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

Nat Commun. 2019 Oct 25;10(1):4902. doi: 10.1038/s41467-019-12780-8.


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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Amacrine Cells / metabolism
  • Astrocytes / metabolism
  • Blood Vessels
  • Ependymoglial Cells / metabolism
  • Gene Expression Profiling
  • Gene Expression*
  • Genetic Predisposition to Disease
  • High-Throughput Nucleotide Sequencing
  • Humans
  • Macular Degeneration / genetics*
  • Microglia / metabolism
  • Neuroglia / metabolism*
  • Retina / cytology*
  • Retina / metabolism
  • Retinal Bipolar Cells / metabolism
  • Retinal Cone Photoreceptor Cells / metabolism*
  • Retinal Ganglion Cells / metabolism
  • Retinal Horizontal Cells / metabolism
  • Retinal Neurons / metabolism*
  • Retinal Rod Photoreceptor Cells / metabolism
  • Retinal Vessels / cytology*
  • Retinal Vessels / metabolism
  • Sequence Analysis, RNA
  • Single-Cell Analysis