Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, 310006, Hangzhou, China.
Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China.
Nat Commun. 2022 Jul 30;13(1):4429. doi: 10.1038/s41467-022-32111-8.
Spatially resolved transcriptomics provides genetic information in space toward elucidation of the spatial architecture in intact organs and the spatially resolved cell-cell communications mediating tissue homeostasis, development, and disease. To facilitate inference of spatially resolved cell-cell communications, we here present SpaTalk, which relies on a graph network and knowledge graph to model and score the ligand-receptor-target signaling network between spatially proximal cells by dissecting cell-type composition through a non-negative linear model and spatial mapping between single-cell transcriptomic and spatially resolved transcriptomic data. The benchmarked performance of SpaTalk on public single-cell spatial transcriptomic datasets is superior to that of existing inference methods. Then we apply SpaTalk to STARmap, Slide-seq, and 10X Visium data, revealing the in-depth communicative mechanisms underlying normal and disease tissues with spatial structure. SpaTalk can uncover spatially resolved cell-cell communications for single-cell and spot-based spatially resolved transcriptomic data universally, providing valuable insights into spatial inter-cellular tissue dynamics.
空间分辨转录组学提供了空间遗传信息，有助于阐明完整器官中的空间结构，以及介导组织稳态、发育和疾病的空间分辨细胞间通讯。为了便于推断空间分辨细胞间通讯，我们在此提出 SpaTalk，它依赖于图网络和知识图，通过非负线性模型和单细胞转录组学与空间分辨转录组学数据之间的空间映射来剖析细胞类型组成，从而对空间上接近的细胞之间的配体-受体-靶标信号网络进行建模和评分。SpaTalk 在公共单细胞空间转录组学数据集上的基准性能优于现有推断方法。然后，我们将 SpaTalk 应用于 STARmap、Slide-seq 和 10X Visium 数据，揭示了具有空间结构的正常和疾病组织的深层次通讯机制。SpaTalk 可以普遍揭示单细胞和基于点的空间分辨转录组学数据的空间分辨细胞间通讯，为空间细胞间组织动力学提供有价值的见解。
