Research Report on Spatiotemporal Transcriptomic Changes in Human Ovarian Aging and the Regulatory Role of FOXP1

Academic Background

With the continuous increase in global average life expectancy, health issues faced by women during menopause are receiving increasing attention. Ovarian aging is one of the significant issues, closely related to various health problems such as osteoporosis, cardiovascular diseases, obesity, tumors, Alzheimer’s disease, and diabetes. To explore therapeutic strategies to delay ovarian aging, it is necessary to fully understand the cellular composition, molecular characteristics, and their spatiotemporal changes in the ovary. However, our understanding of how human ovarian aging affects cellular and molecular levels is still limited. This study systematically characterized the spatiotemporal molecular characteristics of eight cell types during human ovarian aging by integrating single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST-seq).

Source of the Paper

This research was published in the “Nature Aging” journal on April 9, 2024, with the article titled “Spatiotemporal transcriptomic changes of human ovarian aging and the regulatory role of FOXP1.” The study was conducted jointly by researchers from the Department of Obstetrics and Gynecology at the Affiliated Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, the National Clinical Research Center for Obstetrics and Gynecology, and the Key Laboratory of Cancer Invasion and Metastasis of the Ministry of Education, including Meng Wu, Weicheng Tang, and others.

Research Design

• Participants and Sample Source: The study subjects included 15 female cases who voluntarily underwent hysterectomy and salpingectomy. These ovarian samples were categorized into three groups based on age: young group (18-28 years), middle-aged group (36-39 years), and elderly group (47-49 years).
• Control and Grouping: There were 5 ovarian tissue samples in each group, and scRNA-seq and ST-seq were performed on each.
• Experimental Procedures: Ovarian samples were dissociated and single-cell suspensions were prepared, followed by scRNA-seq using the 10x Genomics platform. ST-seq data were generated and integrated with single-cell data to infer the possible single-cell composition of each ST point.
• Data Analysis: Scatter Factor Analysis was used to infer cell type composition for each ST point, and eight cell types were identified using the UMAP algorithm and specific cell markers. Gene ontology (GO) analysis and differentially expressed genes (DEG) analysis were conducted.

Research Results

1. Single-cell RNA Sequencing and Spatial Localization

The study identified eight cell types, including oocytes, granulosa cells (GCs), smooth muscle cells (SMCs), endothelial cells, monocytes, natural killer (NK) cells, and T lymphocytes. GO analysis showed that genes highly expressed in granulosa cells were enriched in hormone level regulation, while oocyte-related genes were associated with oocyte differentiation. Using ST-seq data, the spatial distribution of each cell type was successfully mapped, revealing spatiotemporal changes in ovarian cells at different ages.

2. Transcriptomic Changes Related to Ovarian Cell Aging

The study demonstrated significant differences in gene expression patterns among various ovarian cell types during aging. Specifically, upregulated DEGs were mainly associated with “cellular senescence” and related signaling pathways (such as FOXO, IL-17, and NF-κB). Downregulated DEGs were primarily involved in cell migration, extracellular matrix-receptor interaction, and estrogen signaling pathways. With aging, the senescence signaling pathway scores of most ovarian cells significantly increased, further validating the critical role of cellular senescence in ovarian aging.

3. Characteristics and Changes in Granulosa Cells and Oocytes

Granulosa cells were divided into three subtypes, with the elderly group mainly consisting of GC subtype 3, whose markers were associated with apoptosis and cell cycle. Both scRNA-seq and ST-seq data confirmed the subtypes and spatial distribution of granulosa cells. During aging, oocytes showed evident DNA damage and a weakening repair capability, with significant upregulation of DNA damage response genes and decreased expression of DNA repair genes.

4. Changes in T&S Cells

The study identified five subtypes of T&S cells. In the elderly group, T&S cells primarily exhibited cellular stress responses, and the senescence scores of all T&S subtypes significantly increased. Further analysis indicated that cdkn1a was widely distributed in aging samples, with significantly increased levels of the SASP factor associated with cellular senescence.

5. Regulatory Role of FOXP1

The study found that FOXP1 significantly decreased with age in the ovary, and its downregulation was associated with cellular senescence in granulosa cells and T&S cells. FOXP1 knockdown experiments confirmed that FOXP1 prevents ovarian cell senescence by inhibiting the transcription of cdkn1a. In FOXP1 knockout mice, ovarian volume decreased, the number of healthy follicles reduced, and serum AMH and estradiol levels dropped, exhibiting accelerated ovarian aging characteristics.

6. Protective Effect of Quercetin on Ovarian Aging

The study found that quercetin significantly reduced the expression of cdkn1a and other senescence markers, promoted cell proliferation, and reduced DNA damage. Through drug treatment, quercetin was able to delay ovarian aging in middle-aged mice, increase the number of healthy follicles, and enhance reproductive capability. Quercetin demonstrated potential therapeutic value in combating ovarian aging.

Summary

This study systematically elucidated the spatiotemporal transcriptomic changes during human ovarian aging for the first time by integrating single-cell and spatial transcriptomics, identified FOXP1 as a key regulator of ovarian cell senescence, and showcased the potential application value of quercetin in delaying ovarian aging. These findings provide valuable resources and perspectives for further research on ovarian aging mechanisms and the development of new diagnostic and therapeutic strategies.