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Decoding functional cell-cell communication events by multi-view graph learning on spatial transcriptomics
bioRxiv - Bioinformatics Pub Date : 2023-02-23 , DOI: 10.1101/2022.06.22.496105
Haochen Li , Tianxing Ma , Minsheng Hao , Lei Wei , Xuegong Zhang

Cell-cell communication events (CEs) are mediated by multiple ligand-receptor pairs. Usually only a particular subset of CEs directly works for a specific downstream response in a particular microenvironment. We name them as functional communication events (FCEs) of the target responses. Decoding the FCE-target gene relations is important for understanding the machanisms of many biological processes, but has been intractable due to the mixing of multiple factors and the lack of direct observations. We developed a method HoloNet for decoding FCEs using spatial transcriptomic data by integrating ligand-receptor pairs, cell-type spatial distribution and downstream gene expression into a deep learning model. We modeled CEs as a multi-view network, developed an attention-based graph learning method to train the model for generating target gene expression with the CE networks, and decoded the FCEs for specific downstream genes by interpreting the trained model. We applied HoloNet on three Visium datasets of breast cancer or liver cancer. It revealed the communication landscapes in tumor microenvironments, and uncovered how various ligand-receptor signals and cell types affect specific biological processes. We also validated the stability of HoloNet in a Slideseq-v2 dataset. The experiments showed that HoloNet is a powerful tool on spatial transcriptomic data to help revealing specific cell-cell communications in a microenvironment that shape cellular phenotypes.

中文翻译:

通过空间转录组学的多视图图学习解码功能性细胞间通讯事件

细胞间通讯事件 (CE) 由多个配体-受体对介导。通常只有特定的 CE 子集直接用于特定微环境中的特定下游响应。我们将它们命名为目标响应的功能通信事件 (FCE)。解码 FCE-目标基因关系对于理解许多生物过程的机制很重要,但由于多种因素的混合和缺乏直接观察而变得棘手。我们通过将配体-受体对、细胞类型空间分布和下游基因表达整合到深度学习模型中,开发了一种使用空间转录组数据解码 FCE 的 HoloNet 方法。我们将 CE 建模为多视图网络,开发了一种基于注意力的图学习方法来训练使用 CE 网络生成目标基因表达的模型,并通过解释训练模型来解码特定下游基因的 FCE。我们将 HoloNet 应用于乳腺癌或肝癌的三个 Visium 数据集。它揭示了肿瘤微环境中的通讯景观,并揭示了各种配体-受体信号和细胞类型如何影响特定的生物过程。我们还在 Slideseq-v2 数据集中验证了 HoloNet 的稳定性。实验表明,HoloNet 是空间转录组数据的强大工具,有助于揭示微环境中特定的细胞间通讯,从而塑造细胞表型。我们将 HoloNet 应用于乳腺癌或肝癌的三个 Visium 数据集。它揭示了肿瘤微环境中的通讯景观,并揭示了各种配体-受体信号和细胞类型如何影响特定的生物过程。我们还在 Slideseq-v2 数据集中验证了 HoloNet 的稳定性。实验表明,HoloNet 是空间转录组数据的强大工具,有助于揭示微环境中特定的细胞间通讯,从而塑造细胞表型。我们将 HoloNet 应用于乳腺癌或肝癌的三个 Visium 数据集。它揭示了肿瘤微环境中的通讯景观,并揭示了各种配体-受体信号和细胞类型如何影响特定的生物过程。我们还在 Slideseq-v2 数据集中验证了 HoloNet 的稳定性。实验表明,HoloNet 是空间转录组数据的强大工具,有助于揭示微环境中特定的细胞间通讯,从而塑造细胞表型。
更新日期:2023-02-23
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