Integrated analyses of the single-cell ATAC-seq and RNA-seq reveal the epigenetic landscape of human ovarian aging

Abstract

AbstractStudying the molecular mechanisms of ovarian aging is crucial for understanding the age-related fertility issues in females. Recently, a single-cell transcriptomic roadmap of ovarian aging based on non-human primates revealed the molecular signatures of the oocytes at different developmental stages. Herein, we present the first epigenetic landscape of human ovarian aging, through an integrated analysis of the single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) and single-cell RNA-seq. We depicted the transcriptional profiles and chromatin accessibility of the ovarian tissues isolated from old (n=4) and young (n=2) donors. The unsupervised clustering of data revealed seven distinct cell populations in the ovarian tissues and six subtypes of oocytes, which could be distinguished by age difference. Further analysis of the scATAC-seq data from the young and old oocytes revealed that the interaction between the Notch signaling pathway and AP-1 family transcription factors may crucially determine oocyte aging. Finally, a machine-learning algorithm was applied to calculate the optimal model based on the single-cell dataset for predicting oocyte aging, which exhibited excellent accuracy with a cross-validated area under the receiver operating characteristics score of 0.99. In summary, this study provides a comprehensive understanding of human ovarian aging at both the transcriptomic and epigenetic levels, based on an integrated analysis of large-scale single-cell datasets. We believe our results will shed light on the discovery of potential therapeutic targets or diagnostic markers for age-related ovarian disorders.