Sang-Aram Chananchida, Browaeys Robin, Seurinck Ruth, Saeys Yvan
Data Mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium.
Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
Nat Protoc. 2025 Mar 4. doi: 10.1038/s41596-024-01121-9.
Ligand-receptor interactions constitute a fundamental mechanism of cell-cell communication and signaling. NicheNet is a well-established computational tool that infers ligand-receptor interactions that potentially regulate gene expression changes in receiver cell populations. Whereas the original publication delves into the algorithm and validation, this paper describes a best practices workflow cultivated over four years of experience and user feedback. Starting from the input single-cell expression matrix, we describe a 'sender-agnostic' approach that considers ligands from the entire microenvironment and a 'sender-focused' approach that considers ligands only from cell populations of interest. As output, users will obtain a list of prioritized ligands and their potential target genes, along with multiple visualizations. We include further developments made in NicheNet v2, in which we have updated the data sources and implemented a downstream procedure for prioritizing cell type-specific ligand-receptor pairs. Although a standard NicheNet analysis takes <10 min to run, users often invest additional time in making decisions about the approach and parameters that best suit their biological question. This paper serves to aid in this decision-making process by describing the most appropriate workflow for common experimental designs like case-control and cell-differentiation studies. Finally, in addition to the step-by-step description of the code, we also provide wrapper functions that enable the analysis to be run in one line of code, thus tailoring the workflow to users at all levels of computational proficiency.
配体-受体相互作用构成了细胞间通讯和信号传导的基本机制。NicheNet是一个成熟的计算工具，可推断可能调节受体细胞群体中基因表达变化的配体-受体相互作用。虽然原始出版物深入探讨了算法和验证，但本文描述了基于四年经验和用户反馈形成的最佳实践工作流程。从输入的单细胞表达矩阵开始，我们描述了一种“不考虑发送细胞”的方法，该方法考虑来自整个微环境的配体，以及一种“关注发送细胞”的方法，该方法仅考虑来自感兴趣细胞群体的配体。作为输出，用户将获得一份优先排序的配体及其潜在靶基因的列表，以及多种可视化结果。我们还介绍了NicheNet v2的进一步发展，其中我们更新了数据源，并实施了一种下游程序来对细胞类型特异性配体-受体对进行优先排序。虽然标准的NicheNet分析运行时间不到10分钟，但用户通常会投入额外的时间来决定最适合其生物学问题的方法和参数。本文旨在通过描述针对病例对照和细胞分化研究等常见实验设计的最合适工作流程，来辅助这一决策过程。最后，除了对代码的逐步描述外，我们还提供了包装函数，使分析能够在一行代码中运行，从而使工作流程适合所有计算水平的用户。
