Spatial transcriptomics measures gene activity while preserving the location of the RNA in a tissue sample. Instead of reporting only which genes were detected, it connects those measurements to positions in a tissue section, so researchers can see how expression varies across regions, cells, or neighborhoods.
What spatial transcriptomics adds to gene-expression analysis
In conventional bulk analysis, tissue is homogenized before its RNA is measured. That can reveal which genes are present in the sample, but it loses information about where each transcript came from. Spatial transcriptomics keeps gene-expression measurements linked to tissue coordinates, allowing researchers to compare expression with the sample’s visible structure.
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The foundational method was introduced by Ståhl and coauthors in a 2016 paper. They described a strategy for “visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections.” The authors demonstrated the approach in mouse brain and human breast cancer tissue. Read the paper on PubMed.
How spatial barcodes map RNA to tissue locations
In the original sequencing-based approach, a tissue section was placed on an array of reverse-transcription primers. Each primer carried a unique positional barcode. Messenger RNA from the section was captured and converted into a sequencing library; when the resulting RNA reads were analyzed, their barcodes identified the positions where the transcripts had been collected. Researchers could then align gene counts with the tissue’s two-dimensional layout.
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A representative sequencing-based workflow follows these stages:
- Prepare and section the tissue. The sample is processed according to the assay’s requirements and placed on a capture surface.
- Stain and image the section. The image supplies the anatomical context for interpreting the RNA measurements.
- Capture transcripts. Spatially barcoded probes or primers collect RNA from defined locations in the tissue.
- Build and sequence a library. Sequencing identifies the captured transcripts while retaining their barcode information.
- Map counts back to the image. Software uses the spatial barcodes to align gene-expression measurements with tissue coordinates.
The chemistry and sample preparations supported vary by assay. 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 are examples, not universal preparation rules; experiments need to follow the current protocol for the chosen platform. See 10x Genomics’ overview of spatial transcriptomics and spatial biology and its Visium tissue imaging guidelines.
Sequencing-based and imaging-based approaches
Both approaches preserve spatial information, but they measure RNA differently. Sequencing-based methods can support broad transcript discovery across tissue regions. Imaging-based methods detect selected transcripts directly in place, using gene-specific probes or other optical signatures. Repeated imaging or decoding reveals where those targets occur. Imaging can provide cell-boundary or subcellular localization, but its measurements generally cover a chosen panel rather than every transcript.
| Consideration | Sequencing-based methods | Imaging-based methods |
|---|---|---|
| Gene breadth | Can support whole-transcriptome discovery. | Generally measures a targeted gene panel. |
| Spatial detail | Varies by technology, from multi-cell spots to finer spatial units. | Can localize selected transcripts at cell or subcellular detail. |
| How signal is measured | Captured RNA is sequenced; spatial barcodes link reads to positions. | Probes or optical signatures detect target transcripts in situ. |
| Key trade-off | Broad discovery must be balanced against spatial granularity and detection. | Fine localization applies to selected targets, so panel design matters. |
These are broad distinctions, not guarantees for every assay. Platform chemistry, tissue preparation, and analysis workflow affect the result. The National Cancer Institute’s spatial transcriptomics guidance discusses practical considerations for choosing methods.
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A method’s nominal resolution is not the same as the effective detail in its data. Capture efficiency, sequencing depth, panel design, tissue properties, and molecular diffusion can all affect which transcripts are detected and how precisely their locations can be interpreted. A location may represent multiple cells or a tissue region rather than a single cell, depending on the method.
A 2024 systematic comparison of 11 sequencing-based spatial transcriptomics methods reported that molecular diffusion varied across methods and tissues and significantly affected effective resolution. The study also noted that spatial data capture can be influenced by sequencing depth and resolution. Its findings caution against comparing resolution labels or gene counts across platforms as if they were measured under identical conditions. Read the comparison in Nature Methods.
- More spatial detail is not automatically more informative. Higher nominal resolution can be accompanied by sparse counts or dropout, which can make rare or low-abundance subpopulations harder to distinguish.
- A map does not identify cell types by itself. Spatial measurements require quality control and interpretation; a detected location is not, on its own, proof of a cell identity or biological interaction.
- Sample compatibility matters. Preservation method and tissue type can constrain which assay is suitable.
How to choose an approach
Start with the biological question, then choose the method whose trade-offs fit the sample and the analysis the team can perform. The National Cancer Institute recommends considering tissue type, sample size, and whether the study needs detail about particular cell types or niches versus a broader spatial pattern.
- What tissue do you have? Check whether the method supports its tissue type and preservation state.
- How large is the sample? Sample area and assay coverage affect whether the method can capture the region of interest.
- Do you need broad discovery or a selected set of genes? A sequencing-based approach may suit broad discovery; imaging may suit focused measurement of selected targets.
- What spatial scale answers the question? Decide whether a regional pattern is sufficient or whether cell-level or subcellular localization is necessary.
- Can the study support the analysis? Plan for image registration, quality control, gene-count analysis, and spatial interpretation; some workflows require specialized data-science skills.
Spatial transcriptomics is most useful when the location of gene expression is part of the question—not simply when a study needs another way to count transcripts. The result is a spatially anchored measurement that must still be evaluated in light of assay design, tissue, and detection limits.
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