CELLetter:利用大型语言模型和双流网络来识别特定上下文的配体-受体相互作用,用于细胞-细胞通信分析
Wei Wu1, Junfeng Huang1, Yan Jiang2
1School of Biological Science and Medical Engineering, Hunan University of Technology, Hunan, Zhuzhou 412007, China.
Briefings in bioinformatics
|December 26, 2025
概括
CELLetter是一个深度学习框架,识别了细胞间通信的连接体-受体相互作用和下游信号. 它的性能优于现有的方法,揭示了潜在的治疗点,如HNSCC中的MIF-CD44.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 基因组学就是基因组学.
背景情况:
- 细胞间通信 (CCC) 对多细胞生物来说至关重要.
- 现有的计算方法往往忽视了细胞内信号传输,依赖于静态的体受体 (L-R) 数据库.
研究的目的:
- 开发一种新的深度学习框架,CELLetter,用于识别L-R相互作用和破译细胞信号.
- 整合L-R共表达与下游转录因子 (TF) 活动,以增强通信分析.
主要方法:
- 使用 ProstT5 蛋白质语言模型进行特征嵌入.
- 采用双流架构,用于功能融合和交互的门机制.
- 集成的L-R对,scRNA-seq数据和TF活动来量化通信强度.
主要成果:
- 与最先进的方法相比,CELLetter表现出卓越的L-R分类性能.
- 预测的L-R对在人类心脏和肺组织中显示出显著的空间相关性.
- 确定MIF-CD44作为HNSCC的一个关键信号轴,表明治疗潜力.
结论:
- CELLetter提供了一个强大的框架来识别L-R相互作用并推断CCC.
- 该框架增强了对细胞信号通路的理解,并确定了像HNSCC这样的疾病中的潜在治疗点.
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