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在体积电子显微镜中对全细胞器官进行细分

Larissa Heinrich¹, Davis Bennett¹, David Ackerman¹

¹Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.

|October 7, 2021

概括

这项研究提出了一种用于3D细胞重建的自动化方法,使其能够详细分析器官相互作用. OpenOrganelle平台提供开放式数据和工具,以促进细胞生物学研究.

科学领域:

细胞生物学
生物物理
计算生物学

背景情况:

了解细胞组织需要高分辨率的3D器官重建.
可扩展的自动化方法对于实现全细胞重建至关重要.

研究的目的:

从FIB-SEM数据开发和验证用于细分和重建细胞器官的自动深度学习方法.
创建一个3D细胞生物学研究的开放资源.

主要方法:

在FIB-SEM数据集 (4nm和8nm分辨率) 中对35个器官类进行注释.
训练深度学习模型进行细胞结构细分.
对细分性能的验证和在定量分析中的应用.

主要成果:

从多种细胞类型中成功细分和3D重建多种器官.
自动定量器官空间相互作用的演示.
对相关光和电子显微镜的自动注册的开发.

结论:

自动化3D重建方法显著提升了细胞架构的研究.
开放Organelle存储库为改善细胞重建提供全球数据,代码和模型.
这项工作使细胞组织和相关成像的新定量分析成为可能.

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