Spatial genomics is an emerging field that allows researchers to study gene expression patterns across tissues at an unprecedented resolution. By mapping where RNA or DNA is located within a sample, spatial techniques shed light on important biological relationships that are obscured in traditional bulk assays. Here we review the principles and potential of major spatial genomics methods, with a focus on spatial transcriptomics.
Spatial Transcriptomics Methods
One of the most widely used spatial genomics techniques is spatial transcriptomics, which enables high-throughput spatial gene expression profiling from tissue sections. In a typical spatial transcriptomics experiment, thin slices of fresh frozen tissue are placed on a glass slide coated with an array of DNA capture probes arranged in a grid pattern. The tissue is then lysed to release RNA, which can hybridize to the DNA spots based on sequence complementarity. The location of each spot indicates where the RNA originated from within the tissue. After sequencing, the expression levels of thousands of genes can be mapped back to their precise locations, generating a comprehensive molecular map of the tissue.
A key advantage of spatial transcriptomics is that it preserves spatial context without needing to image individual cells. This makes it applicable to heterogeneous complex tissues where large numbers of cell types interact. It also does not require isolating individual cells, circumventing issues with dissociation and loss of spatial context during single-cell dissociation protocols. The trade-off is reduced resolution compared to techniques like single-cell RNA sequencing, which profile gene expression in individual cells. However, spatial transcriptomics still achieves cell-level resolution for many tissues.
New Applications in Disease Research
Spatial genomics is accelerating research into disease pathways by untangling complex spatial relationships in affected tissues. For example, scientists have used spatial transcriptomics to map out immunological landscapes in tissues affected by conditions like influenza infection, colitis and skin disorders. This has provided new insights into how different immune cell types interact and respond over the course of disease progression within 3D tissue microenvironments. Spatial techniques are also being applied to profile the tumor microenvironment at high resolution, with studies revealing heterogeneous immune responses and non-cell-autonomous effects on nearby normal cells. By understanding the crosstalk between different cell types in disease contexts, spatial approaches hold promise for developing more effective immunotherapies and treatment strategies.
Resolving Cell Types from Spatial Data
A key analytical challenge for spatial genomics is teasing apart different cell types from expression data without prior cellular identity information. Computational methods have been developed that leverage signature gene sets and spatial clustering patterns to infer cell types present within a tissue. For example, researchers have used spatial transcriptomics data to deconvolve complex brain regions into distinct neuronal and glial populations. Other algorithms employ machine learning on cell type signature genes to automatically annotate cell identities across large and diverse datasets. The growing availability of single-cell reference atlases also aids refinement of cell type predictions from spatial data. Combined with histological validation, these computational approaches enable researchers to resolve heterogeneous cellular mixtures and identify rare cell populations based on spatial gene expression patterns alone.
Single-Cell Validation and Multi-omics Integration
While spatial techniques achieve cell-level resolution without single-cell dissociation, integration with single-cell profiling provides important validation and additional dimensionality. Single-cell RNA sequencing of cells manually isolated from defined locations within spatial transcriptomics datasets allows verification of predicted cell types and characterization of cell states. It also captures additional dimensions like splicing variability that are averaged out at the spatial resolution. In addition, spatial proteomics and epigenomics methods are being integrated with RNA-based approaches. For instance, paired spatial transcriptomics and chromatin accessibility profiling has yielded novel insights into 3D genome architecture and long-range gene regulatory interactions in situ. Going forward, multi-omics profiling at matched single-cell and spatial resolution promises to revolutionize our understanding of tissue organization and disease pathogenesis.
Conclusion
In summary, spatial genomics is a transformative suite of technologies that reveal the genomic architecture of tissues at unprecedented detail. Of the various approaches, spatial transcriptomics has emerged as a powerful workhorse method due its high-throughput and cytoplasmic RNA-centric profiling. Its application to diverse biological and medical questions is revealing complex regulatory interactions between cells in healthy and diseased contexts. Combining spatial techniques with single-cell methods and multi-omics readouts holds great potential to illuminate the molecular logic of tissue organization. Overall, spatial genomics is a burgeoning field catalyzing major advances in our understanding of localization-dependent biological processes.
