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Spatial transcriptomics measures gene activity while preserving the location of the RNA in a tissue sample. Sequencing-based methods use spatial barcodes to map captured transcripts back to tissue coordinates; imaging-based methods detect selected transcripts directly in place. The resulting gene-expression map can be read alongside tissue structure and the cells or regions around each measurement.
Why location changes gene-expression analysis
In conventional bulk gene-expression analysis, tissue is homogenized before its RNA is measured. That reveals which genes are present across the sample, but loses information about where each RNA molecule came from. Spatial transcriptomics retains that link between gene-expression measurements and tissue position, helping researchers examine how expression varies across regions and tissue neighborhoods.
The foundational 2016 study described the approach as a way to visualize and quantitatively analyze the transcriptome with spatial resolution in tissue sections. Its authors demonstrated two-dimensional positional information in mouse brain and human breast cancer sections. Read the paper on PubMed.
How spatial barcodes map RNA to tissue coordinates
The original sequencing-based approach
In the original method, a tissue section was placed on an array of reverse-transcription primers. Each primer carried a unique positional barcode. Messenger RNA from the tissue was captured and converted into a sequencing library. When the RNA was sequenced, its associated barcode indicated the position on the array where it had been captured. Researchers could then align gene counts to the tissue section and interpret them with the tissue image.
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A representative sequencing workflow
- Prepare and section the tissue. The preparation must suit the assay and the tissue-preservation method it supports.
- Stain and image the section. The image provides the anatomical context used to interpret the expression map.
- Capture RNA with spatially barcoded probes. The barcode records the capture location for each measured transcript.
- Build and sequence a library. Sequencing identifies the captured RNA; the spatial barcode connects those reads to positions.
- Align counts to the tissue image. Image registration and analysis place gene-expression measurements in their tissue context.
Exact chemistry and compatible preparations vary by platform. For example, 10x Genomics describes poly(A)-based capture for its fresh-frozen Visium Gene Expression assay and a probe-based CytAssist assay for fresh-frozen, fixed-frozen, or FFPE human and mouse tissue. These examples are platform-specific; experimenters should follow the current protocol for the assay they use. 10x Genomics’ overview of spatial transcriptomics and spatial biology.
Sequencing-based and imaging-based methods compared
Both approaches connect gene expression to tissue position, but they make different trade-offs. Sequencing-based methods can support broad transcript discovery, while imaging-based methods typically focus on selected genes and locate them directly in the tissue.
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| Consideration | Sequencing-based methods | Imaging-based methods |
|---|---|---|
| How transcripts are measured | Captured RNA is sequenced; spatial barcodes link measurements to tissue coordinates. | Gene-specific probes or other optical signatures detect transcripts in place, with imaging or decoding used to determine their positions. |
| Gene breadth | Can offer whole-transcriptome discovery, depending on the assay. | Generally measures a targeted panel of selected genes. |
| Spatial detail | Varies by technology, from spots that may cover multiple cells to finer spatial units. | Can localize selected transcripts at cell-boundary or subcellular detail. |
| Key trade-off | Broad discovery comes with resolution and detection characteristics that depend on the technology and experiment. | Detailed localization is available for the selected panel, rather than an unrestricted transcriptome-wide readout. |
These are broad method categories, not guarantees about every assay. The National Cancer Institute’s guidance emphasizes choosing a method in light of the tissue, sample size, desired resolution, and analysis needs. NCI guidance on spatial transcriptomics.
What affects resolution and detection?
A platform’s stated or nominal resolution is not the only factor that determines what can be distinguished in the resulting data. Tissue properties, assay chemistry, capture efficiency, sequencing depth, panel design, and molecular diffusion all influence the observed signal. A spatial measurement may represent a region or several cells rather than one cell, depending on the method.
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A 2024 systematic comparison examined 11 sequencing-based spatial transcriptomics methods. The study reported that molecular diffusion varied across methods and tissues and affected effective resolution; it also noted that sequencing depth and resolution influence spatial data capture. The 2024 comparison in Nature Methods.
- Higher nominal resolution does not automatically mean that every cell or low-abundance transcript will be detected.
- Sparse counts and dropout can make rare or weakly expressed subpopulations difficult to distinguish.
- Resolution labels and gene counts should not be treated as directly comparable across platforms without checking the study conditions and assay details.
How to choose a method for a research question
Begin with what the experiment needs to reveal, then check whether the assay and analysis workflow can answer it.
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- Tissue type and preservation: Confirm that the assay supports the tissue and whether it is fresh-frozen, fixed-frozen, or FFPE.
- Sample area: Consider the size of the tissue section and the area that must be mapped.
- Desired detail: Decide whether the question requires cell-level or subcellular localization, or whether a broader spatial pattern is sufficient.
- Gene breadth: Choose between broad transcript discovery and focused measurement of a selected gene panel.
- Detection needs: Consider whether the genes of interest are expected to be abundant enough for the assay and experimental depth.
- Analysis capacity: Plan for image registration, quality control, gene-count analysis, and spatial interpretation; some workflows require specialized data-science skills.
Spatial maps are measurements, not self-interpreting cell identities or proof that neighboring cells are biologically interacting. Their interpretation depends on the assay’s spatial scale, data quality, tissue image, and the question being asked.
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