Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration

Madhvi Menon, Shahin Mohammadi, Jose Davila-Velderrain, Brittany A. Goods, Tanina D. Cadwell, Yu Xing, Anat Stemmer-Rachamimov, Alex K. Shalek, John Christopher Love, Manolis Kellis and Brian P. Hafler ()
Additional contact information
Madhvi Menon: Broad Institute of MIT and Harvard
Shahin Mohammadi: Broad Institute of MIT and Harvard
Jose Davila-Velderrain: Broad Institute of MIT and Harvard
Brittany A. Goods: Broad Institute of MIT and Harvard
Tanina D. Cadwell: Evergrande Center for Immunologic Diseases, Harvard Medical School
Yu Xing: Evergrande Center for Immunologic Diseases, Harvard Medical School
Anat Stemmer-Rachamimov: Massachusetts General Hospital
Alex K. Shalek: Broad Institute of MIT and Harvard
John Christopher Love: Broad Institute of MIT and Harvard
Manolis Kellis: Broad Institute of MIT and Harvard
Brian P. Hafler: Broad Institute of MIT and Harvard

Nature Communications, 2019, vol. 10, issue 1, 1-9

Abstract: Abstract Genome-wide association studies (GWAS) have identified genetic variants associated with age-related macular degeneration (AMD), one of the leading causes of blindness in the elderly. However, it has been challenging to identify the cell types associated with AMD given the genetic complexity of the disease. Here we perform massively parallel single-cell RNA sequencing (scRNA-seq) of human retinas using two independent platforms, and report the first single-cell transcriptomic atlas of the human retina. Using a multi-resolution network-based analysis, we identify all major retinal cell types, and their corresponding gene expression signatures. Heterogeneity is observed within macroglia, suggesting that human retinal glia are more diverse than previously thought. Finally, GWAS-based enrichment analysis identifies glia, vascular cells, and cone photoreceptors to be associated with the risk of AMD. These data provide a detailed analysis of the human retina, and show how scRNA-seq can provide insight into cell types involved in complex, inflammatory genetic diseases.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.nature.com/articles/s41467-019-12780-8 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-12780-8

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-019-12780-8

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().