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

K Achim, JB Pettit, LR Saraiva, D Gavriouchkina… - Nature …, 2015 - nature.com
K Achim, JB Pettit, LR Saraiva, D Gavriouchkina, T Larsson, D Arendt, JC Marioni
Nature biotechnology, 2015nature.com
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 …
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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