从单细胞和空间转录组学推断模式驱动的细胞间流动。
Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics.
发表日期：2024 Aug 26
作者：
Axel A Almet, Yuan-Chen Tsai, Momoko Watanabe, Qing Nie
来源：
NATURE METHODS
摘要：
从单细胞 RNA 测序 (scRNA-seq) 和空间转录组学 (ST) 中，人们可以提取高维基因表达模式，这些模式可以通过细胞间通讯网络或解耦基因模块进行描述。信息流的这两种描述通常被认为是独立发生的。然而，细胞间通讯驱动由细胞内基因模块介导的定向信息流，进而触发其他信号的流出。缺乏描述这种细胞间流动的方法。我们提出了 FlowSig，一种使用图形因果模型和条件独立性从 scRNA-seq 或 ST 数据推断通信驱动的细胞间流动的方法。我们使用新生成的实验皮质类器官数据和数学建模生成的合成数据对 FlowSig 进行基准测试。我们通过将 FlowSig 应用于各种研究来展示 FlowSig 的实用性，表明 FlowSig 可以捕获刺激诱导的胰岛旁分泌信号传导变化，证明由于 COVID-19 严重程度增加而导致的细胞间流动变化，并重建小鼠中形态发生素驱动的激活剂-抑制剂模式胚胎发生。© 2024。作者。
From single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST), one can extract high-dimensional gene expression patterns that can be described by intercellular communication networks or decoupled gene modules. These two descriptions of information flow are often assumed to occur independently. However, intercellular communication drives directed flows of information that are mediated by intracellular gene modules, in turn triggering outflows of other signals. Methodologies to describe such intercellular flows are lacking. We present FlowSig, a method that infers communication-driven intercellular flows from scRNA-seq or ST data using graphical causal modeling and conditional independence. We benchmark FlowSig using newly generated experimental cortical organoid data and synthetic data generated from mathematical modeling. We demonstrate FlowSig's utility by applying it to various studies, showing that FlowSig can capture stimulation-induced changes to paracrine signaling in pancreatic islets, demonstrate shifts in intercellular flows due to increasing COVID-19 severity and reconstruct morphogen-driven activator-inhibitor patterns in mouse embryogenesis.© 2024. The Author(s).