Retinal Aging Transcriptome and Cellular Landscape in Association With the Progression of Age-Related Macular Degeneration

Abstract

Purpose: Age is the main risk factor for age-related macular degeneration (AMD), a leading cause of blindness in the elderly, with limited therapeutic options.

Methods: Here, we analyze the transcriptomic characteristics and cellular landscape of the aging retinas from controls and patients with AMD.

Results: We identify the aging genes in the neural retina, which are associated with innate immune response and inflammation. Deconvolution analysis reveals that the estimated proportions of M2 macrophages are significantly increased with both age and AMD severity. Moreover, we find that proportions of Müller glia are significantly increased only with age but not with AMD severity. Several genes associated with both age and AMD severity, particularly C1s and MR1, are strong positively correlated with the proportions of Müller glia.

Conclusions: Our studies expand the genetic and cellular landscape of AMD and provide avenues for further studies on the relationship between age and AMD.

Figure 1.

Identification of age-associated genes in the human retina from donors with AMD. (a) Demographics of the human retina RNA-Seq profiles in the EyeGEx dataset (n = 453, previously quality-controlled dataset), detailed by age group and AMD severity (MGS level). (b) Forest plot of the ordinal logistic regression with MGS level as dependent variable. The green box and black line represent the estimate and the corresponding 95% confidence interval (CI), respectively. (c) Tukey boxplots (interquartile range [IQR] boxes with 1.5 × IQR whiskers) of age-associated genes in the human retina. Expression of age-up genes increase with age (left, n = 469). Age-associated genes were defined with DESeq2 two-sided likelihood-ratio test (adjusted p [adj P] < 0.05) by controlling for MGS level. Adjusted gene expressions were shown as z-score. (d) Functional enrichment analyses (biological processes) of age-up genes by Metascape.

Figure 2.

Identification of age-associated genes progressively increased with AMD severity (MGS level). Forest plot of age-associated gene having significant correlation with MGS level (age-MGS genes, n = 26, adjusted P < 0.05). Positive coefficients indicate age-associated genes increase in expression with MGS level. We focused the genes with increased expression over MGS level and defined them. Statistical analyses of MGS association for age-up genes were assessed by a nonparametric ordinal logistic regression model that controls for age. Point sizes are scaled by statistical significance. Error bars represent 95% confidence intervals.

Figure 3.

Correlation of age-MGS genes and their enrichment in retinal cell types. (a) Heatmap of the correlation matrix across 26 age-MGS genes identified in Figure 2. Pearson's correlation was calculated among 26 age-MGS genes to show the co-expression patterns of genes adjusted by age in the heatmap. Color key denotes the Pearson's correlation higher than absolute value 0.3 between genes. Genes highlighted in red indicates their stronger correlation with many other genes. (b) Heatmap showing the percentage of retina cells expressing each of age-MGS genes, scaled by gene across the different cell types. Data are from HCA.

Figure 4.

Functional roles of age-MGS genes in AMD. (a) Functional enrichment analyses (gene ontology annotations of biological processes) of 50 age-MGS genes by Metascape. (b) Eight selected age-MGS genes based on the GO annotations in Figure 4a. (c) Tukey boxplots (interquartile range [IQR] boxes with 1.5 × IQR whiskers) showing the expression of C3, C1s, S100B, MR1, FGF1, CLEC7A, BTN3A3, and TMEM98. Gene expression value are shown as log-transformed, controlled for age (Fig. 4c) or MGS level (Fig. 4d). Statistical significance of age-associated or MGS-associated difference was assessed by two-sided Kruskal-Wallis test on the adjusted expression values.

Figure 5.

The cellular landscape of the retina of patients with AMD and its relation to Age-MGS genes. (a, b) Forest plot of estimated proportions of retinal cells having significant correlation with age or MGS level, respectively (P < 0.05). Positive coefficients indicate the proportion of cells increases with age or MGS level, while negative coefficients indicate the proportion of cells decrease with age or MGS level. Statistical analyses of age association or MGS association for the proportions of cells were assessed by a nonparametric ordinal logistic regression model, adjusted for MGS level (a) or age (b), respectively. Point sizes are scaled by statistical significance. Error bars represent 95% confidence intervals. (c) Analyses of the correlation between expression level of C3, C1s, or MR1 and the estimated proportion of Müller glia, adjusted for age. Pearson's correlation coefficient was used to test the strength of linear relationships between gene expression and the estimated proportion of Müller glia. Black dots denote individual samples (n = 547). Blue lines denote regression lines. (d) Heatmap of the correlation matrix among retinal cell types, based on the proportions of retina cells estimated by CIBERSORTx and adjusted for age. Pearson's correlation was calculated among retinal cells to show the co-expression patterns of cells in the heatmap. Values in the heatmap indicates the Pearson correlation coefficient between two cell types.