Inferring spatial and signaling relationships between cells from single cell transcriptomic data
Inferring spatial and signaling relationships between cells from single cell transcriptomic data
复制标题
从单细胞转录组数据推断细胞之间的空间和信号传导关系
DOI：
10.1038/s41467-020-15968-5
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发表时间：
2020-04-29
影响因子：
16.6
通讯作者：
Nie, Qing
中科院分区：
文献类型：
--
作者：
Cang, Zixuan;Nie, Qing
单细胞 RNA 测序 (scRNA-seq) 提供单个细胞的详细信息；然而，重要的空间信息经常丢失。我们提出了 SpaOTsc，这是一种依靠结构化最优传输的方法，通过利用相对少量基因的空间测量来恢复 scRNA-seq 数据的空间特性。 scRNA-seq 数据中单个细胞的空间度量首先基于将其与空间测量连接的地图建立。然后通过将信号发送器“最佳地传输”到空间中的目标信号接收器来获得小区间通信。接下来，我们使用部分信息分解计算细胞间基因-基因信息流，以估计跨细胞基因之间的空间规则。采用四个数据集来交叉验证空间基因表达预测并与已知的细胞间通讯进行比较。 SpaOTsc 具有更广泛的应用，既可以将非空间单细胞测量与空间数据集成，也可以直接在空间单细胞转录组数据中重建组织中的空间细胞动力学。组织解离允许对单细胞进行高通量表达谱分析，但空间信息会丢失。在这里，作者应用一种不平衡和结构化的最佳传输方法，通过将 scRNA-seq 数据与空间成像数据集成来推断细胞之间的空间和信号关系。
Single-cell RNA sequencing (scRNA-seq) provides details for individual cells; however, crucial spatial information is often lost. We present SpaOTsc, a method relying on structured optimal transport to recover spatial properties of scRNA-seq data by utilizing spatial measurements of a relatively small number of genes. A spatial metric for individual cells in scRNA-seq data is first established based on a map connecting it with the spatial measurements. The cell-cell communications are then obtained by "optimally transporting" signal senders to target signal receivers in space. Using partial information decomposition, we next compute the intercellular gene-gene information flow to estimate the spatial regulations between genes across cells. Four datasets are employed for cross-validation of spatial gene expression prediction and comparison to known cell-cell communications. SpaOTsc has broader applications, both in integrating non-spatial single-cell measurements with spatial data, and directly in spatial single-cell transcriptomics data to reconstruct spatial cellular dynamics in tissues. Dissociation of tissues allows high-throughput expression profiling of single cells, but spatial information is lost. Here the authors apply an unbalanced and structured optimal transport method to infer spatial and signalling relationships between cells from scRNA-seq data by integrating it with spatial imaging data.