Spatial reconstruction of single-cell gene expression data.
IF: 68.164
Cited by: 3,473


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.


Spatial reconstruction
Spatial Gene Expression

MeSH terms

Gene Expression Regulation, Developmental
High-Throughput Nucleotide Sequencing
Image Processing, Computer-Assisted
In Situ Hybridization, Fluorescence
Single-Cell Analysis


Satija, Rahul
Farrell, Jeffrey A
Gennert, David
Schier, Alexander F
Regev, Aviv

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