Department of Mathematics, University of California, Irvine, Irvine, CA, USA.
NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, USA.
Nat Methods. 2024 Oct;21(10):1806-1817. doi: 10.1038/s41592-024-02380-w. Epub 2024 Aug 26.
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.
从单细胞 RNA 测序 (scRNA-seq) 和空间转录组学 (ST)，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的基因模块来描述。这两种信息流的描述通常被假设是独立发生的。然而，细胞间通讯驱动了受细胞内基因模块介导的定向信息流，进而引发其他信号的外流。描述这种细胞间流的方法学还很缺乏。我们提出了 FlowSig，这是一种使用图形因果建模和条件独立性从 scRNA-seq 或 ST 数据推断通讯驱动的细胞间流的方法。我们使用新生成的皮质类器官实验数据和数学建模生成的合成数据来对 FlowSig 进行基准测试。我们通过将其应用于各种研究来展示 FlowSig 的实用性，表明 FlowSig 可以捕获胰腺胰岛中刺激诱导的旁分泌信号变化，展示由于 COVID-19 严重程度增加而导致的细胞间流的变化，并在小鼠胚胎发生中重建形态发生素驱动的激活抑制剂模式。
