Spatial transcriptomics is an advanced scientific technique that allows researchers to observe gene activity within individual cells while preserving their original location inside a tissue. It combines the detailed molecular information of genomics with the visual context provided by microscopy. This method helps scientists understand how genes are expressed across different regions of a tissue, which is not possible with older techniques that break down the tissue. By mapping gene expression directly onto a tissue image, spatial transcriptomics offers a more complete picture of biological processes and cellular interactions.
Preparing Tissues for Spatial Analysis
Tissue preparation is the first step in spatial transcriptomics, ensuring structural integrity and molecular information. Tissue samples undergo fixation, a process that preserves the tissue’s architecture and prevents degradation. These fixed tissues are then embedded in a supporting medium, like paraffin or optimal cutting temperature (OCT) compound, which provides stability for subsequent slicing.
Following embedding, the tissue is sectioned into thin slices. These sections are then placed onto specialized glass slides for spatial analysis. Some advanced workflows, such as Visium CytAssist, allow direct sectioning onto standard glass slides, which are later transferred to the specialized slides for probe hybridization. This ensures that the spatial relationships of cells and their molecular contents are maintained.
Capturing and Sequencing RNA in Tissue
Once tissue sections are on specialized slides, spatial transcriptomics begins to capture gene activity. Messenger RNA (mRNA) molecules are released from cells within the tissue section. These slides are covered with spatially barcoded probes. Each probe identifies its exact location on the slide.
The released mRNA molecules bind to these spatially barcoded probes, tagging them with their precise coordinates within the tissue. This ensures that when the mRNA is sequenced, its original location can be reconstructed. The tagged mRNA molecules are converted into complementary DNA (cDNA) through reverse transcription, incorporating the spatial barcodes. These cDNA molecules are prepared for high-throughput sequencing, linking gene expression to specific spatial points.
Analyzing Spatial Transcriptomics Data
After sequencing, specialized computational analysis extracts meaningful biological insights from the generated data. The initial steps involve pre-processing, including quality control checks to ensure data accuracy and image registration to align the sequencing data with the original tissue image. This alignment allows the reconstruction of a spatial map of gene activity.
Specialized software tools are then used to map the sequenced gene expression data back onto the tissue image, creating a visual representation of where genes are active. Subsequent analytical steps include normalization to correct for technical variations, dimensionality reduction to simplify complex datasets, and clustering to group regions or cells based on similar gene expression profiles. These computational methods help identify different cell types, discover spatially distinct gene expression patterns, and visualize how gene activity varies across the tissue.
Unlocking Biological Insights
Spatial transcriptomics impacts biological understanding by providing a complete view of gene activity within its tissue context. This technology allows researchers to generate detailed transcriptome maps, which reveal how gene expression profiles connect to specific cellular locations and tissue morphology. For instance, in cancer research, it helps scientists examine tumor heterogeneity and the complex interactions within the tumor microenvironment, offering insights into tumor evolution and potential resistance to therapies.
In developmental biology, spatial transcriptomics enables the tracing of cellular differentiation and the reconstruction of complex developmental processes. It also aids in understanding neurological diseases by mapping gene expression patterns in specific brain regions. By combining molecular data with spatial information, this technology helps identify disease mechanisms, accelerate drug discovery by pinpointing spatially regulated drug targets, and contribute to precision medicine by characterizing tissue-specific gene expression.