Satija Rahul, Farrell Jeffrey A, Gennert David, Schier Alexander F, Regev Aviv
Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, USA.
Nat Biotechnol. 2015 May;33(5):495-502. doi: 10.1038/nbt.3192. Epub 2015 Apr 13.
Spatial localization is a key determinant of cellular fate and behavior, but methods for spatially resolved, transcriptome-wide gene expression profiling across complex tissues are lacking. RNA staining methods assay only a small number of transcripts, whereas single-cell RNA-seq, which measures global gene expression, separates 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 and generated a transcriptome-wide map of spatial patterning. We confirmed Seurat's accuracy using several experimental approaches, then used the strategy to identify a set of archetypal expression patterns and spatial markers. 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.
空间定位是细胞命运和行为的关键决定因素，但目前缺乏在复杂组织中进行空间分辨、全转录组范围基因表达谱分析的方法。RNA染色方法只能检测少量转录本，而测量全局基因表达的单细胞RNA测序则将细胞与其天然空间背景分离。在此，我们介绍Seurat，这是一种通过将单细胞RNA测序数据与原位RNA模式相结合来推断细胞定位的计算策略。我们应用Seurat对来自解离的斑马鱼（Danio rerio）胚胎的851个单细胞进行空间定位，并生成了全转录组范围的空间模式图谱。我们使用几种实验方法证实了Seurat的准确性，然后利用该策略识别了一组原型表达模式和空间标记。Seurat能够正确定位罕见亚群，准确绘制空间受限和分散群体的图谱。Seurat将适用于绘制不同系统中复杂模式组织内的细胞定位。
