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. 2024 Feb 19;22(1):24.
doi: 10.1186/s12958-024-01195-w.

Deconvolution at the single-cell level reveals ovarian cell-type-specific transcriptomic changes in PCOS

Shumin Li  1   2   3 , Yimeng Li  2   4   5   6 , Yu Sun  2   4   5   6 , Gengchen Feng  2   4   5   6 , Ziyi Yang  2   4   5   6 , Xueqi Yan  2   4   5   6 , Xueying Gao  1   3 , Yonghui Jiang  7 , Yanzhi Du  8   9 , Shigang Zhao  10   11   12   13   14 , Han Zhao  15   16   17   18   19 , Zi-Jiang Chen  20   21   22   23   24   25   26   27
Affiliations

Deconvolution at the single-cell level reveals ovarian cell-type-specific transcriptomic changes in PCOS

Shumin Li et al. Reprod Biol Endocrinol. .

Abstract

Background: Polycystic ovary syndrome (PCOS) is one of the most common reproductive endocrine disorders in females of childbearing age. Various types of ovarian cells work together to maintain normal reproductive function, whose discordance often takes part in the development and progression of PCOS. Understanding the cellular heterogeneity and compositions of ovarian cells would provide insight into PCOS pathogenesis, but are, however, not well understood. Transcriptomic characterization of cells isolated from PCOS cases have been assessed using bulk RNA-seq but cells isolated contain a mixture of many ovarian cell types.

Methods: Here we utilized the reference scRNA-seq data from human adult ovaries to deconvolute and estimate cell proportions and dysfunction of ovarian cells in PCOS, by integrating various granulosa cells(GCs) transcriptomic data.

Results: We successfully defined 22 distinct cell clusters of human ovarian cells. Then after transcriptome integration, we obtained a gene expression matrix with 13,904 genes within 30 samples (15 control vs. 15 PCOS). Subsequent deconvolution analysis revealed decreased proportion of small antral GCs and increased proportion of KRT8high mural GCs, HTRA1high cumulus cells in PCOS, especially increased differentiation from small antral GCs to KRT8high mural GCs. For theca cells, the abundance of internal theca cells (TCs) and external TCs was both increased. Less TCF21high stroma cells (SCs) and more STARhigh SCs were observed. The proportions of NK cells and monocytes were decreased, and T cells occupied more in PCOS and communicated stronger with inTCs and exTCs. In the end, we predicted the candidate drugs which could be used to correct the proportion of ovarian cells in patients with PCOS.

Conclusions: Taken together, this study provides insights into the molecular alterations and cellular compositions in PCOS ovarian tissue. The findings might contribute to our understanding of PCOS pathophysiology and offer resource for PCOS basic research.

Keywords: Bulk RNA-seq; Deconvolution; Granulosa cells; Polycystic ovary syndrome; scRNA-seq.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
scRNA sequencing and bulk-RNA sequencing data analysis. A Flowchart of data processing in the study. B UMAP cluster map revealing 22 specific clusters representing the major ovarian cell types in human adult ovaries. C Heatmap of marker genes of each cluster from 22 specific cell clusters. D PCA plot of granulosa cells transcriptome data from 15 controls and 15 PCOS patients. E Heatmap of expression of 13,904 genes all detected in these 30 samples
Fig. 2
Fig. 2
Changes of the ovarian cell compositions in PCOS. A Column stacked plots of the proportions of 22 cell clusters in each sample in control and PCOS groups. (B-K) Boxplots of clusters with altered proportions between two groups, including small antral GCs, KRT8high mural GCs, HTRA1high CC, TCF21high SC, STARhigh SC, inTC, exTC, T cells, monocytes and NK cells. Student’s t test. P < 0.05 was considered statistically significant with a signal * and not significant without any marking
Fig. 3
Fig. 3
GCs dysfunction along with the disrupted GCs differentiation in PCOS. A Heatmap showing the core gene in modules regulating GCs differentiation in Monocle 3 (resolution = 0.0001). B The top 5 enriched GO (biological pathway) terms of downregulated DEGs in module 4. C The top 5 enriched GO (biological pathway) terms of upregulated DEGs in module 3/14/15. D Monocle 3 generated pseudotemporal trajectory of preantral GCs, small antral GCs, KRT8high mural GCs and HSPA6high mural GCs. E The granulosa cell trajectory predicted by Monocle 3 and visualized by UMAP. Cells were ordered in pseudotime colored in a gradient from purple to yellow. F Top 15 significant switching genes were ordered in pseudotime in the trajectory, from preantral GCs, small antral GCs to KRT8high mural GCs. G Top 15 significant switching genes were ordered in pseudotime in the trajectory, from preantral GCs, small antral GCs to HSPA6high mural GCs. H Venn plot of all the significant switching genes in above two trajectories. I Pathway enrichment of the 47 significant switching genes specific in KRT8high mural GCs differentiation trajectory
Fig. 4
Fig. 4
Enhanced cell communications from TCs in PCOS. A Network displaying the number of cell-to-cell interactions in the TC, SC and immune cell clusters. B Heatmap of outgoing/incoming cell-to-cell interaction signaling pathways in the TC, SC and immune cell clusters. C Chord plot of COLLAGEN pathway among TC, SC and immune cell clusters. D Chord plot of LAMININ pathway among TC, SC and immune cell clusters. E The bar diagram showing the intersection of the DEGs between PCOS and control groups with the TC and SC clusters marker genes, with -log10(P-value) in vertical coordinates
Fig. 5
Fig. 5
Drug candidates for improving altered cellular compositions in PCOS. A Venn plot of top markers of cell clusters whose proportions were altered and DEGs between PCOS and control. B Barplot of the top 10 predicted compounds targeting the altered cellular compositions in PCOS
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