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通过从转录组学数据推断活性联体来解开与NicheNet的细胞-细胞通信

Chananchida Sang-Aram^1,2,3, Robin Browaeys^2,4, Ruth Seurinck^1,2,3

¹Data Mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium.

Nature protocols

|March 4, 2025

概括

NicheNet v2提供了一种最佳实践工作流程,用于分析单细胞数据中的联体受体相互作用. 这种计算工具有助于优先考虑调节基因表达的信号通路,帮助细胞通信研究.

科学领域:

细胞和分子生物学细胞和分子生物学
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

体受体相互作用对于细胞间的通信和信号传递至关重要.
尼奇网是一种用于推断这些相互作用及其对基因表达的影响的计算工具.
本文详细介绍了基于广泛的经验和用户反的NicheNet优化工作流.

研究的目的:

介绍一个最佳实践工作流程,用于使用NicheNet来分析连接体-受体相互作用.
引入 NicheNet v2 以更新的数据源和改进的下游分析来优先考虑特定细胞类型的相互作用.
引导用户为各种实验设计选择合适的方法和参数.

主要方法:

描述来自单细胞表达矩阵的连接体分析的发送者不可知和发送者集中方法.
在NicheNet v2.2中实施更新的数据源和下游程序,以优先考虑特定细胞类型的联体受体对.
提供逐步代码描述和一行封装功能,以提高用户的访问性.

主要成果:

一个优先级列表的连接体及其目标基因,随附的可视化.
增强识别细胞类型特定的连接体-受体相互作用的能力.
精简的工作流程适用于各种实验设计,包括病例控制和细胞分化研究.

结论:

提出的工作流和NicheNet v2促进了对单细胞数据中的联体受体相互作用的可靠分析.
该工具和工作流程的设计旨在让具有不同计算专业知识的用户可以访问.
这项工作有助于研究人员了解复杂的生物系统中的细胞通信和基因调节.

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