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Nat Biotechnol. 2015 May;33(5):503-9. doi: 10.1038/nbt.3209. Epub 2015 Apr 13.

High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin.

Author information

1
1] European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK. [2] Developmental Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
2
European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK.
3
1] European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK. [2] Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK.
4
Developmental Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
5
1] European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK. [2] Developmental Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany. [3] Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK.

Abstract

Understanding cell type identity in a multicellular organism requires the integration of gene expression profiles from individual cells with their spatial location in a particular tissue. Current technologies allow whole-transcriptome sequencing of spatially identified cells but lack the throughput needed to characterize complex tissues. Here we present a high-throughput method to identify the spatial origin of cells assayed by single-cell RNA-sequencing within a tissue of interest. Our approach is based on comparing complete, specificity-weighted mRNA profiles of a cell with positional gene expression profiles derived from a gene expression atlas. We show that this method allocates cells to precise locations in the brain of the marine annelid Platynereis dumerilii with a success rate of 81%. Our method is applicable to any system that has a reference gene expression database of sufficiently high resolution.

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PMID:
25867922
DOI:
10.1038/nbt.3209
[Indexed for MEDLINE]

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