Javaid Azka, Frost H Robert
Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, USA.
Complex Netw Appl XIII (2024). 2025;1189:394-405. doi: 10.1007/978-3-031-82435-7_32. Epub 2025 Mar 28.
We describe a network analysis-based cell-cell communication method for Spatial Transcriptomics (ST) data. For each evaluated ligand-receptor interaction, we define a fully connected, directed and weighted network model where nodes represent the individual ST locations with directed edge weights set to the product of the reduced rank reconstructed expression values for the ligand at the source location and cognate receptor at the target location divided by the squared distance between the locations. Using this network, we compute the weighted in-degree centrality to quantify signaling activity of the target ligand-receptor interaction at each location. Our method is validated for three interactions on a real ST dataset against five different cell-cell communication strategies. We report that our method captures the simultaneous expression heterogeneity in both the ligand and the receptor and generates biologically plausible cell communication profiles for the Wnt3-Fzd1, Ephb1-Efnb3 and Ptprc-Cd22 interactions. An important finding of this work is the importance of building network models for ST data using a low dimensional embedding of the gene-level data.
我们描述了一种基于网络分析的空间转录组学（ST）数据细胞间通信方法。对于每个评估的配体-受体相互作用，我们定义了一个完全连通、有向且加权的网络模型，其中节点代表各个ST位置，有向边权重设置为源位置处配体的降秩重构表达值与目标位置处同源受体的降秩重构表达值之积除以位置之间的平方距离。利用这个网络，我们计算加权入度中心性，以量化每个位置上目标配体-受体相互作用的信号传导活性。我们的方法针对真实ST数据集上的三种相互作用，与五种不同的细胞间通信策略进行了验证。我们报告称，我们的方法捕捉了配体和受体中的同时表达异质性，并为Wnt3-Fzd1、Ephb1-Efnb3和Ptprc-Cd22相互作用生成了生物学上合理的细胞通信图谱。这项工作的一个重要发现是，利用基因水平数据的低维嵌入为ST数据构建网络模型的重要性。
