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Updated: Jul 4, 2026

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
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Spatial Transcriptomics in Ovarian Biology Technologies, Computational Challenges, and Biological Insights.

Ruixu Huang1, Brittany Anne Goods2

  • 1Thayer School of Engineering, Dartmouth College, NH, United States.

Reproduction (Cambridge, England)
|July 2, 2026
PubMed
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Spatially resolved transcriptomics (ST) reveals gene expression in intact ovarian tissue, complementing single-cell RNA sequencing. This review details ST platforms and their applications in ovarian research, highlighting integration strategies for deeper insights.

Area of Science:

  • Reproductive biology and genomics.
  • Biotechnology and bioinformatics.

Background:

  • The ovary's complex cellular interactions are crucial for its function.
  • Dissociation-based methods like single-cell RNA sequencing (scRNA-seq) lose vital spatial context.
  • Spatially resolved transcriptomics (ST) preserves tissue architecture for gene expression analysis.

Purpose of the Study:

  • To provide a comprehensive review of Spatially resolved transcriptomics (ST) platforms.
  • To assess the suitability of ST technologies for ovarian research.
  • To discuss computational challenges and multi-modal integration strategies in ovarian ST studies.

Main Methods:

  • Review of major ST platforms: sequencing-based (Visium, Visium HD, Stereo-seq, GeoMx) and imaging-based (Xenium, MERSCOPE, CosMx).
Keywords:
Computational BiologyOvarySingle-cell RNA SequencingSpatial Transcriptomics

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  • Survey of 40 published studies applying ST to ovarian biology (atlases, aging, follicle development, cancer).
  • Discussion of computational analyses and multi-modal integration (e.g., with scRNA-seq, spatial proteomics).

    • Main Results:

    • ST platforms offer distinct technical features, resolution, and suitability for ovarian research.
    • ST applications span ovarian atlases, aging, follicle dynamics, and cancer studies.
    • Multi-modal integration enhances the resolution of ovarian molecular complexity.

    Conclusions:

    • No single ST platform is optimal for all ovarian research questions.
    • Careful selection of ST platforms based on biological goals and tissue scale is critical.
    • Advancing ST in ovarian research requires aligning platform capabilities with research objectives for mechanistic discovery.