Department of Mathematics and the NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA, 92697, USA.
Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, CA, 92697, USA.
Nat Commun. 2023 Feb 28;14(1):1128. doi: 10.1038/s41467-023-36800-w.
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.
神经通讯网络构成了大脑功能的基本基础。这些通讯网络是通过发射配体（如神经递质）来实现的，这些配体激活受体复合物以促进通讯。因此，神经通讯从根本上依赖于转录组。在这里，我们开发了 NeuronChat，这是一种使用单细胞表达数据推断、可视化和分析预定义细胞群之间神经特异性通讯网络的方法和包。我们整合了一个针对人类和小鼠的神经信号分子相互作用的手动策管分子数据库，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，以说明其识别神经内通讯网络的功能，揭示不同生物背景下保守或特定于背景的相互作用，并预测自闭症谱系障碍等疾病大脑中的通讯模式变化。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的细胞间通讯。
