Spatial reconstruction of single-cell gene expression data
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Regev_Spatial reconstruction.pdf
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Author(s)
Satija, Rahul
Farrell, Jeffrey A
Gennert, David
Schier, Alexander F
Regev, Aviv
Date Issued
April 2015
Journal
Nature Biotechnology
Publisher
Nature Publishing Group
Citation
Satija, Rahul et al. “Spatial Reconstruction of Single-Cell Gene Expression Data.” Nature Biotechnology 33.5 (2015): 495–502.
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Biology
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1038/nbt.3192
