High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin.
Kaia AchimJean-Baptiste PettitLuís R SaraivaDaria GavriouchkinaTomas LarssonDetlev ArendtJohn C MarioniPublished in: Nature biotechnology (2015)
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.
Keyphrases
- single cell
- rna seq
- high throughput
- gene expression
- induced apoptosis
- cell cycle arrest
- high resolution
- computed tomography
- emergency department
- magnetic resonance
- stem cells
- dna methylation
- signaling pathway
- cell death
- mass spectrometry
- machine learning
- multiple sclerosis
- resting state
- brain injury
- magnetic resonance imaging
- cell proliferation
- big data
- functional connectivity
- artificial intelligence
- blood brain barrier
- data analysis
- adverse drug
- genome wide analysis