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Choose a spatial transcriptomics platform by matching the assay to your biological question and the specimens you can provide—not by choosing the smallest advertised feature size or the longest gene list. Sequencing-based methods generally suit broad discovery and regional analysis; imaging-based methods generally suit detailed localization of a selected gene panel. Then check specimen compatibility, effective resolution, detection performance, throughput, analysis capacity and total study cost before committing.
What do you need the assay to tell you?
Write the biological question as a decision about what information matters most. Are you looking for previously unknown expression patterns across a tissue, comparing tissue domains or niches, or locating a known set of markers within a smaller area? The answer determines whether broad transcriptome coverage or detailed in-place detection of selected genes is more useful.
The National Cancer Institute (NCI) frames the broad distinction this way: “In general, use imaging-based ST if you need a lot of detail for a small area; use sequencing-based ST if you’re more interested in regional results (domain or niche-level analysis).” Treat that as a starting point, not a rule that settles every experiment. Tissue, panel, workflow and analysis can change what a platform can answer.
For broad discovery
If you do not yet know which genes or cell states matter, prioritize an approach with the breadth needed to discover patterns rather than assuming a predefined panel will be sufficient. Sequencing-based approaches capture spatially barcoded transcripts and read them by sequencing; some support broader transcriptome coverage. Confirm the exact assay version and whether its coverage fits your specimen and question.
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For a focused localization question
If the key question is where selected genes are expressed, an imaging-based assay may provide useful in-place detail for a defined panel. Check whether the available panel includes the genes you need and whether its usable tissue area and resolution fit the experiment. A highly detailed result for a small area may not substitute for broad coverage across a tissue.
How do the main assay families differ?
| Approach | How it detects transcripts | Often a better fit when | Check before choosing |
|---|---|---|---|
| Sequencing-based | Spatially barcoded transcripts are captured and read by sequencing. | You want broader discovery or regional/domain-level analysis. | Coverage of the exact version, specimen compatibility, spatial unit, sensitivity, sequencing needs and analysis workload. |
| Imaging-based | Fluorescent probes detect transcripts in place through sequential imaging. | You want detailed localization of a selected set of genes, often in a smaller area. | Panel composition, tissue compatibility, usable area, detection background, segmentation and imaging throughput. |
| ROI-oriented spatial profiling | GeoMx Digital Spatial Profiler is included in the broader spatial-profiling landscape as an ROI-oriented approach. | Your design is organized around selected regions of interest. | Whether its specific assay and output support the biological comparison you intend. The evidence summarized here does not establish a universal performance or cost ranking. |
These are method-level distinctions, not guarantees about every product or version. Commercial options named in the available comparisons include 10x Genomics Visium and Visium HD and BGI Stereo-seq among sequencing-based approaches, and 10x Genomics Xenium, NanoString CosMx SMI and Vizgen MERSCOPE among imaging-based approaches. Exact chemistry, panel options, tissue compatibility, throughput and instrument configuration vary; check current product documentation for the configuration you would actually use.
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Will your specimen work with the assay?
Confirm specimen feasibility before comparing instruments. Fresh-frozen and formalin-fixed, paraffin-embedded (FFPE) tissues are not interchangeable assumptions: compatibility depends on the assay version and workflow. Species, tissue type, fixation and processing history also matter. A platform-family label alone does not prove that your particular specimen is supported.
- Record the specimen type, species, tissue, fixation and processing history for every sample in the proposed study.
- Check current compatibility documentation for the exact assay version and preparation workflow—not just the vendor’s general platform page.
- Ask whether the planned sections, regions and sample quality meet the assay’s requirements, and how failure or poor-quality samples are handled.
- If specimens are scarce or difficult to replace, confirm feasibility with the platform provider or an experienced core before committing the full set.
What does “resolution” mean for your experiment?
Do not equate a small feature size with a complete, reliable single-cell profile. The spatial unit reported by an assay and the ability to assign transcripts to individual cells depend on capture efficiency, transcript density, gene dropout, cell segmentation and downstream analysis. As the NCI notes, higher cellular resolution can make lower-hierarchy cell populations harder to identify when gene dropout is an issue.
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Ask what the effective spatial unit is in your intended tissue and workflow, how cells are segmented or assigned, and what validation supports that assignment. A nominal resolution figure describes one aspect of the measurement; it does not by itself establish the completeness of a cell’s expression profile or the accuracy of a fine-grained cell-state call.
How should you interpret platform benchmarks?
Benchmarks can reveal practical trade-offs, but their findings apply to the samples, panels, versions, preparation and analysis tested. A 2025 Nature Communications systematic benchmark compared four high-throughput systems across human tumors. It reported the following named-version and panel figures:
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| System or panel named in the 2025 benchmark | Reported figure | How to use the figure |
|---|---|---|
| Stereo-seq v1.3 | 0.5 μm spatial resolution | A study-reported specification for that version; not a guarantee of effective single-cell profiling in every tissue. |
| Visium HD FFPE target panel | 18,085 genes at 2 μm resolution | A study-reported panel and resolution; confirm current product options and what the workflow measures in your specimen. |
| CosMx 6K | 6,175 genes | A study-reported panel count for the named configuration, not a timeless panel specification. |
| Xenium 5K | 5,001 genes | A study-reported panel count for the named configuration, not a timeless panel specification. |
Those figures are not a single apples-to-apples ranking: resolution, panel size and measurement approach describe different properties. The same benchmark reported differences in capture, background, segmentation and annotation across its tested systems. A separate 2026 Genome Biology comparison of matched FFPE tumors across six cancer types covered Visium v1, Visium v2/CytAssist, Visium HD, Xenium and CosMx. In that study, Xenium showed stronger spatial signal and lower background than CosMx; the authors also described Visium HD as combining broad coverage and near-single-cell-scale resolution with increased data sparsity and computational challenges. These are results for the study’s samples and workflows, not a universal ranking for other tissues or experiments.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can your lab support the data and analysis?
Include analysis capacity in platform selection rather than treating it as a later handoff. Spatial workflows may require specialized data-science support, and high-resolution data can increase sparsity and computational demands. Plan for data storage, quality control, segmentation where relevant, and the spatial statistics needed to answer the biological question.
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- Identify who will perform assay QC, image or transcript processing, segmentation and downstream analysis.
- Confirm that the lab or core can store and process the expected data volume.
- Agree in advance on the outputs needed for the study—such as regional comparisons or cell-level localization—and whether the available workflow supports them.
- Include analysis labor or external support in the project plan, not only the assay and instrument costs.
How do you compare throughput and total cost?
Compare the cost and practical capacity of the complete study, not a headline assay price. The available evidence does not establish reliable current comparative prices or local availability. Request equivalent quotes for the exact configuration, specimen type, number of samples and regions, sequencing, service and analysis requirements. Also ask how many sections can be processed in the time available and whether the workflow preserves the tissue context that matters to your question.
If purchasing or operating a system is not justified for an initial study, ask an institutional core or service provider whether a pilot is feasible. That can help establish specimen compatibility and analytical workload before scaling up. It does not remove the need to match the pilot’s panel, tissue and processing conditions to the intended experiment.
A practical selection sequence
- Define the output. State whether the study needs broad discovery, regional/domain comparisons or localization of selected genes.
- Qualify the specimens. List species, tissue, fresh-frozen or FFPE status, fixation and processing history; eliminate workflows that do not support them.
- Choose the coverage strategy. Determine whether a predefined panel can answer the question or whether broader transcriptome coverage is needed.
- Set the spatial requirement. Specify the area and spatial unit you need, then check segmentation and dropout considerations rather than relying on a resolution number alone.
- Check performance evidence in context. Review benchmarks that resemble your tissue and workflow, and treat their results as conditional evidence rather than a universal winner.
- Confirm analytical and operational fit. Assign QC, segmentation, storage and downstream analysis responsibilities; establish sample capacity and timing.
- Compare complete quotes. Use the same sample count, specimen assumptions, sequencing, service and analysis scope for each option. If uncertainty remains, consider a matched pilot through a core or provider before committing the full study.
A useful choice is the platform that can answer the lab’s actual question with its available specimens and analytical resources. Broad discovery and detailed localization call for different compromises; no single resolution figure, panel count or benchmark result replaces checking the complete workflow.
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