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

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.