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基于图表的超分辨率蛋白-蛋白相互作用的空间近距离预测了单细胞中癌症药物反应

Nicholas Zhang^1,2,3, Shuangyi Cai^1,3, Mingshuang Wang^1,3

¹Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA USA.

Cellular and molecular bioengineering

|November 8, 2024

概括

这项研究介绍了一种基于图形的超分辨率蛋白质-蛋白质相互作用 (GSR-PPI) 技术,用于绘制细胞信号. GSR-PPI准确地预测药物反应和T细胞状态,优于传统方法.

科学领域:

蛋白质组学是指蛋白质组学.
细胞信号传输细胞信号传输
癌症生物学癌症生物学
深度学习 (Deep Learning) 是一种深度学习.

背景情况:

目前的大型分子分析缺乏癌症信号的空间分辨率.
了解空间蛋白-蛋白相互作用 (PPI) 对药物耐药性机制至关重要.

研究的目的:

开发基于图形的超分辨率蛋白质-蛋白质相互作用 (GSR-PPI) 技术.
在空间上解析单细胞信号网络.
评估是否更高分辨率的显微镜提高PPI研究使用深度学习.

主要方法:

在EGFR突变细胞上进行单细胞空间近距离结合试验 (PLA),这些细胞接受了Osimertinib的治疗.
使用广场和超分辨率显微镜进行多重复合的PPI成像.
基于图形的深度学习模型用于分析亚细胞蛋白相互作用和分类药物治疗状态.

主要成果:

GSR-PPI准确地预测了EGFR突变细胞中的药物反应,表现优于基线模型.
与广场成像相比,超分辨率显微镜显著提高了准确性.
在癌细胞,人类肺组织和T细胞刺激状态中,GSR-PPI分类的药物治疗状态.

结论:

GSR-PPI框架提供了对空间蛋白相互作用和药物反应的见解.
这种技术增强了对信号生物学和药物耐药性的研究.
GSR-PPI显示了分析复杂生物系统的潜力,包括T细胞激活.

关键词:

图形神经网络是一个神经网络.基于图像的系统传递生物信号.肺癌的治疗方法是肺癌治疗.预测药物反应的药物反应.蛋白质蛋白质相互作用空间信号交互动学空间信号交互学超高分辨率显微镜的使用方法

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