Stanford Immunology Program, Stanford University School of Medicine, Stanford, CA, USA.
Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Nat Biotechnol. 2024 Mar;42(3):470-483. doi: 10.1038/s41587-023-01782-z. Epub 2023 May 11.
Inference of cell-cell communication from single-cell RNA sequencing data is a powerful technique to uncover intercellular communication pathways, yet existing methods perform this analysis at the level of the cell type or cluster, discarding single-cell-level information. Here we present Scriabin, a flexible and scalable framework for comparative analysis of cell-cell communication at single-cell resolution that is performed without cell aggregation or downsampling. We use multiple published atlas-scale datasets, genetic perturbation screens and direct experimental validation to show that Scriabin accurately recovers expected cell-cell communication edges and identifies communication networks that can be obscured by agglomerative methods. Additionally, we use spatial transcriptomic data to show that Scriabin can uncover spatial features of interaction from dissociated data alone. Finally, we demonstrate applications to longitudinal datasets to follow communication pathways operating between timepoints. Our approach represents a broadly applicable strategy to reveal the full structure of niche-phenotype relationships in health and disease.
从单细胞 RNA 测序数据推断细胞间通讯是揭示细胞间通讯途径的强大技术，但现有的方法在细胞类型或簇的水平上进行此分析，丢弃了单细胞水平的信息。在这里，我们提出了 Scriabin，这是一种灵活且可扩展的框架，用于在单细胞分辨率下进行细胞间通讯的比较分析，而无需细胞聚集或下采样。我们使用多个已发表的图谱规模数据集、遗传扰动筛选和直接实验验证表明，Scriabin 可以准确地恢复预期的细胞间通讯边缘，并识别出可能被聚集方法掩盖的通讯网络。此外，我们使用空间转录组学数据表明，Scriabin 可以仅从分离的数据中揭示相互作用的空间特征。最后，我们展示了对纵向数据集的应用，以跟踪在时间点之间运行的通讯途径。我们的方法代表了一种广泛适用的策略，可以揭示健康和疾病中生态位-表型关系的完整结构。
