Spatial genomics is an emerging field that combines spatial mapping with molecular profiling to characterize cells within tissues. By applying genomics techniques directly to intact tissues rather than dissociated cells, spatial genomics allows researchers to study how gene expression varies across cell types and locations within complex tissues. This provides insights into how cells interact and organize into functional units. Over the past decade, new techniques like spatial transcriptomics have begun to transform our understanding of tissue organization and cellular heterogeneity.
The Development of Spatial Transcriptomics
One pioneering spatial genomics technique is called Spatial Genomics Transcriptomics, developed in 2016 by researchers at the Royal Institute of Technology in Stockholm. Unlike previous methods that required dissociating tissues, spatial transcriptomics performs gene expression profiling directly on intact tissue sections. The technique works by immobilizing cellular RNAs onto a gel surface containing spatial barcodes, with each barcode encoding the position of the cell whose RNAs are captured. The resulting "spatially resolved transcriptomes" map which genes are expressed in each location across the tissue.
This method has several major advantages over previous techniques. It preserves the native context and organization of tissue, revealing how gene expression patterns vary between neighboring cells. It also does not require isolating individual cells, allowing a far greater number of cells to be analyzed from a single tissue sample. Early applications of spatial transcriptomics revealed new insights into tissue structure and biology in the retina, lymph node, and brain. The technique has since been further developed and applied to many other tissues.
Uncovering New Cell Types through Spatial Genomics Transcriptomics
One key application of spatial transcriptomics is discovering novel cell types that were previously hidden within complex tissues. By analyzing gene expression patterns at high spatial resolution, researchers have identified rare and poorly understood populations that traditional bulk tissue profiling would group together.
In the brain, spatial transcriptomics has revealed new subclasses of neurons and glia with distinct expression profiles. Similarly, studies of the skin, lungs, and intestine have found previously unrecognized cell populations involved in immune responses, barrier functions, and other roles. Characterizing these cellular subtypes is helping explain complex tissue organization and physiology. Spatial analysis has also uncovered gradients and transitions in cell states, giving insights into differentiation pathways.
Elucidating Tissue Architecture and Cell Interactions
Beyond cell type identification, spatial genomics is enhancing understanding of tissue architecture and how cells interact to form functional units. Studies mapping tens of thousands of gene expression profiles within intact tissues are elucidating organizational principles. Analysis of the brain, for example, has revealed molecular signatures associated with layered cortical structures, delineating interconnected networks involved in sensory and motor functions.
Spatial studies are also providing insights into cellular interactions. Analyzing gene expression patterns between neighboring cells in the lymph node, lung, and other tissues has uncovered paracrine signaling events that coordinate immune responses, gas exchange, and other cooperative functions. Spatial profiling is also revealing how disruption of architecture influences disease, such as the localized inflammatory response in COVID-19 lung injury. These findings have important implications for characterizing tissue dynamics in health and disease.
The Rise of Multi-Omics Spatial Technologies
The field of spatial genomics continues to rapidly progress, with new technologies expanding the type of molecular information that can be analyzed with spatial resolution. Alongside spatial transcriptomics, techniques have emerged for mapping other omics layers like the epigenome, proteome, and metabolome within intact tissue context.
For example, new methods integrate spatial transcriptomics with immunohistochemistry to simultaneously profile gene expression and protein localization. Other approaches are exploring spatially resolved epigenetic signatures like DNA methylation and histone modifications. Integrating multi-omics data provides a more multidimensional view of cell state and identity. It also enhances characterization of regulatory networks, modification of gene expression programs, and other dynamic biological processes.
Looking Ahead
As technologies like Spatial Genomics Transcriptomics become more accessible and widely applied, they are revolutionizing our understanding of tissue organization and cellular systems. By preserving native tissue structure, spatial omics is revealing organizational principles, cell types, and interactions that were previously hidden from bulk analysis. When combined with other spatially resolved data types, it is providing multidimensional portraits of biological systems. Going forward, spatial genomics promises to transform fields like developmental biology, immunology, neuroscience and more by elucidating how cells coordinate functions within complex tissues. With ongoing methodological innovations, its potential for new insights into health and disease is just beginning to be realized.
