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...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
13 April 2015
|
| In: |
Nature biotechnology
Year: 2015, Jahrgang: 33, Heft: 5, Pages: 503-509 |
| ISSN: | 1546-1696 |
| DOI: | 10.1038/nbt.3209 |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org/10.1038/nbt.3209 Verlag, Volltext: https://www.nature.com/nbt/journal/v33/n5/full/nbt.3209.html 
|
| Verfasserangaben: | Kaia Achim, Jean-Baptiste Pettit, Luis R. Saraiva, Daria Gavriouchkina, Tomas Larsson, Detlev Arendt & John C. Marioni |
| Zusammenfassung: | 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. |
|---|---|
| Beschreibung: | Gesehen am 09.06.2017 |
| Beschreibung: | Online Resource |
| ISSN: | 1546-1696 |
| DOI: | 10.1038/nbt.3209 |