美国加州大学尔湾分校Qing Nie研究组实现从单细胞和空间转录组学推断驱动模式的细胞间流动。相关论文于2024年8月26日在线发表在《自然—方法学》杂志上。

研究人员提出了FlowSig，这是一种通过图形因果建模和条件独立性从单细胞RNA测序（scRNA-seq）或空间转录组学（ST）数据中推断通信驱动的细胞间流动的方法。研究人员通过新生成的实验性皮层类器官数据和数学建模生成的合成数据对FlowSig进行了基准测试。

研究人员通过将FlowSig应用于各种研究，展示了其实用性，表明FlowSig可以捕捉刺激诱导的胰岛旁分泌信号变化、展示COVID-19严重程度增加导致的细胞间流动转变，并重建小鼠胚胎发生过程中由形态发生素驱动的激活-抑制模式。

据悉，通过scRNA-seq和ST可以提取高维度的基因表达模式，这些模式可以通过细胞间通信网络或独立的基因模块来描述。这两种信息流的描述通常被认为是独立发生的。然而，细胞间通信驱动的信息流是通过细胞内基因模块介导的，并进一步触发其他信号的外流。目前缺乏描述这种细胞间流动的方法。

附：英文原文

Title: Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics

Author: Almet, Axel A., Tsai, Yuan-Chen, Watanabe, Momoko, Nie, Qing

Issue&Volume: 2024-08-26

Abstract: 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.

DOI: 10.1038/s41592-024-02380-w

Source: https://www.nature.com/articles/s41592-024-02380-w