School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang 110159, China.
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
Nano Lett. 2024 Oct 2;24(39):12323-12332. doi: 10.1021/acs.nanolett.4c03861. Epub 2024 Sep 20.
Mechanical forces are essential for life activities, and the mechanical phenotypes of single cells are increasingly gaining attention. Atomic force microscopy (AFM) has been a standard method for single-cell nanomechanical assays, but its efficiency is limited due to its reliance on manual operation. Here, we present a study of deep learning image recognition-assisted AFM that enables automated high-throughput single-cell nanomechanical measurements. On the basis of the label-free identification of the cell structures and the AFM probe in optical bright-field images as well as the consequent automated movement of the sample stage and AFM probe, the AFM probe tip could be accurately and sequentially moved onto the specific parts of individual living cells to perform a single-cell indentation assay or single-cell force spectroscopy in a time-efficient manner. The study illustrates a promising method based on deep learning for achieving operator-independent high-throughput AFM single-cell nanomechanics, which will benefit the application of AFM in mechanobiology.
力学对于生命活动至关重要，单细胞的力学表型也越来越受到关注。原子力显微镜（AFM）已经成为单细胞纳米力学检测的标准方法，但由于其依赖于手动操作，因此效率有限。在这里，我们研究了深度学习图像识别辅助 AFM，该方法可以实现自动化高通量单细胞纳米力学测量。基于光学明场图像中对细胞结构和 AFM 探针的无标记识别，以及随后对样品台和 AFM 探针的自动移动，AFM 探针尖端可以准确地、顺序地移动到单个活细胞的特定部位，以高效地进行单细胞压痕测定或单细胞力谱学。该研究说明了一种基于深度学习的有前途的方法，可实现无需操作人员的高通量 AFM 单细胞纳米力学，这将有益于 AFM 在机械生物学中的应用。
