Spatial reconstruction of single-cell gene expression data.

Nat Biotechnol
Authors
Keywords
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.

Year of Publication
2015
Journal
Nat Biotechnol
Volume
33
Issue
5
Pages
495-502
Date Published
2015 May
ISSN
1546-1696
URL
DOI
10.1038/nbt.3192
PubMed ID
25867923
PubMed Central ID
PMC4430369
Links
Grant list
F32 HD075541 / HD / NICHD NIH HHS / United States
Howard Hughes Medical Institute / United States
P50 HG006193 / HG / NHGRI NIH HHS / United States
R01 GM056211 / GM / NIGMS NIH HHS / United States
1P50HG006193 / HG / NHGRI NIH HHS / United States