Open-ST: High-resolution spatial transcriptomics in 3D

Area of Science:

• Molecular Biology
• Genomics
• Bioinformatics

Background:

• Spatial transcriptomics (ST) methods are crucial for understanding tissue complexity.
• Existing ST methods face challenges in ease of use, resolution, cost, and 3D scalability.

Purpose of the Study:

• To introduce Open-ST, an open-source, sequencing-based resource for 2D and 3D spatial transcriptomics.
• To address the need for high-resolution, cost-efficient, and scalable ST methods.

Main Methods:

• Development of an open-source experimental and computational framework called Open-ST.
• Application of Open-ST to mouse brain for subcellular resolution transcript capture.
• Utilized Open-ST on head-and-neck tumors and lymph nodes for immune, stromal, and tumor cell population analysis.

Main Results:

• Open-ST achieved subcellular resolution in mouse brain, enabling cell type reconstruction.
• Successfully captured diverse cell populations in tumors and lymph nodes, validated by imaging ST.
• Identified spatial organization of cell states around communication hotspots in tumors.
• 3D reconstruction of metastatic lymph nodes revealed structures and biomarkers at the tumor boundary not visible in 2D.

Conclusions:

• Open-ST offers a versatile and accessible platform for advanced spatial transcriptomics research.
• The method provides high-resolution insights into tissue organization and cellular interactions in both 2D and 3D.
• Open-ST facilitates the discovery of novel biomarkers and understanding of disease mechanisms at a spatial level.