Comparative single-cell transcriptomic roadmap of mammalian fetal ovarian development
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Curated by eLife
eLife Assessment
This valuable study provides a cross-species single-cell transcriptomic resource for early female gonadal development in mammals. The data supporting the main conclusion remain incomplete, and experimental validation is needed to strengthen the conclusions. The work will be of interest to reproductive biologists and developmental biologists.
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Abstract
Early ovarian development establishes the cellular basis of female reproduction, yet its cellular composition and developmental dynamics remain poorly characterized across mammalian species. Here, we generated a cross-species single-cell transcriptomic atlas of early female gonadal development in cattle (E38-E112), human (PCW6-16), and mouse (E11.5-E18.5), integrating 107,930 cells across comparable developmental windows, spanning sex determination and early ovarian differentiation. We identified 11 shared gonadal cell types across three species, including germ cells and granulosa cells, and uncovered a previously undescribed bovine-specific cell population with steroidogenic features, suggesting species differences in ovarian somatic lineage development. Epigenetic regulators exhibited dynamic activation in germ cells and granulosa cells. Developmental trajectory analysis revealed shared and species-specific genes associated with germ cell and granulosa cell development, including conserved dynamically expressed genes such as TFAP2C and ZCWPW1 in germ cells and FOS and JUNB in granulosa cells. Cross-species cell type classification using a machine learning support vector machine (SVM) model revealed transcriptionally conserved cell types, such as immune cells and germ cells, while granulosa cells showed substantial cross-species divergence, associated with extracellular matrix-related gene expression. Together, our study provides a comparative framework for understanding conserved and divergent mechanisms of early ovarian development across mammals.
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eLife Assessment
This valuable study provides a cross-species single-cell transcriptomic resource for early female gonadal development in mammals. The data supporting the main conclusion remain incomplete, and experimental validation is needed to strengthen the conclusions. The work will be of interest to reproductive biologists and developmental biologists.
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Reviewer #1 (Public review):
Summary:
Fang et al. characterize the cellular basis of early ovarian development through a comparative analysis of single-cell transcriptomic data. The authors integrate a novel bovine scRNA-seq dataset, spanning six gestational stages (E38-E112), with stage-matched human (PCW6-16) and mouse (E11.5-E18.5) counterparts. Beyond identifying shared gonadal cell types across these three species, the study uncovers a previously uncharacterized bovine-specific cell population with steroidogenic features. Their analysis highlights conserved, dynamically expressed regulators, including TFAP2C and ZCWPW1 in germ cells and FOS and JUNB in granulosa cells. Furthermore, by employing a machine learning Support Vector Machine (SVM) model, the authors quantify cell-type conservation, demonstrating that while immune and …
Reviewer #1 (Public review):
Summary:
Fang et al. characterize the cellular basis of early ovarian development through a comparative analysis of single-cell transcriptomic data. The authors integrate a novel bovine scRNA-seq dataset, spanning six gestational stages (E38-E112), with stage-matched human (PCW6-16) and mouse (E11.5-E18.5) counterparts. Beyond identifying shared gonadal cell types across these three species, the study uncovers a previously uncharacterized bovine-specific cell population with steroidogenic features. Their analysis highlights conserved, dynamically expressed regulators, including TFAP2C and ZCWPW1 in germ cells and FOS and JUNB in granulosa cells. Furthermore, by employing a machine learning Support Vector Machine (SVM) model, the authors quantify cell-type conservation, demonstrating that while immune and germ cells are highly conserved across species, granulosa cells exhibit substantial evolutionary divergence. This study makes a significant contribution to developmental biology by establishing a comprehensive, cross-species single-cell roadmap of fetal ovarian development. By integrating livestock data with human and rodent models, the authors identify novel cellular states and provide a framework for assessing transcriptional conservation across species.
Strengths:
(1) While human and mouse fetal ovaries have been mapped, the inclusion of a high-resolution bovine dataset (107,930 cells total across the study) provides a critical "large mammal" perspective that is often missing from comparative studies.
(2) The identification of a bovine-specific cell population is an important finding. It suggests that ruminants may have a different developmental timeline for steroidogenic precursors (potentially theca cell ancestors) compared to rodents or humans.
(3) Training a Support Vector Machine (SVM) to quantitatively assess cell-type similarity is a major strength. It moves beyond qualitative UMAP "eye-balling" to provide a statistical probability of conservation.
(4) The study links gene expression to higher-order biological processes like epigenetic reprogramming and cell-cell communication (CellChat), providing a holistic view of the gonadal niche.
Weaknesses:
(1) The authors integrated publicly available scRNA-seq datasets generated across different laboratories and technical platforms. However, the specific methods used to control for and evaluate batch effects are not clearly described. It is critical to clarify whether the observed species-specific differences are purely biological or partly influenced by technical variation between datasets.
(2) A challenge inherent to all single-cell studies is the reliance on manual marker-gene-based annotation. While this is standard practice, it remains unclear how robust these assignments are, particularly for the novel "bovine-specific" population. Further evidence or cross-validation (e.g., through varied clustering resolutions or automated annotation tools) is required to ensure these clusters represent true biological states rather than computational artifacts.
(3) The authors utilized a linear SVM to assess cross-species similarity. However, it is not clear how this model performs compared to established single-cell mapping and comparative tools (e.g., MetaNeighbor or Seurat v5). Providing a justification for this specific SVM-based approach, or a brief comparison with existing benchmarks, would strengthen the methodological rigor of the study.
(4) While the computational evidence is compelling, the study would be significantly enhanced by independent validation of the "unclassified bovine-specific" cell population. To confirm the biological reality and reproducibility of this novel cell state, the authors should provide additional evidence. This could include in situ validation (e.g., immunofluorescence or in situ hybridization) to determine its physical location and morphology within the gonad, or demonstrating the presence of this specific cell population within an independent, non-overlapping bovine dataset.
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Reviewer #2 (Public review):
Summary:
The authors generate a comparative single-cell transcriptomic atlas of fetal ovarian development in cattle, human, and mouse, with the goal of identifying conserved and species-specific cellular and molecular features of early ovarian differentiation. The study provides a valuable resource for the field and reveals potentially interesting species-specific characteristics, including a putative bovine steroidogenic cell population. While the dataset is substantial and the computational analyses are generally appropriate, several major conclusions rely primarily on computational inference without independent experimental validation, limiting the strength of evidence supporting some of the central claims.
Strengths:
This study provides a valuable cross-species single-cell atlas of fetal ovarian …
Reviewer #2 (Public review):
Summary:
The authors generate a comparative single-cell transcriptomic atlas of fetal ovarian development in cattle, human, and mouse, with the goal of identifying conserved and species-specific cellular and molecular features of early ovarian differentiation. The study provides a valuable resource for the field and reveals potentially interesting species-specific characteristics, including a putative bovine steroidogenic cell population. While the dataset is substantial and the computational analyses are generally appropriate, several major conclusions rely primarily on computational inference without independent experimental validation, limiting the strength of evidence supporting some of the central claims.
Strengths:
This study provides a valuable cross-species single-cell atlas of fetal ovarian development by integrating newly generated bovine data with human and mouse datasets. The work fills an important gap in reproductive biology and offers a useful resource for investigating conserved and species-specific features of ovarian development.
The analyses are comprehensive and combine developmental trajectory reconstruction, regulatory network inference, cell-cell communication analysis, and cross-species classification. The identification of a putative bovine-specific steroidogenic cell population is particularly intriguing and may provide a basis for future studies of species-specific ovarian development.
Weaknesses:
The main limitation is that several key conclusions rely primarily on computational analyses without independent experimental validation. In particular, the proposed bovine-specific steroidogenic cell population, which represents the major novel finding of the study, is supported only by transcriptomic evidence.
In addition, many mechanistic interpretations derived from trajectory, regulatory network, and cell-cell communication analyses remain speculative. While the study succeeds as a comparative resource, the evidence supporting several of the central biological claims remains incomplete, and the biological significance of some cross-species differences is not fully explored.
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