhiwen Wang, Jie Zhou, Qiao Liu, and Xuantao Su "Development of a single-detector imaging flow cytometer for light scattering and fluorescence dual-modality analysis with deep learning", Proc. SPIE 12846, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XXII, 1284608 (12 March 2024); https://doi.org/10.1117/12.3001191

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