Wu Zhaozhao, Feng Yiting, Bi Ran, Liu Zhiqiang, Niu Yudi, Jin Yuhong, Li Wenjing, Chen Huijun, Shi Yan, Du Yanan
School of Biomedical Engineering, Tsinghua University, Beijing, 100084, China.
Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, 100084, China.
Cell Regen. 2025 Jun 5;14(1):21. doi: 10.1186/s13619-025-00239-9.
Mechanical properties of cells have been proposed as potential biophysical markers for cell phenotypes and functions since they are vital for maintaining biological activities. However, current approaches used to measure single-cell mechanics suffer from low throughput, high technical complexity, and stringent equipment requirements, which cannot satisfy the demand for large-scale cell sample testing. In this study, we proposed to evaluate cell stiffness at the single-cell level using deep learning. The image-based deep learning models could non-invasively predict the stiffness ranges of mesenchymal stem cells (MSCs) and macrophages in situ with high throughput and high sensitivity. We further applied the models to evaluate MSC functions including senescence, stemness, and immunomodulatory capacity as well as macrophage diversity in phenotypes and functions. Our image-based deep learning models provide potential techniques and perspectives for cell-based mechanobiology research and clinical translation.
细胞的力学特性已被认为是细胞表型和功能的潜在生物物理标志物，因为它们对维持生物活性至关重要。然而，目前用于测量单细胞力学的方法存在通量低、技术复杂性高和设备要求严格的问题，无法满足大规模细胞样本测试的需求。在本研究中，我们提出使用深度学习在单细胞水平评估细胞刚度。基于图像的深度学习模型可以高通量、高灵敏度地原位非侵入性预测间充质干细胞（MSC）和巨噬细胞的刚度范围。我们进一步应用这些模型来评估MSC的功能，包括衰老、干性和免疫调节能力，以及巨噬细胞在表型和功能上的多样性。我们基于图像的深度学习模型为基于细胞的力学生物学研究和临床转化提供了潜在的技术和前景。
