Spatial reconstruction of single-cell gene expression data.

IF: 68.164

Cited by: 3,473

Abstract

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.

Keywords

Spatial reconstruction

Spatial Gene Expression

Seurat

MeSH terms

Animals

Gene Expression Regulation, Developmental

High-Throughput Nucleotide Sequencing

Image Processing, Computer-Assisted

In Situ Hybridization, Fluorescence

Single-Cell Analysis

Transcriptome

Zebrafish

Authors

Satija, Rahul

Farrell, Jeffrey A

Gennert, David

Schier, Alexander F

Regev, Aviv

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