Zhao Wei, Johnston Kevin G, Ren Honglei, Xu Xiangmin, Nie Qing
Department of Mathematics and the NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA 92697.
Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, CA 92697.
bioRxiv. 2023 Jan 16:2023.01.12.523826. doi: 10.1101/2023.01.12.523826.
Neural communication networks form the fundamental basis for brain function. These communication networks are enabled by emitted ligands such as neurotransmitters, which activate receptor complexes to facilitate communication. Thus, neural communication is fundamentally dependent on the transcriptome. Here we develop NeuronChat, a method and package for the inference, visualization and analysis of neural-specific communication networks among pre-defined cell groups using single-cell expression data. We incorporate a manually curated molecular interaction database of neural signaling for both human and mouse, and benchmark NeuronChat on several published datasets to validate its ability in predicting neural connectivity. Then, we apply NeuronChat to three different neural tissue datasets to illustrate its functionalities in identifying interneural communication networks, revealing conserved or context-specific interactions across different biological contexts, and predicting communication pattern changes in diseased brains with autism spectrum disorder. Finally, we demonstrate NeuronChat can utilize spatial transcriptomics data to infer and visualize neural-specific cell-cell communication.
神经通信网络构成了大脑功能的基本基础。这些通信网络由诸如神经递质等释放的配体实现，神经递质激活受体复合物以促进通信。因此，神经通信从根本上依赖于转录组。在这里，我们开发了NeuronChat，这是一种方法和软件包，用于使用单细胞表达数据在预定义的细胞组之间推断、可视化和分析神经特异性通信网络。我们纳入了一个经过人工策划的人类和小鼠神经信号分子相互作用数据库，并在几个已发表的数据集上对NeuronChat进行基准测试，以验证其预测神经连接性的能力。然后，我们将NeuronChat应用于三个不同的神经组织数据集，以说明其在识别神经间通信网络、揭示不同生物学背景下保守或特定背景的相互作用以及预测自闭症谱系障碍患病大脑中通信模式变化方面的功能。最后，我们证明NeuronChat可以利用空间转录组学数据来推断和可视化神经特异性细胞间通信。
