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

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 d...

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Autores principales: Menon, Madhvi, Mohammadi, Shahin, Davila-Velderrain, Jose, Goods, Brittany A., Cadwell, Tanina D., Xing, Yu, Stemmer-Rachamimov, Anat, Shalek, Alex K., Love, John Christopher, Kellis, Manolis, Hafler, Brian P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6814749/
https://www.ncbi.nlm.nih.gov/pubmed/31653841
http://dx.doi.org/10.1038/s41467-019-12780-8
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author Menon, Madhvi
Mohammadi, Shahin
Davila-Velderrain, Jose
Goods, Brittany A.
Cadwell, Tanina D.
Xing, Yu
Stemmer-Rachamimov, Anat
Shalek, Alex K.
Love, John Christopher
Kellis, Manolis
Hafler, Brian P.
author_facet Menon, Madhvi
Mohammadi, Shahin
Davila-Velderrain, Jose
Goods, Brittany A.
Cadwell, Tanina D.
Xing, Yu
Stemmer-Rachamimov, Anat
Shalek, Alex K.
Love, John Christopher
Kellis, Manolis
Hafler, Brian P.
author_sort Menon, Madhvi
collection PubMed
description 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.
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spelling pubmed-68147492019-10-28 Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration Menon, Madhvi Mohammadi, Shahin Davila-Velderrain, Jose Goods, Brittany A. Cadwell, Tanina D. Xing, Yu Stemmer-Rachamimov, Anat Shalek, Alex K. Love, John Christopher Kellis, Manolis Hafler, Brian P. Nat Commun Article 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. Nature Publishing Group UK 2019-10-25 /pmc/articles/PMC6814749/ /pubmed/31653841 http://dx.doi.org/10.1038/s41467-019-12780-8 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Menon, Madhvi
Mohammadi, Shahin
Davila-Velderrain, Jose
Goods, Brittany A.
Cadwell, Tanina D.
Xing, Yu
Stemmer-Rachamimov, Anat
Shalek, Alex K.
Love, John Christopher
Kellis, Manolis
Hafler, Brian P.
Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration
title Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration
title_full Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration
title_fullStr Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration
title_full_unstemmed Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration
title_short Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration
title_sort single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6814749/
https://www.ncbi.nlm.nih.gov/pubmed/31653841
http://dx.doi.org/10.1038/s41467-019-12780-8
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