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基于活细胞AFM图像的细胞骨架网络的机器学习引导的重建

Hanqiu Ju^1,2, Henrik Skibbe³, Masaya Fukui¹

¹Laboratory of Data-driven Biology, Graduate School of Integrated Sciences for Life, Hiroshima University, Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8526, Japan.

|October 11, 2024

概括

一种新的机器学习方法揭示了活性丝在细胞中如何重组. 这项技术可视化单个的actin丝,揭示它们在细胞结构中的特定方向,如lamellipodia和细胞皮层.

科学领域:

细胞生物学细胞生物学
生物物理学的生物物理.
显微镜的使用方法

背景情况:

了解在单个导线水平上在运动细胞中的动态活性丝 (F-actins) 的动态重组仍然是一个挑战.
高速原子力显微镜 (HS-AFM) 为细胞内F-actin动态的实时成像提供了潜力,但图像噪声和低分辨率阻碍了详细分析.

研究的目的:

开发一种新的机器学习方法,用于在HS-AFM图像中定量识别和分析单个F-actin丝.
阐明活细胞内F-actin网络的结构动力学和组织,特别是在lamellipodia和细胞皮层中.

主要方法:

开发一种机器学习算法,以提高分辨率,并从HS-AFM图像中估计F-actin方向.
应用开发的方法来分析F-actin组织在lamellipodia和移动细胞的细胞皮质.

主要成果:

发现,lamellipodia中的F-actin纤维面向±35°向膜方向,与Arp2/3复合体诱导的分支相一致.
对细胞皮层的分析揭示了四个特定角度的非随机F-actin定向,这表明了一个新的组织机制.

结论:

开发的机器学习方法显著提高了识别和分析单个F-actin丝的能力,克服了HS-AFM的局限性.
这些发现为不同细胞区中的F-actin组织机制提供了新的洞察力,进步了我们对细胞骨动态的理解.

关键词:

生物科学生物科学计算机科学计算机科学工程工程工程工程工程物理物理物理物理物理

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