Single-cell RNA sequencing uncovers cellular geterogeneity of granulosa cells and provides a signature for follicular development in chicken

Abstract

Functional differentiation of the granulosa cell (GC) layer plays an essential role in follicular development and oocyte maturation in birds. However, despite the importance of GCs, heterogeneity within the granulosa layer across poultry species remains poorly defined and functional transcriptomic signatures across distinct cellular subpopulations are lacking. In this study, single-cell RNA sequencing was used to identify GC types, uncover heterogeneity, and construct the developmental trajectories of chicken GCs at two developmental stages: the hierarchical follicle (HF)-GC and prehierarchical follicle (PHF)-GC stages. The following four GC types were identified: rapid growth, early, luteal, and primitive GCs. We found significant differences in the abundance of these different cell types at the HF-GC and PHF-GC stages. We also identified four potential differentiation trajectories for GCs during follicular development. The four distinct developmental trajectories may elucidate that the dynamic interplay and transition among these four GC types are pivotal in determining the fate of the follicle. A single-cell regulatory network inference and clustering analysis was performed to identify specifically expressed transcription factors in the different GC types. These transcription factors may have key regulatory roles in follicular development. Furthermore, we identified genes that were differentially expressed in HF-GCs and PHF-GCs. In total, 1,049 differentially expressed genes were identified in the two groups, including 379 upregulated and 670 downregulated genes. Finally, we identified several genes that may play important roles in follicle selection and development in chicken, including CDK2, CCND1, CCND2, BCL2, FOXO1, AMH, WT1, STAR, CYP11A1, HSD11B2, CYP51A1, and ESR1. These findings enhance our understanding of the cell biology of the GC layer in ovarian follicles‌ ‌and provide a basis for future studies of the relevant molecular regulatory mechanisms.

Keywords: Cell heterogeneity; Chicken; Granulosa cell Follicular development; Single-cell transcriptome.

Fig. 1

Stages of chicken follicle development Granulosa layers were isolated from hierarchical and prehierarchical follicles (F1–F5). Prehierarchical follicles included small white follicles, large white follicles (white arrows), and small yellow follicles (black arrows).

Fig. 2

Single-cell transcriptome sequencing of granulosa cells (GCs) in chicken follicles. (A) Uniform Manifold Approximation and Projection (UMAP) analysis showing the cell clusters identified in the hierarchical follicle and prehierarchical follicle GC groups. (B) Bar plot showing the proportion of cell clusters in each group.

Fig. 3

Landscape of cell types in the granulosa cell (GC) layers of chicken follicles. (A) Uniform Manifold Approximation and Projection (UMAP) plot of granulosa cell (GC) types. Different colors indicate different cell types. (B–E) UMAP plots showing expression levels for canonical markers for rapid growth GCs (B), luteal GCs (C), early GCs (D), and primitive GCs (E). (F) Dot plots showing the expression patterns and levels of representative marker genes for each cell type. (G) Bar plots showing the proportions of each cell type in both groups.

Fig. 4

Heat map showing the top 10 marker genes for each cell type and functional enrichment.

Fig. 5

Dynamic transition of granulosa cells (GCs) in follicle selection. (A) RNA velocity analysis showing the transition potential among 15 granulosa cell (GC) clusters. (B) RNA velocity analysis showing the transition potential among four GC types. (C) Uniform Manifold Approximation and Projection (UMAP) plot of the full dataset showing latent time values. The velocity length (D) and velocity confidence (E) of the GCs. (F) Slingshot differentiation trajectory analyses of GCs.

Fig. 6

Single-cell regulatory network inference and clustering (SCENIC) analysis showing key transcription factors that regulate various granulosa cell (GC) types. (A) Active heatmap of regulons for all the granulosa cell (GC) types. The number of target genes enriched in each regulon is shown in parentheses. Abbreviation: g, genes. (B) Regulons ranked in four GC types, based on regulon specificity score. (C) Heatmap showing connection specificity index (CSI) clustering of the regulon module. Color changes from blue to yellow indicate that CSI correlation values are increasing. Regulons with high CSI values may have similar cellular functions and may jointly regulate downstream genes. (D) Heatmap for the activity of the CSI module in each GC type. Rows represent CSI modules; columns represent different GC types.