How Does Spatial Transcriptomics Work?

Every cell in a tissue has a story to tell, and understanding that story means knowing not just what is being said, but where it is being said. Spatial transcriptomics is an emerging method that integrates gene expression data with spatial context, allowing researchers to investigate how cells function, interact, and organize within intact tissue environments.

Instead of isolating RNA from single cells and analyzing it, spatial transcriptomics captures gene activity directly from intact samples at single-cell resolution, preserving the biological structure researchers need to understand complex systems. This approach unlocks new perspectives across biology, from mapping disease pathways to exploring how different cell types shape healthy function.

While different spatial transcriptomics platforms use slightly different chemistries, the core process follows these general steps:

• 1. Preparing and placing the tissue
  
• 🧪What happens:
  • Tissue is thinly sectioned and mounted onto a specially treated slide that supports RNA preservation
    and probe access. Depending on the platform, the surface may be patterned with spatial barcodes or
    optimized for direct hybridization.
• 💡Why it matters:
  • This step preserves both the structural integrity of the tissue and the accessibility of RNA
    molecules, ensuring that spatial information and transcript signals remain intact throughout the
    process.

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• 2. Labeling RNA molecules
  
• 🧪What happens:
  • Fluorescently labeled or barcoded probes hybridize to RNA transcripts inside the tissue. These
    probes are designed to detect specific genes, often using sequential hybridization rounds to capture
    large gene sets.
• 💡Why it matters:
  • This enables high-plex profiling, which allows researchers to detect hundreds or thousands of genes
    in their original locations rather than relying on bulk expression averages.

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• 3. Imaging the sample
  
• 🧪What happens:
  • The tissue is imaged through high-resolution fluorescence microscopy. Each round of imaging captures
    a subset of transcripts, and the process repeats until all targets are visualized.
• 💡Why it matters:
  • Precise imaging preserves spatial resolution and ensures that transcripts are accurately localized.
    This is essential for downstream decoding and spatial analysis.

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• 4. Decoding and mapping transcripts
  
• 🧪What happens:
  • Software decodes the fluorescence signals into gene identities and assigns each transcript to a
    specific location within the tissue, producing a spatial gene expression map.
• 💡Why it matters:
  • The resulting maps reveal where genes are active, offering a view into the molecular landscape of
    intact tissues. This spatial insight provides a foundation for biological interpretation.

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• 5. Analyzing spatial patterns
  
• 🧪What happens:
  • Researchers examine gene expression across tissue regions to identify spatial domains, molecular
    gradients, or cell-to-cell interactions.
• 💡Why it matters:
  • By placing gene activity in spatial context, scientists can uncover biological mechanisms that are
    hidden in dissociated or averaged data, from developmental programs to disease progression.

By preserving the spatial context of gene activity, this method allows scientists to study tissue biology in a way that traditional RNA-sequencing approaches cannot. It reveals not only which genes are active, but also how cells relate to one another in their native environment.

By preserving spatial context, spatial transcriptomics uncovers patterns of gene expression that reveal how cells function together in complex tissues. Across a growing range of disciplines, this approach is transforming how scientists explore health and disease, but it is especially useful in the following fields:

• Cancer research
  Spatial maps of tumors reveal the cellular makeup of the microenvironment, including immune infiltration, stromal composition, and tumor heterogeneity. These insights are helping researchers develop more targeted and effective therapies.
• Neuroscience
  With the brain’s tightly organized structure, spatial transcriptomics is uniquely suited to identify neuronal subtypes, trace molecular signatures across regions, and study gene activity linked to development or degeneration.
• Drug discovery
  Understanding where and how genes respond to treatment can reveal tissue-specific effects and unintended targets. These insights support more selective, localized, and effective therapeutic strategies.
• Developmental biology
  Spatial transcriptomics helps map how gene expression patterns change over time and across tissue layers in the developing embryo. This reveals how cells acquire identity, migrate, and organize into complex structures during early development.
• Regenerative medicine
  Tracking gene expression during tissue repair sheds light on how stem cells differentiate and how the local environment shapes regeneration, especially in complex or damaged tissues.
• Multi-modal integration
  Spatial transcriptomics is increasingly used alongside single-cell RNA sequencing, proteomics, and imaging to add depth and dimensionality to biological analysis, linking molecular identity to physical structure.

Spatial transcriptomics is even more powerful when combined with complementary technologies. The table below highlights several common pairings, when to use them, and the insights that can be gained by layering spatial context with other molecular data.

Technology	When to Pair with Spatial Transcriptomics	Benefits of Pairing
Single-cell RNA sequencing (scRNA-seq)	When you need high-resolution molecular profiles of individual cells to complement spatial context	Combines molecular detail with spatial location for a deeper understanding of cell states and types
Proteomics	When studying how protein expression patterns align with transcriptomic activity across tissue	Reveals how mRNA and protein expression co-localize, supporting biomarker discovery and multi-layered functional analysis.
ATAC-seq (chromatin accessibility)	When analyzing how gene regulation and chromatin states correlate with spatial gene expression	Links regulatory landscapes with spatial gene expression patterns to reveal gene control mechanisms
Imaging mass cytometry	When looking to visualize multiple protein markers alongside gene expression in complex tissues	Enables high-plex spatial analysis of both RNA and proteins in situ
CRISPR-based perturbation screens	When evaluating how genetic perturbations affect gene expression and spatial cell organization	Connects functional genetic outcomes to spatial transcriptomic changes within tissues

Vizgen’s spatial transcriptomics platform is built on MERFISH 2.0, a technology that combines high multiplexing, single-molecule sensitivity, and subcellular resolution. Unlike methods that rely on physical barcoding arrays or limited gene panels, MERFISH uses combinatorial labeling and error-robust barcoding to profile hundreds to thousands of genes in each sample accurately.

What makes this approach unique is its ability to scale without sacrificing precision. With the MERSCOPE Ultra platform, researchers can capture spatial gene expression across large tissue sections, up to 3 cm², with automated imaging and data analysis. The result is a workflow that delivers detailed spatial maps of RNA distribution while remaining flexible for different tissue types, experimental designs, and research goals.