Institute of Medical Informatics, University of Münster, Münster, Germany.
Department of Pediatric Hematology and Oncology, University Children's Hospital Münster, Münster, Germany.
Commun Biol. 2022 Jan 11;5(1):21. doi: 10.1038/s42003-021-02986-2.
Deciphering cell-cell communication is a key step in understanding the physiology and pathology of multicellular systems. Recent advances in single-cell transcriptomics have contributed to unraveling the cellular composition of tissues and enabled the development of computational algorithms to predict cellular communication mediated by ligand-receptor interactions. Despite the existence of various tools capable of inferring cell-cell interactions from single-cell RNA sequencing data, the analysis and interpretation of the biological signals often require deep computational expertize. Here we present InterCellar, an interactive platform empowering lab-scientists to analyze and explore predicted cell-cell communication without requiring programming skills. InterCellar guides the biological interpretation through customized analysis steps, multiple visualization options, and the possibility to link biological pathways to ligand-receptor interactions. Alongside convenient data exploration features, InterCellar implements data-driven analyses including the possibility to compare cell-cell communication from multiple conditions. By analyzing COVID-19 and melanoma cell-cell interactions, we show that InterCellar resolves data-driven patterns of communication and highlights molecular signals through the integration of biological functions and pathways. We believe our user-friendly, interactive platform will help streamline the analysis of cell-cell communication and facilitate hypothesis generation in diverse biological systems.
解析细胞间通讯是理解多细胞系统生理和病理的关键步骤。单细胞转录组学的最新进展有助于揭示组织的细胞组成，并开发了计算算法来预测由配体-受体相互作用介导的细胞通讯。尽管存在各种能够从单细胞 RNA 测序数据中推断细胞间相互作用的工具，但对生物信号的分析和解释通常需要深入的计算专业知识。在这里，我们展示了 InterCellar，这是一个交互式平台，使实验室科学家能够在不需要编程技能的情况下分析和探索预测的细胞间通讯。InterCellar 通过定制的分析步骤、多种可视化选项以及将生物途径与配体-受体相互作用联系起来的可能性来指导生物学解释。除了方便的数据探索功能外，InterCellar 还实现了数据驱动的分析，包括比较来自多种条件的细胞间通讯的可能性。通过分析 COVID-19 和黑色素瘤细胞间的相互作用，我们表明 InterCellar 解决了数据驱动的通讯模式，并通过整合生物学功能和途径突出了分子信号。我们相信，我们用户友好、交互式的平台将有助于简化细胞间通讯的分析，并促进在不同生物系统中产生假设。
