Xu Xiayu, Xiao Zhanfeng, Zhang Fan, Wang Changxiang, Wei Bo, Wang Yaohui, Cheng Bo, Jia Yuanbo, Li Yuan, Li Bin, Guo Hui, Xu Feng
The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Xi'an Jiaotong University, Xi'an 710049, P.R. China.
Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China.
Research (Wash D C). 2023 Dec 5;6:0285. doi: 10.34133/research.0285. eCollection 2023.
Visualizing cellular structures especially the cytoskeleton and the nucleus is crucial for understanding mechanobiology, but traditional fluorescence staining has inherent limitations such as phototoxicity and photobleaching. Virtual staining techniques provide an alternative approach to addressing these issues but often require substantial amount of user training data. In this study, we develop a generalizable cell virtual staining toolbox (termed CellVisioner) based on few-shot transfer learning that requires substantially reduced user training data. CellVisioner can virtually stain F-actin and nuclei for various types of cells and extract single-cell parameters relevant to mechanobiology research. Taking the label-free single-cell images as input, CellVisioner can predict cell mechanobiological status (e.g., Yes-associated protein nuclear/cytoplasmic ratio) and perform long-term monitoring for living cells. We envision that CellVisioner would be a powerful tool to facilitate on-site mechanobiological research.
可视化细胞结构，尤其是细胞骨架和细胞核，对于理解力学生物学至关重要，但传统的荧光染色存在诸如光毒性和光漂白等固有局限性。虚拟染色技术为解决这些问题提供了一种替代方法，但通常需要大量的用户训练数据。在本研究中，我们基于少样本迁移学习开发了一个可推广的细胞虚拟染色工具箱（称为CellVisioner），该工具箱所需的用户训练数据大幅减少。CellVisioner可以对各种类型的细胞进行F-肌动蛋白和细胞核的虚拟染色，并提取与力学生物学研究相关的单细胞参数。以无标记的单细胞图像为输入，CellVisioner可以预测细胞的力学生物学状态（例如，Yes相关蛋白的核/质比）并对活细胞进行长期监测。我们设想CellVisioner将成为促进现场力学生物学研究的强大工具。
