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

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...

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Bibliographic Details
Main Authors: Achim, Kaia (Author) , Larsson, Tomas (Author) , Arendt, Detlev (Author)
Format: Article (Journal)
Language:English
Published: 13 April 2015
In: Nature biotechnology
Year: 2015, Volume: 33, Issue: 5, Pages: 503-509
ISSN:1546-1696
DOI:10.1038/nbt.3209
Online Access:Verlag, Volltext: http://dx.doi.org/10.1038/nbt.3209
Verlag, Volltext: https://www.nature.com/nbt/journal/v33/n5/full/nbt.3209.html
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Author Notes:Kaia Achim, Jean-Baptiste Pettit, Luis R. Saraiva, Daria Gavriouchkina, Tomas Larsson, Detlev Arendt & John C. Marioni

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520 |a 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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