Zhang Tianjiao, Wu Zhenao, Li Liangyu, Ren Jixiang, Zhang Ziheng, Zhang Jingyu, Wang Guohua
College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.
Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150040, China.
Biomolecules. 2025 Feb 27;15(3):342. doi: 10.3390/biom15030342.
The growth, development, and differentiation of multicellular organisms are primarily driven by intercellular communication, which coordinates the activities of diverse cell types. This cell-to-cell signaling is typically mediated by various types of protein-protein interactions, including ligand-receptor; receptor-receptor, and extracellular matrix-receptor interactions. Currently, computational methods for inferring ligand-receptor communication primarily depend on gene expression data of ligand-receptor pairs and spatial information of cells. Some approaches integrate protein complexes; transcription factors; or pathway information to construct cell communication networks. However, few methods consider the critical role of protein-protein interactions (PPIs) in intercellular communication networks, especially when predicting communication between different cell types in the absence of cell type information. These methods often rely on ligand-receptor pairs that lack PPI evidence, potentially compromising the accuracy of their predictions. To address this issue, we propose CellGAT, a framework that infers intercellular communication by integrating gene expression data of ligand-receptor pairs, PPI information, protein complex data, and experimentally validated pathway information. CellGAT not only builds a priori models but also uses node embedding algorithms and graph attention networks to build cell communication networks based on scRNA-seq (single-cell RNA sequencing) datasets and includes a built-in cell clustering algorithm. Through comparisons with various methods, CellGAT accurately predicts cell-cell communication (CCC) and analyzes its impact on downstream pathways; neighboring cells; and drug interventions.
多细胞生物的生长、发育和分化主要由细胞间通讯驱动，这种通讯协调了不同细胞类型的活动。这种细胞间信号传导通常由各种类型的蛋白质-蛋白质相互作用介导，包括配体-受体、受体-受体和细胞外基质-受体相互作用。目前，推断配体-受体通讯的计算方法主要依赖于配体-受体对的基因表达数据和细胞的空间信息。一些方法整合蛋白质复合物、转录因子或通路信息来构建细胞通讯网络。然而，很少有方法考虑蛋白质-蛋白质相互作用（PPI）在细胞间通讯网络中的关键作用，特别是在缺乏细胞类型信息的情况下预测不同细胞类型之间的通讯时。这些方法通常依赖于缺乏PPI证据的配体-受体对，这可能会影响其预测的准确性。为了解决这个问题，我们提出了CellGAT，这是一个通过整合配体-受体对的基因表达数据、PPI信息、蛋白质复合物数据和经过实验验证的通路信息来推断细胞间通讯的框架。CellGAT不仅构建先验模型，还使用节点嵌入算法和图注意力网络基于scRNA-seq（单细胞RNA测序）数据集构建细胞通讯网络，并包括一个内置的细胞聚类算法。通过与各种方法的比较，CellGAT能够准确预测细胞-细胞通讯（CCC）并分析其对下游通路、邻近细胞和药物干预的影响。
