Retinal aging transcriptome and cellular landscape in association with the progression of age-related macular degeneration
Wang, J.-H.; Wong, R. C. B.; Liu, G.-S.
Show abstract
Age is the main risk factor for age-related macular degeneration (AMD), a leading cause of blindness in the elderly, with limited therapeutic options. Here we systematically analyzed the transcriptomic characteristics and cellular landscape of the aging retina from controls and patients with AMD. We identify the aging genes in the retina that are associated with innate immune response and inflammation. Deconvolution analysis reveals that the estimated proportion of M2 and M0 macrophages is increased and decreased, respectively with both age and AMD severity. Moreover, we find that Muller glia are increased with age but not with disease severity. Several genes associated with both age and disease severity in AMD, particularly C1s and MR1, are strong positively correlated with the proportions of Muller glia. Our studies expand the genetic and cellular landscape of AMD and provide avenues for further studies on the relationship between age and AMD.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Aging-related cell type-specific pathophysiologic immune responses that exacerbate disease severity in aged COVID-19 patients 94%
- Müller Glia regenerative potential is maintained throughout life despite neurodegeneration and gliosis in the ageing zebrafish retina 94%
- Alterations of the gut microbiome are associated with epigenetic age acceleration and physical fitness 93%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Missense mutations in CRX homeodomain cause dominant retinopathies through two distinct mechanisms 94%
- The Neuron-specific IIS/FOXO Transcriptome in Aged Animals Reveals Regulatory Mechanisms of Neuronal and Cognitive Aging 94%
- voyAGEr: free web interface for the analysis of age-related gene expression alterations in human tissues 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.