Increased transcriptome variation and localised DNA methylation changes in oocytes from aged mice revealed by parallel single-cell analysis

Abstract

Advancing maternal age causes a progressive reduction in fertility. The decline in developmental competence of the oocyte with age is likely to be a consequence of multiple contributory factors. Loss of epigenetic quality of the oocyte could impair early developmental events or programme adverse outcomes in offspring that manifest only later in life. Here, we undertake joint profiling of the transcriptome and DNA methylome of individual oocytes from reproductively young and old mice undergoing natural ovulation. We find reduced complexity as well as increased variance in the transcriptome of oocytes from aged females. This transcriptome heterogeneity is reflected in the identification of discrete sub-populations. Oocytes with a transcriptome characteristic of immature chromatin configuration (NSN) clustered into two groups: one with reduced developmental competence, as indicated by lower expression of maternal effect genes, and one with a young-like transcriptome. Oocytes from older females had on average reduced CpG methylation, but the characteristic bimodal methylation landscape of the oocyte was preserved. Germline differentially methylated regions of imprinted genes were appropriately methylated irrespective of age. For the majority of differentially expressed transcripts, the absence of correlated methylation changes suggests a post-transcriptional basis for most age-related effects on the transcriptome. However, we did find differences in gene body methylation at which there were corresponding changes in gene expression, indicating age-related effects on transcription that translate into methylation differences. Interestingly, oocytes varied in expression and methylation of these genes, which could contribute to variable competence of oocytes or penetrance of maternal age-related phenotypes in offspring.

Keywords: DNA methylation; advanced maternal age; chromatin; epigenetics; gene expression; oocytes; single-cell genomics.

Figure 1

Transcriptomic profiles of oocytes from young and old females. (a) Boxplots of transcript diversity showing a decrease in oocytes from aged mice (Wilcoxon test;p = 2.6x10⁻⁵). Each dot represents the number of genes detected at ≥1 counts in scRNA‐seq of an individual MII oocyte. (b) Heatmap of the associations (linear regression) between a transcriptional signature of chromatin configuration, number of detected transcripts and age with the first five principal components of the transcriptome. (c) Principal component analysis (PCA) plot of the transcriptome of oocytes from young and aged mice. Principal component 1 (PC1) is highly explained by a predicted chromatin configuration (non‐surrounded nucleolus (NSN): circles; surrounded nucleolus (SN): triangles). PC2 is highly explained by age (young: red; aged:turquoise). A subpopulation of aged oocytes with young‐like (light turquoise) features is also demarcated. (d) Hierarchical clustering for the prediction of chromatin states of oocytes as NSN (black) or SN (grey) according to the level of expression of genes overexpressed in SN oocytes. (e) t‐SNE plot showing four main clusters of oocytes driven by inferred chromatin state and/or age group. (f) Barcode plot showing the enrichment of maternal effect genes when testing for differences across the four main clusters of oocytes. Each horizontal bar represents one maternal effect gene. The position of the bar along they‐axis represents its ranking across all expressed genes tested for differential expression (1 being the most significant)

Figure 2

Differences in variability and mean levels of transcript abundance. (a) Volcano plot of differential variability between young and aged oocytes showing a greater number of genes having more variable expression values in the aged group. (b) Upper: boxplot of the pairwise distances between cells of the same age group. Lower: heatmap of the 2D distribution of oocytes using the expression level of genes identified as differentially variable between young and aged oocytes. (c) Volcano plot of differential mean expression between young and aged oocytes. (d) Scatter plot of the effect size vs. mean expression level of genes tested for differential expression. (e) Hierarchical clustering of oocytes using expression levels of 560 age‐associated genes showing a young‐like subgroup within the aged oocytes. In A, C, D, red and blue dots represent higher or lower variability (A) and mean expression (C, D), respectively, in the aged group

Figure 3

DNA methylation profiles of individual oocytes from young and aged females. (a) Boxplot of average CpG methylation values showing lower levels in aged oocytes (Wilcoxon test;p = 0.024). Each dot represents the global CpG methylation estimate from scBS‐seq of 32 individual GV oocytes from young and 30 from aged females. (b) PCA plot of oocyte CpG methylomes of young and aged oocytes based on the average methylation at hyper‐, hypo‐ and intermediately methylated domains. (c) Violin plots of the distribution of average CpG methylation values across three types of methylation domains in the oocyte: hypermethylated (HyperD: 75‐100%); hypomethylated (HypoD: 0‐25%) and intermediately methylated (Inter: 25‐75%) in young and aged oocytes. scBS‐seq data merged by group. (d) Boxplot of average CpG methylation values in oocytes assigned as NSN and SN (Wilcoxon test;p = 1.4x10⁻⁵). (e) Violin plots of the distribution of average CpG methylation values of HyperD, HypoD and Inter domains in oocytes assigned as NSN and SN. scBS‐seq data merged by group. (F) Boxplot of average non‐CpG methylation values in young and aged oocytes (Wilcoxon test;p = 0.028)

Figure 4

Localised DNA methylation changes in oocytes from aged females. (a) Heatmap showing methylation levels at 19 maternal gDMRs, three paternal gDMRs (Rasgrf1,Dlk1‐Gtl2,H19_Igf2) and secondary DMRs (NespandGpr1/Zdbf2). Shown are the CpG methylation levels of each DMR merged across the 30 young or 32 aged oocyte scBS‐seq data sets. (b) Scatter plot showing the average methylation in aged vs. young oocytes of high‐confidence DMRs (hyper‐ (HyperD), hypo‐ (HypoD) and intermediately methylated (Inter) domains) with higher levels in aged or young oocytes marked in turquoiseor red, respectively. DMRs were defined as significant at a false discovery rate of 5% in at least 95% of 100 pseudobulk combinations of 10 oocytes. (c) Pie chart of the percentage of high‐confidence DMRs within hyper‐ (HyperD), hypo‐ (HypoD) and intermediately methylated (Inter) domains and the expected genomic distribution. (d) Coordinate changes in gene expression and CpG methylation at 23 genes in aged oocytes showing a positive association. Methylation difference refers to difference in % CpG methylation in merged data of young and old oocytes. (e) Scatter plot ofUstas an example of coupled changes between gene expression and CpG methylation at single‐cell level. (f) Seqmonk genome browser of a 169.1 kbp interval surrounding theUstlocus. The RNA‐seq tracks quantify counts at 2 bp windows. In the BS‐seq tracks, each vertical block represents a hyper‐, hypo‐ or intermediately methylated domain. Height and colour‐coding indicate read counts and % methylation, respectively. Methylation values of 100‐CpG tiles represent mean of three pseudobulk groups each comprising 10 scBS‐seq data sets, with standard error indicated. Tracks labelled methylated CpG (red) and unmethylated CpG (blue) indicate total reads at individual CpGs for the scBS‐seq data sets merged by young and aged groups. On the top, the track labelled oocyte mRNA depicts exonic structures of oocyte transcripts with red indicating transcription from left to right and blue indicating transcription from right to left