Application of Single-Cell RNA Sequencing in Ovarian Development

Biomolecules. 2022 Dec 27;13(1):47. doi: 10.3390/biom13010047.

Abstract

The ovary is a female reproductive organ that plays a key role in fertility and the maintenance of endocrine homeostasis, which is of great importance to women's health. It is characterized by a high heterogeneity, with different cellular subpopulations primarily containing oocytes, granulosa cells, stromal cells, endothelial cells, vascular smooth muscle cells, and diverse immune cell types. Each has unique and important functions. From the fetal period to old age, the ovary experiences continuous structural and functional changes, with the gene expression of each cell type undergoing dramatic changes. In addition, ovarian development strongly relies on the communication between germ and somatic cells. Compared to traditional bulk RNA sequencing techniques, the single-cell RNA sequencing (scRNA-seq) approach has substantial advantages in analyzing individual cells within an ever-changing and complicated tissue, classifying them into cell types, characterizing single cells, delineating the cellular developmental trajectory, and studying cell-to-cell interactions. In this review, we present single-cell transcriptome mapping of the ovary, summarize the characteristics of the important constituent cells of the ovary and the critical cellular developmental processes, and describe key signaling pathways for cell-to-cell communication in the ovary, as revealed by scRNA-seq. This review will undoubtedly improve our understanding of the characteristics of ovarian cells and development, thus enabling the identification of novel therapeutic targets for ovarian-related diseases.

Keywords: intercellular interaction; ovarian development; ovary; single-cell RNA sequencing.

Publication types

  • Review
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Endothelial Cells*
  • Female
  • Granulosa Cells / metabolism
  • Oocytes* / metabolism
  • Ovary / metabolism
  • Sequence Analysis, RNA