Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration
Name
s41467-019-12780-8.pdf
Description
Published version
Size
3.77 MB
Format
Adobe PDF
Checksum (MD5)
b240d58047b12aa3061949fe92cb0b1e
Author(s)
Mohammadi, Shahin
Davila Velderrain, Jose
Goods, Brittany A.
Shalek, Alexander K
Love, Christopher J.
Kellis, Manolis
Date Issued
October 2019
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Citation
Menon, Madhvi et al. “Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration.” Nature Communications 10 (2019): 4902 © 2019 The Author(s)
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Department of Chemistry
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/s41467-019-12780-8
