Spatial genomics transcriptomics is a revolutionary technique developed in recent years to simultaneously map the spatial distribution of cells and measure genome-wide gene expression levels at high resolution within intact tissues. This technique builds upon earlier spatial mapping methods but achieves single-cell level resolution for the first time. By combining spatial information with gene expression profiles, it allows us to understand how cells interact and work together in their native tissue environment.

The Technique Behind Spatial Genomics Transcriptomics

The key innovation behind spatial genomics transcriptomics is the use of DNA or RNA barcode arrays to spatially label genes expressed within a tissue sample. In this technique, a tissue sample such as a thin slice of brain, immune system node or tumor is placed on a glass slide coated with an array of oligonucleotide barcodes. As cells within the tissue naturally degrade and release their RNA contents, the RNA gets captured by the nearby barcodes on the array. After sequencing, the location and identity of each captured RNA molecule can be revealed based on the barcode it is associated with. This allows building up spatially resolved maps of where specific genes are expressed within the intact tissue architecture.

Advantages Over Previous Spatial Mapping Methods

Prior spatial mapping techniques such as in situ hybridization could detect only a limited number of genes or relied on immunofluorescent detection of selected proteins. While useful for studying expression of known marker genes, they did not provide a comprehensive view of all genes expressed within tissues. Techniques like Geo-Seq and SPRITE that applied random barcodes had low resolution, with each barcode representing an area containing multiple cell types. In contrast, spatial genomics transcriptomics achieves single cell resolution by using dense arrays of spatially distributed barcodes. It can simultaneously analyze expression of thousands of genes, revealing novel gene expression patterns and cell type signatures within intact tissues.

Applications and Insights Provided

Researchers have already employed spatial genomics transcriptomics to gain new understandings in various disease contexts. In neuroscience, it has mapped out region-specific gene expression programs in the intact mouse brain at an unprecedented scale. In cancer research, it has uncovered diverse tumor-infiltrating leukocyte populations and their spatial distributions in brain cancer samples. In immunology, it has revealed organization of immune cell subsets and their gene activities within lymphoid organs. Going forward, as reference maps are created for more species and tissues, it promises to transform our understanding of tissue organization, cell-cell communication networks and disease pathogenesis. Researchers will be able to investigate previously hidden layers of complexity in gene regulation influenced by the precise spatial microenvironment of cells.

Technical Advances and Future Potential

The original technique employed arrays of short DNA barcodes to achieve 100-200 micron resolution. Further technical optimizations have improved the resolution to below 20 microns, nearing single-cell levels. Longer or molecularly barcoded arrays will allow scaling the technique to sequence whole organs or even entire model organisms. Coupling it with multiplexed fluorescence imaging could add protein-level validation to gene expression profiles. As analysis algorithms and reference maps grow in sophistication, spatial genomics transcriptomics spatial genomics transcriptomics holds immense promise to revolutionize systems biology research and accelerate new biomarker and drug target discovery efforts. By taking a more comprehensive view of how genes regulate each other within intact tissue organization, it may yield new strategies for intervening in diseases as complex networks.