Spatial transcriptomics in ovarian biology technologies: computational challenges, and biological 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).
• 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.