Hou Jiawen, Zhao Wei, Nie Qing
Department of Mathematics, University of California Irvine, 92697 Irvine, CA, USA.
The NSF-Simons Center for Multiscale Cell Fate Research, University of California Irvine, 92697 Irvine, CA, USA.
bioRxiv. 2025 Jun 3:2025.05.31.657197. doi: 10.1101/2025.05.31.657197.
During cell-cell communication (CCC), pathways activated by different ligand-receptor pairs may have crosstalk with each other. While multiple methods have been developed to infer CCC networks and their downstream response using single-cell RNA-seq data (scRNA-seq), the potential crosstalk between pathways connecting CCC with its downstream targets has been ignored. Here we introduce a machine learning-based method SigXTalk to analyze the crosstalk using scRNA-seq data by quantifying signal fidelity and specificity, two critical quantities measuring the effect of crosstalk. Specifically, a hypergraph learning method is used to encode the higher-order relations among receptors, transcription factors and target genes within regulatory pathways. Benchmarking of SigXTalk using simulation data shows the effectiveness, robustness, and accuracy in identifying key shared molecules among crosstalk pathways and their roles in transferring shared CCC information. Analysis of disease data shows SigXTalk's capability in identifying crucial signals, targets, regulatory networks, and CCC patterns that distinguish different disease conditions. Applications to the data with multiple time points reveals SigXTalk's capability in tracking the evolution of crosstalk pathways over time. Together our studies provide a systematic analysis of CCC-induced regulatory networks from the perspective of crosstalk between pathways.
在细胞间通讯（CCC）过程中，由不同配体-受体对激活的信号通路可能会相互发生串扰。虽然已经开发了多种方法，利用单细胞RNA测序数据（scRNA-seq）来推断CCC网络及其下游反应，但连接CCC与其下游靶点的信号通路之间的潜在串扰却一直被忽视。在此，我们引入一种基于机器学习的方法SigXTalk，通过量化信号保真度和特异性这两个衡量串扰效应的关键指标，利用scRNA-seq数据来分析串扰。具体而言，采用超图学习方法对调控通路中受体、转录因子和靶基因之间的高阶关系进行编码。使用模拟数据对SigXTalk进行基准测试，结果表明该方法在识别串扰通路之间的关键共享分子及其在传递共享CCC信息中的作用方面具有有效性、稳健性和准确性。对疾病数据的分析表明，SigXTalk能够识别区分不同疾病状态的关键信号、靶点、调控网络和CCC模式。将其应用于多个时间点的数据，揭示了SigXTalk跟踪串扰通路随时间演变的能力。我们的研究共同从通路间串扰的角度，对CCC诱导的调控网络进行了系统分析。
