DNA-GPS: A theoretical framework for optics-free spatial genomics and synthesis of current methods

• Published in: Cell Systems Vol. 14; no. 10; pp. 844 - 859.e4
• Main Authors: Greenstreet, Laura, Afanassiev, Anton, Kijima, Yusuke, Heitz, Matthieu, Ishiguro, Soh, King, Samuel, Yachie, Nozomu, Schiebinger, Geoffrey
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 18.10.2023; Elsevier BV
• Subjects: DNA; DNA - genetics; DNA microscopy; Gene Expression Profiling; Genomics; Genomics - methods; manifold learning; spatial transcriptomics; DNA microscopy; spatial transcriptomics; manifold learning
• ISSN: 2405-4712; 2405-4720; 2405-4720
• DOI: 10.1016/j.cels.2023.08.005

While single-cell sequencing technologies provide unprecedented insights into genomic profiles at the cellular level, they lose the spatial context of cells. Over the past decade, diverse spatial transcriptomics and multi-omics technologies have been developed to analyze molecular profiles of tissues. In this article, we categorize current spatial genomics technologies into three classes: optical imaging, positional indexing, and mathematical cartography. We discuss trade-offs in resolution and scale, identify limitations, and highlight synergies between existing single-cell and spatial genomics methods. Further, we propose DNA-GPS (global positioning system), a theoretical framework for large-scale optics-free spatial genomics that combines ideas from mathematical cartography and positional indexing. DNA-GPS has the potential to achieve scalable spatial genomics for multiple measurement modalities, and by eliminating the need for optical measurement, it has the potential to position cells in three-dimensions (3D). [Display omitted] •Spatial transcriptomics methods exhibit trade-offs in resolution and scale•They can be split into optical imaging, positional indexing, and mathematical cartography•DNA-GPS combines ideas from positional indexing and mathematical cartography•It provides a mathematical framework for large-scale optics-free spatial transcriptomics While single-cell sequencing methods capture genomic profiles at the cellular level, they lose spatial context. In the past decade, over a dozen spatial transcriptomics methods have been developed. Greenstreet et al. highlight trade-offs and synergies in existing methods and propose DNA-GPS, a framework for large-scale optics-free spatial transcriptomics.