成像流式细胞术,它结合了流式细胞术和显微镜的优点,已成为各种生物医学领域(如癌症检测)中细胞分析的强大工具。在这项研究中,我们通过采用空间波分复用技术开发了多重成像流式细胞术(mIFC)。我们的mIFC可以同时获得流中单个细胞的明场和多色荧光图像,由金属卤化物灯激发并由单个检测器测量。分辨率测试镜头多重成像实验的统计分析结果,放大试验镜头,和荧光微球验证了mIFC的操作具有良好的成像通道一致性和微米级区分能力。设计了一种用于多路图像处理的深度学习方法,该方法由三个深度学习网络(U-net,非常深的超分辨率,和视觉几何组19)。证明了分化簇24(CD24)成像通道比明场更敏感,核,或癌抗原125(CA125)成像通道在分类三种类型的卵巢细胞系(IOSE80正常细胞,A2780和OVCAR3癌细胞)。当考虑所有四个成像通道时,通过深度学习分析对这三种类型的细胞进行分类的平均准确率为97.1%。我们的单检测器mIFC有望用于未来成像流式细胞仪的开发以及在各种生物医学领域中通过深度学习进行自动单细胞分析。
Imaging flow cytometry, which combines the advantages of flow cytometry and microscopy, has emerged as a powerful tool for cell analysis in various biomedical fields such as cancer detection. In this study, we develop multiplex imaging flow cytometry (mIFC) by employing a spatial wavelength division multiplexing technique. Our mIFC can simultaneously obtain brightfield and multi-color fluorescence images of individual cells in flow, which are excited by a metal halide lamp and measured by a single detector. Statistical analysis results of multiplex imaging experiments with resolution test lens, magnification test lens, and fluorescent microspheres validate the operation of the mIFC with good imaging channel consistency and micron-scale differentiation capabilities. A deep learning method is designed for multiplex image processing that consists of three deep learning networks (U-net, very deep super resolution, and visual geometry group 19). It is demonstrated that the cluster of differentiation 24 (CD24) imaging channel is more sensitive than the brightfield, nucleus, or cancer antigen 125 (CA125) imaging channel in classifying the three types of ovarian cell lines (IOSE80 normal cell, A2780, and OVCAR3 cancer cells). An average accuracy rate of 97.1% is achieved for the classification of these three types of cells by deep learning analysis when all four imaging channels are considered. Our single-detector mIFC is promising for the development of future imaging flow cytometers and for the automatic single-cell analysis with deep learning in various biomedical fields.