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托莫斯科:一种神经网络方法,用于细胞冷ET的质量评估

Xuqian Tan¹, Ethan Boniuk^1,2, Anisha Abraham^1,3

¹Verna and Marrs McLean Department of Biochemistry and Molecular Pharmacology, Baylor College of Medicine, Houston, TX 77030, USA.

Research square

|May 9, 2025

概括

我们开发了TomoScore,这是一种深度学习工具,可以自动评估电子冷断层扫描 (cryo-ET) 数据质量,用于细胞注释. 这简化了处理,减少了对查断层图像的专家判断.

科学领域:

结构生物学结构生物学
细胞生物学细胞生物学
显微镜的使用方法

背景情况:

电子冷断层扫描 (cryo-ET) 能够对细胞结构进行无标签的3D可视化.
断层图像质量是高度可变的,需要对数据处理进行专家评估.
不同的冷ET应用 (例如,注释与平均) 有不同的质量要求.

研究的目的:

开发一种自动化工具,用于评估细胞注释的冷-ET断层图像质量.
提供对断层图像适合区分亚细胞特征的定量测量.
为了研究电子剂量对断层图像质量的影响.

主要方法:

开发一种基于深度学习的选工具,名为TomoScore.
应用TomoScore来评估断层图像适合于细胞注释.
分析累积电子剂量与断层图像质量之间的关系.

主要成果:

托莫斯科提供了对细胞注释相关的断层图像质量的定量测量.
该工具自动化了断层扫描的预选,减少了人工专家的参与.
为冷ET数据收集提出了一个最佳的电子剂量范围.

结论:

TomoScore有效地自动化了用于细胞注释任务的冷ET数据的质量评估.
开发的工具提高了冷ET数据处理管道的效率.
结果为优化冷ET数据采集参数提供了洞察力.

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