Spatial reconstruction of single-cell gene expression

Spatial localization is a key determinant of cellular fate and behavior, but spatial RNA assays traditionally rely on staining for a limited number of RNA species. In contrast, single-cell RNA-seq allows for deep profiling of cellular gene expression, but established methods separate cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos, inferring a transcriptome-wide map of spatial patterning. We confirmed Seurat’s accuracy using several experimental approaches, and used it to identify a set of archetypal expression patterns and spatial markers. Additionally, Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems. Overall design: We generated single-cell RNA-seq profiles from dissociated cells from developing zebrafish embryos (late blastula stage - 50% epiboly)

空间定位是调控细胞命运与行为的核心决定因素，但传统空间RNA检测技术通常仅能针对有限种类的RNA分子开展染色分析。与之相对，单细胞RNA测序（single-cell RNA-seq）能够实现细胞基因表达的深度表达谱分析，但现有成熟技术方法会将细胞脱离其原生的空间环境。
本研究报道了Seurat——一种通过整合单细胞RNA测序数据与原位RNA（in situ RNA）表达模式来推断细胞空间定位的计算策略。我们将Seurat应用于解离后的斑马鱼（Danio rerio）胚胎的851个单细胞的空间定位绘图，由此构建了覆盖全转录组的空间模式图谱。
我们通过多种实验手段验证了Seurat的准确性，并利用该工具识别出一系列典型基因表达模式与空间标记物。此外，Seurat能够准确识别稀有细胞亚群的空间定位，精准绘制出空间受限与弥散分布的两类细胞群。
Seurat可适用于多种不同研究体系中复杂模式组织内的细胞空间定位研究。
整体实验设计：我们从发育至晚囊胚期——50%外包期的斑马鱼胚胎解离细胞中，构建了单细胞RNA测序表达谱。