使用CrossChat检测细胞间通信中的全球和本地层次结构.
Xinyi Wang1, Axel A Almet2,3, Qing Nie4,5,6
1Department of Mathematics, University of California, Irvine, CA, USA.
Nature communications
|December 3, 2024
概括
本研究介绍了CrossChat,这是一个用于分析层次细胞-细胞通信 (CCC) 结构的计算框架. 交叉聊天揭示了全球和本地CCC层次结构,为复杂的组织功能提供了新的见解.
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-细胞通信 (CCC) 在生物尺度上至关重要,形成复杂的层次结构.
- 从单细胞RNA测序 (scRNA-seq) 和空间转录组学推断CCC的现有方法忽略了这些固有的层次结构.
- 了解层次的CCC对于破译复杂的组织功能至关重要.
研究的目的:
- 开发CrossChat,这是一个新的计算框架,用于推断和分析层次性的细胞-细胞通信结构.
- 为了解CCC内部的全球和地方层次关系提供全面的方法.
- 为了证明CrossChat在各种生物数据集中的实用性.
主要方法:
- 交叉聊天采用两种互补的方法:全球层次结构的多分辨率聚类和地方层次结构的树检测.
- 该框架分析信号分子,特别是连接体和受体,以定义层次结构.
- 应用于来自人类和小鼠样本的非空间scRNA-seq和空间转录组学数据集.
主要成果:
- 交叉聊天成功地推断和可视化全球和本地层次的CCC结构.
- 该框架在发送器和接收器细胞组内确定了不同的信号属性.
- 展示了CrossChat在分析COVID-19患者数据,小鼠胚胎皮肤和受伤小鼠皮肤中的CCC等级的能力.
结论:
- 交叉聊天提供了一个强大的新工具,用于剖析细胞-细胞通信层次的复杂性.
- 该框架增强了我们对等级CCC如何支配组织功能的理解.
- 跨多个数据集的CrossChat应用验证了其在生物研究中的多功能性和有效性.
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