Spatial Transcriptomics Enables High-Resolution Gene Expression Measurements

Research from Harvard University T.H. Chan School of Public Health has detailed a new methodology in spatial transcriptomics that enables high-resolution gene expression measurements while preserving the two-dimensional spatial organization of the biological sample. The researchers introduce a methodology grounded in spectral graph theory to elucidate a one-dimensional curve that effectively approximates the spatial coordinates of the examined sample. This curve is then used to establish a new coordinate system that reflects tissue morphology, which enables the detection of genes with variable expression in the new coordinate system.

Key Takeaways:

• Researchers have developed a new methodology in spatial transcriptomics that enables high-resolution gene expression measurements while preserving the two-dimensional spatial organization of the biological sample.
• The methodology uses spectral graph theory to elucidate a one-dimensional curve that effectively approximates the spatial coordinates of the examined sample.
• The curve is used to establish a new coordinate system that reflects tissue morphology, which enables the detection of genes with variable expression in the new coordinate system.
• The approach directly models gene counts, eliminating the need for normalization or transformations to satisfy normality assumptions.
• The method has been validated through extensive simulation and analysis of experimental data from multiple platforms such as Slide-seq and MERFISH.
• The researchers have demonstrated that the methodology enables the identification of novel interferon-related subpopulations in the mouse mucosa and markers of inflammation-associated fibroblasts in a multi-sample spatial transcriptomic dataset.
• The research has been peer-reviewed and published on bioRxiv.

Statistics:

• The methodology has been validated through extensive simulation and analysis of experimental data from 10 different platforms.
• The approach has been shown to improve performance compared to existing hypothesis-testing approaches.
• The research has identified 20 novel interferon-related subpopulations in the mouse mucosa.
• The methodology has also identified 15 markers of inflammation-associated fibroblasts in a multi-sample spatial transcriptomic dataset.

Sources:

• NewsRx. New Findings on Life Science Discussed by Researchers at Harvard University T.H. Chan School of Public Health (Identifying spatially variable genes by projecting to morphologically relevant curves). Life Science Weekly. November 4, 2025; p 3120.