Spatial genomics transcriptomics (SGT) is a novel technique that allows researchers to map where RNA is expressed across intact tissue sections at single-cell resolution. Traditional transcriptomics techniques like RNA sequencing provides gene expression profiles for entire tissues or cell populations but loses all spatial information about where specific genes are expressed within the tissue. SGT overcomes this limitation by capturing both the spatial location and molecular signatures of thousands of cells simultaneously without having to isolate individual cells.
How Does it Work?
In SGT, an intact fresh frozen or fixed tissue section is mounted on a slide coated with an array of oligonucleotide-conjugated beads. Each spot on the array contains millions of identical oligonucleotides programmed with a spatial barcode and unique molecular identifiers (UMIs). The tissue is then permeabilized so that cellular RNA can diffuse out and hybridize to the oligonucleotides on the array. After washing away unbound RNA, the spatial positions as well as transcripts of thousands of cells are captured by the array. The slide is then processed to generate a cDNA library where each cDNA molecule represents a single RNA molecule from a single cell, tagged with the spatial barcode and UMI for later identification. High-throughput sequencing is then used to decode the spatial map of gene expression across the entire tissue section at single cell resolution.
Advantages over Traditional Techniques
One of the biggest advantages of SGT compared to traditional techniques like fluorescent in situ hybridization (FISH) or immunohistochemistry (IHC) is that it allows unbiased detection of all expressed genes in a tissue rather than being limited to just a few pre-selected targets. SGT provides a digital gene expression profile for each spatially resolved cell across the whole tissue section rather than just detecting the presence or absence of specific mRNAs/proteins of interest.
Another major advantage is that SGT preserves the cellular context and spatial organization of the original tissue architecture. This fine-grained spatial information is lost in bulk dissociation-based techniques like single-cell RNA sequencing where isolated single cells are randomly dispersed during downstream processing and analysis. SGT therefore maintains crucial localization data about gene expression patterns within different tissue compartments, cellular interactions, and gradients that would otherwise be impossible to determine.
SGT also offers a more comprehensive overview of a tissue's transcriptomic landscape compared to traditional methods like IHC that can only detect one marker at a time. And unlike FISH, SGT does not require prior knowledge of which genes to interrogate - it allows unbiased interrogation of all expressed genes simultaneously. This makes SGT especially useful for discovery-based studies exploring new biomarkers, cellular subtypes, and biological gradients within tissues.
Applications
Since its development in 2018, SGT has already found various applications in neuroscience, immunology, and cancer research. In neuroscience, SGT has been used to map the precise expression patterns of thousands of genes across whole mouse brain sections to characterize distinct neuronal and glial cell subtypes. In cancer, SGT has revealed novel intratumoral heterogeneity profiles and immune cell infiltration patterns in breast cancer. In immunology, SGT mapped cytokine gradients and immune cell interactions within lymph nodes, spleen and gut tissues at single cell resolution.
Ongoing method optimization is also extending SGT applications to more challenging sample types like FFPE preserved tissues. Newer versions like Merfish allow multiplexed detection of up to 100 targets, further expanding the technique's discovery potential. Combining SGT with immunohistochemistry, fluorescence, or mass spectrometry could integrate spatially resolved molecular profiles with cellular morphology and protein localization data. Integration of SGT maps with techniques like ATAC-seq may also help identify cell type-specific regulatory elements and characterize 3D genome organization across tissues.
Conclusion
In summary, spatial genomics transcriptomics is ushering a new era of spatially resolved single cell omics by simultaneously capturing both the spatial positions and molecular signatures of thousands of cells within intact tissues. Compared to traditional techniques, SGT offers an unprecedented level of topological, cellular, and molecular detail about normal and diseased tissues. This digital and unbiased spatial mapping of whole transcriptomes promises novel insights into tissue biology, disease pathogenesis, biomarker and cell type discovery across various biomedical research areas. As the technique continually evolves, SGT is set to become a powerful routine tool for spatially resolved multi-omics characterization of complex tissues.
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