Li Zhi, Zhang Xiaoyu, Li Guosheng, Peng Jun, Su Xuantao
School of Integrated Circuits, Shandong University, Jinan 250101, China; Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan 250061, China.
Department of Hematology, Qilu Hospital of Shandong University, Jinan 250012, China.
Comput Methods Programs Biomed. 2025 Jun;264:108726. doi: 10.1016/j.cmpb.2025.108726. Epub 2025 Mar 15.
Single-cell imaging plays a key role in various fields, including drug development, disease diagnosis, and personalized medicine. To obtain multi-modal information from a single-cell image, especially for label-free cells, this study develops modal expansion cytometry for label-free single-cell analysis.
The study utilizes a deep learning-based architecture to expand single-mode light scattering images into multi-modality images, including bright-field (non-fluorescent) and fluorescence images, for label-free single-cell analysis. By combining adversarial loss, L1 distance loss, and VGG perceptual loss, a new network optimization method is proposed. The effectiveness of this method is verified by experiments on simulated images, standard spheres of different sizes, and multiple cell types (such as cervical cancer and leukemia cells). Additionally, the capability of this method in single-cell analysis is assessed through multi-modal cell classification experiments, such as cervical cancer subtypes.
This is demonstrated by using both cervical cancer cells and leukemia cells. The expanded bright-field and fluorescence images derived from the light scattering images align closely with those obtained through conventional microscopy, showing a contour ratio near 1 for both the whole cell and its nucleus. Using machine learning, the subtyping of cervical cancer cells achieved 92.85 % accuracy with the modal expansion images, which represents an improvement of nearly 20 % over single-mode light scattering images.
This study demonstrates the light scattering imaging modal expansion cytometry with deep learning has the capability to expand the single-mode light scattering image into the artificial multimodal images of label-free single cells, which not only provides the visualization of cells but also helps for the cell classification, showing great potential in the field of single-cell analysis such as cancer cell diagnosis.
单细胞成像在包括药物开发、疾病诊断和个性化医疗等各个领域发挥着关键作用。为了从单细胞图像中获取多模态信息，特别是对于无标记细胞，本研究开发了用于无标记单细胞分析的模态扩展流式细胞术。
该研究利用基于深度学习的架构将单模光散射图像扩展为多模态图像，包括明场（非荧光）和荧光图像，用于无标记单细胞分析。通过结合对抗损失、L1距离损失和VGG感知损失，提出了一种新的网络优化方法。通过对模拟图像、不同大小的标准球体以及多种细胞类型（如宫颈癌细胞和白血病细胞）进行实验，验证了该方法的有效性。此外，通过多模态细胞分类实验，如宫颈癌亚型分析，评估了该方法在单细胞分析中的能力。
使用宫颈癌细胞和白血病细胞均证明了这一点。从光散射图像衍生的扩展明场和荧光图像与通过传统显微镜获得的图像紧密对齐，整个细胞及其细胞核的轮廓比均接近1。使用机器学习，模态扩展图像对宫颈癌细胞亚型分类的准确率达到92.85%，比单模光散射图像提高了近20%。
本研究表明，基于深度学习的光散射成像模态扩展流式细胞术能够将单模光散射图像扩展为无标记单细胞的人工多模态图像，不仅提供了细胞可视化，还有助于细胞分类，在癌细胞诊断等单细胞分析领域显示出巨大潜力。
