结合原子力显微镜和光学图像识别的单细胞力学特性快速测量研究

Combining Atomic Force Microscopy With Optical Image Recognition for Rapid Measurements of Single-cell Mechanical Properties

目的细胞力学特性在生理病理变化过程中起着关键调控作用,开展细胞力学特性研究为揭示生命活动奥秘及疾病发生发展演变规律提供了新的视角。原子力显微镜(AFM)的出现为单细胞力学特性研究提供了强大的技术手段。AFM的独特优势是不需要对活细胞进行任何预处理即可在溶液环境下对天然状态的活细胞力学特性进行高精度(纳米级空间分辨率,皮牛级力感知灵敏度)探测。基于AFM压痕实验的细胞力学特性探测已成为生命科学领域的重要研究方法。然而,现有基于AFM的单细胞力学特性测量主要依赖于人工操作,特别是在测量过程中需要人工控制AFM探针移动到细胞表面特定位置进行压痕实验,导致实验过程耗时费力且效率低下。本文通过将AFM与光学图像自动识别相结合建立了单细胞力学特性快速测量方法。方法分别利用UNet++深度学习网络模型和模板匹配算法识别出光学图像中的细胞及AFM探针,在此基础上自动确定细胞和AFM探针之间的空间位置关系,并控制AFM探针准确移动至目标细胞表面进行压痕实验。在光学显微镜视觉导引下利用AFM微操作将单个微球黏附至AFM探针悬臂梁制作得到球形针尖探针。选取HEK 293(人胚胎肾细胞)和MCF-7(人乳腺癌细胞)两种细胞进行实验。利用Hertz-Sneddon模型对在细胞表面获取的力曲线进行分析得到细胞杨氏模量。结果基于光学图像识别结果可将AFM探针针尖准确移动至目标细胞(HEK 293或MCF-7)并对细胞力学特性进行测量,同时实验结果表明本文所提出的方法不仅适用于常规AFM锥形针尖探针,也适用于AFM球形针尖探针。结论将AFM与光学图像识别结合显著提升了AFM细胞力学特性测量效率,为高通量自动化AFM单细胞力学特性探测提供了新的方法和思路,对于细胞力学特性研究具有广泛的积极意义。

Objective Cell mechanical property plays an essential role in regulating cellular physiological and pathological processes and investigating cell mechanical property offers novel opportunities for revealing the underlying mechanisms guiding life mysteries and human diseases.The advent of atomic force microscopy(AFM)provides a powerful tool for characterizing the mechanical properties of individual cells.The unique merit of AFM is that AFM is able to probe the mechanical properties of single living cells in their native states with high precision(nanometer spatial resolution,piconewton force sensitivity)under aqueous conditions without any pretreatments.AFM-based indentation assay has now become an important method to detect cell mechanical property in the field of life sciences.However,current studies of single-cell mechanical assays based on AFM mainly rely on manual operation,particularly the operator needs to move the AFM probe to the specific position of the target cell to perform indentation assay during experiments,causing that the experimental procedures are quite time-consuming and labor-intensive with low efficiency.Here,a method combining AFM with optical image automatic recognition is developed for rapid measurements of single-cell mechanical property.MethodssThe UNet++deep learning model and the template matching algorithm were used to identify the cells and AFM probe in the optical images,allowing automatically determining the positional relationship between the target cell and AFM probe.The AFM probe was then moved to the target cell to perform indentation assays based on the recognition results.A single microsphere was glued onto the AFM probe cantilever to prepare the spherical probe.Two types of cells,including HEK 293 cell(human embryonic kidney)and MCF-7 cell(human breast cancer cell),were used for the experiments.The Hertz-Sneddon model was applied to analyze the force curves obtained during indentation assays to obtain the Young's modulus of cells.Results The AFM probe could be accurately moved to the target cell(HEK 293 or MCF-7 cell)to measure the mechanical properties of the cell based on the automatic recognition of cells and AFM probe in the optical images.The experimental results also show that the method developed here is not only suitable for conventional conical tips,but also suitable for spherical tips.Conclusion Combining AFM with optical image recognition significantly improves the efficiency of AFMbased single-cell mechanical assay,which provides a novel idea for high-throughput automated detection of single-cell mechanical property and will have active impacts on the studies of cellular mechanical properties.