Yang Xuliang, Yang Yanqi, Zhang Zhihui, Li Mi
School of Artificial Intelligence, Shenyang University of Technology, Shenyang 110870, China.
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
Langmuir. 2024 Jan 9;40(1):837-852. doi: 10.1021/acs.langmuir.3c03046. Epub 2023 Dec 28.
Atomic force microscopy (AFM)-based force spectroscopy assay has become an important method for characterizing the mechanical properties of single living cells under aqueous conditions, but a disadvantage is its reliance on manual operation and experience as well as the resulting low throughput. Particularly, providing a capacity to accurately identify the type of the cell grown in co-culture environments without the need of fluorescent labeling will further facilitate the applications of AFM in life sciences. Here, we present a study of deep learning image recognition-assisted AFM, which not only enables fluorescence-independent recognition of the identity of single co-cultured cells but also allows efficient downstream AFM force measurements of the identified cells. With the use of the deep learning-based image recognition model, the viability and type of individual cells grown in co-culture environments were identified directly from the optical bright-field images, which were confirmed by the following cell growth and fluorescent labeling results. Based on the image recognition results, the positional relationship between the AFM probe and the targeted cell was automatically determined, allowing the precise movement of the AFM probe to the target cell to perform force measurements. The experimental results show that the presented method was applicable not only to the conventional (microsphere-modified) AFM probe used in AFM indentation assay for measuring the Young's modulus of single co-cultured cells but also to the single-cell probe used in AFM-based single-cell force spectroscopy (SCFS) assay for measuring the adhesion forces of single co-cultured cells. The study illustrates deep learning imaging recognition-assisted AFM as a promising approach for label-free and high-throughput detection of single-cell mechanics under co-culture conditions, which will facilitate unraveling the mechanical cues involved in cell-cell interactions in their native states at the single-cell level and will benefit the field of mechanobiology.
基于原子力显微镜（AFM）的力谱分析方法已成为在水性条件下表征单个活细胞力学特性的重要手段，但其缺点是依赖人工操作和经验，且通量较低。特别是，若能在无需荧光标记的情况下准确识别共培养环境中生长的细胞类型，将进一步推动AFM在生命科学中的应用。在此，我们展示了一项关于深度学习图像识别辅助AFM的研究，该方法不仅能够在无需荧光的情况下识别单个共培养细胞的身份，还能对已识别细胞进行高效的下游AFM力测量。通过使用基于深度学习的图像识别模型，直接从光学明场图像中识别出共培养环境中单个细胞的活力和类型，后续的细胞生长和荧光标记结果证实了这一点。基于图像识别结果，自动确定AFM探针与目标细胞之间的位置关系，使AFM探针能够精确移动到目标细胞进行力测量。实验结果表明，所提出的方法不仅适用于用于测量单个共培养细胞杨氏模量的AFM压痕分析中使用的传统（微球修饰）AFM探针，也适用于用于测量单个共培养细胞粘附力的基于AFM的单细胞力谱（SCFS）分析中使用的单细胞探针。该研究表明，深度学习成像识别辅助AFM是一种在共培养条件下对单细胞力学进行无标记和高通量检测的有前景的方法，这将有助于在单细胞水平上揭示天然状态下细胞间相互作用中涉及的力学线索，并将造福于机械生物学领域。
