by 
Spatial genomics transcriptomics is a technique used to simultaneously analyze gene expression patterns across entire tissue sections at single-cell resolution. By mapping where specific genes are expressed within intact tissues, this method can provide insights into tissue architecture and cellular interactions that are not possible with traditional bulk tissue or single-cell RNA sequencing alone. Spatial information is preserved by assigning gene expression values to specific locations on a tissue section.
How Does it Work?
In spatial genomics transcriptomics, mRNA from a thin tissue section is hybridized to a microarray slide that contains spatially barcoded oligonucleotides. Each oligonucleotide contains a spatial barcode and molecular barcode. Upon hybridization, the spatial and molecular barcodes are transferred to mRNA molecules, ‘labeling’ them with positional information from the slide. The slide is then sequenced to identify all mRNA molecules and determine their location of origin. Sophisticated computational methods are then used to build spatially resolved maps displaying where individual genes are expressed across the entire tissue area.
Preserving Cellular Context and Interactions
By maintaining spatial context at a resolution approaching single cells, spatial genomics transcriptomics offers key advantages over traditional non-spatial approaches. It allows observation of heterogeneous cellular compositions within different areas of a tissue as well as interactions between neighboring cell types. For example, one can identify which genes are selectively expressed by immune cells located at the invasive front of a tumor versus those in other areas. Spatial information is also crucial for studying tissues with complex spatial organization like the brain where location indicates cell type identity and function.
Understanding Disease Progression and Response to Therapy
Understanding how gene expression patterns change across tissues and evolve over the course of disease progression or treatment response provides important insights for developing new diagnostics and therapies. Spatial genomics transcriptomics enables such analyses by finely mapping molecular changes associated with processes like fibrosis, necrosis or immune cell infiltration. It has been used to visualize how lung cancer evolves from pre-invasive to invasive stages based on spatial reorganization of multiple signaling pathways. The method can also detect subtle changes in gene expression gradients or boundaries indicative of disease. With its ability to track molecular changes across intact tissues, spatial genomics shows promise for accelerating precision medicine approaches.
Revealing Heterogeneity in Tumor Microenvironments
One of the most promising applications of spatial genomics transcriptomics is in cancer research aiming to dissect the complex interactions between tumors and their microenvironments. Tumors exhibit extensive cellular heterogeneity characterized by distinct subpopulations that evolve dynamically. Spatial genomics helps untangle intratumoral complexity by mapping gene expression profiles of malignant cells, stromal fibroblasts, endothelial cells and diverse immune populations within the same sample. It has revealed previously unappreciated molecular heterogeneity even between spatially proximal regions of the same tumor. Spatial maps of the immune contexture not only localize immune cell types but also uncover their activated functional states – insights critical for immunotherapy. With its unique ability to visualize tumor-immune cell interfaces at high resolution, spatial genomics is an invaluable tool for deciphering how these interactions influence response, resistance and disease progression.
Expanding the Toolkit of Spatial Biology
Since its introduction in 2016, spatial genomics transcriptomics has rapidly developed into a robust and versatile technique that is evolving new applications on a daily basis. Methodological optimizations now allow profiling of sample sizes ranging from organoids and biopsies up to entire organs and model organism embryos with greater sensitivity than initial protocols. Tissue clearing and immuno-staining steps additionally permit correlating gene expression patterns with traditional histology or protein markers. Emerging multiomics variants are poised to integrate spatial transcriptomics with other ‘layers’ of genomic information like DNA methylation, chromatin accessibility or protein abundance within the same samples. With its promise for comprehensively unraveling complex tissue biology, spatial genomics is certain to transform our understanding of development, physiology and disease in the years to come.
*Note:
1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it
