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Choose a spatial transcriptomics platform by matching the assay to your biological question and the specimens you can supply—not by choosing the smallest advertised feature size or the longest gene list. Sequencing-based assays generally suit broad discovery and regional analysis; imaging-based assays generally suit detailed localization of a predefined set of targets. Then verify sample compatibility, effective resolution, sensitivity, throughput, analysis capacity, and total study cost for the specific product version and workflow.
Start with the biological question
Write down what you need the data to show before comparing instruments. Spatial transcriptomics broadly falls into two assay families, and they produce different kinds of evidence.
- Broad discovery: If you need to find unanticipated genes or compare expression across tissue regions, prioritize transcriptome coverage and the ability to analyze regional patterns.
- Localization of known targets: If you already have a focused gene set and need to see where those transcripts occur in detail, an imaging-based assay may be a better fit.
- Cell or niche relationships: Decide whether the key result is a well-resolved view of a small area or a broader map of tissue domains. The National Cancer Institute’s guidance is that imaging-based methods suit “a lot of detail for a small area,” while sequencing-based methods suit regional or niche-level results.
These are starting points, not universal rules. The right choice depends on whether a platform’s measured targets and spatial units can answer the specific biological question.
Understand what each assay family measures
Sequencing-based assays
These workflows capture RNA using spatially barcoded arrays or beads, then read the captured material by sequencing. Their main attraction is the potential for broad gene coverage and discovery. The spatial measurement may represent a spot, bin, or other capture area rather than a complete expression profile for every individual cell.
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Imaging-based assays
These workflows use fluorescent probes to detect transcripts in place through sequential imaging. They can provide detailed localization for the genes included in the assay, but a predefined panel does not automatically answer questions about genes outside that panel.
ROI-oriented profiling
GeoMx Digital Spatial Profiler is part of the broader spatial-profiling landscape and is oriented around regions of interest (ROIs). Consider whether a region-focused workflow matches the scale of the question; do not treat every spatial platform as though it measures the same spatial unit.
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Check the specimen before narrowing the shortlist
Sample feasibility can rule out an otherwise appealing platform. Confirm compatibility for the exact assay version and workflow rather than relying on a broad platform-family description.
- Preservation: Establish whether the available tissue is fresh-frozen or formalin-fixed, paraffin-embedded (FFPE).
- Biological material: Confirm species, tissue type, and any restrictions in the current compatibility documentation.
- Processing history: Record fixation and other relevant handling details, and ask the platform provider or core facility whether they fit the validated workflow.
- Study design: Check that the assay supports the number and type of sections or regions you need, not merely one attractive pilot specimen.
Compatibility varies by product and version. The PLOS Computational Biology article “Ten quick tips for spatial transcriptomics analysis” treats platform comparison as orienting guidance, not a substitute for checking current specifications.
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Compare the practical trade-offs
| Decision axis | What to ask | Why it changes the choice |
|---|---|---|
| Coverage | Is the assay whole-transcriptome or a predefined panel? Does it cover the genes needed to test the hypothesis? | Broad coverage can support discovery; a targeted panel can provide focused in-place measurements. |
| Effective spatial resolution | What is the actual measurement unit? How are cells segmented or assigned to a location? | A small feature size does not by itself guarantee reliable single-cell profiles or correct cell assignment. |
| Sensitivity and background | How well are low-abundance targets detected? What negative controls and quality checks are available? | Transcript recovery, background signal, and spatial signal can differ across platforms and workflows. |
| Tissue context and scale | Do you need detailed imaging in a limited area or a broader view across regions? How many samples and sections are planned? | Methods have different practical spatial profiles and throughput constraints. |
| Analysis capacity | Can the team handle quality control, segmentation, storage, and downstream spatial statistics? | Specialized data-science support may be needed, and high-resolution data can increase computational demands. |
| Total cost and access | What do the assay, instrument access, sequencing, service, analysis, and staff time cost for this exact study? | There is no reliable current comparative price established here; only comparable study-specific quotes can resolve the budget question. |
Use benchmark numbers carefully
A 2025 Nature Communications benchmark compared four high-throughput systems across human tumors. For the named versions and panels in that study, it reported the following figures:
| System and study configuration | Reported figure | How to interpret it |
|---|---|---|
| Stereo-seq v1.3 | 0.5 μm spatial resolution | Figure reported by the 2025 benchmark for this version; it is not a guarantee of single-cell expression completeness. |
| Visium HD | 18,085-gene FFPE target panel; 2 μm resolution | Figures reported by the 2025 benchmark for its stated configuration. |
| CosMx 6K | 6,175 genes | Panel count reported by the 2025 benchmark. |
| Xenium 5K | 5,001 genes | Panel count reported by the 2025 benchmark. |
These values describe study configurations, not permanent product specifications. Panel availability, chemistry, compatibility, throughput, and instrument configuration can change, so check current official documentation before using a figure to make a purchasing or experimental decision.
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The benchmark also found differences in capture, background, segmentation, and annotation among the systems it tested. A separate 2026 Genome Biology comparison of matched FFPE tumors across six cancer types reported stronger spatial signal and lower background for Xenium than CosMx in its study. It also described Visium HD as combining broad coverage and near-single-cell-scale resolution with increased data sparsity and computational challenges. These are findings from the samples and workflows studied—not a universal platform ranking.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Shortlist platform families, then verify the exact version
The platforms covered in the comparison literature illustrate the available range, but their names alone do not establish that a particular configuration fits your study.
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| Approach | Examples identified in the evidence | What to verify for your study |
|---|---|---|
| Sequencing-based | 10x Genomics Visium and Visium HD; BGI Stereo-seq | Current sample compatibility, capture unit, coverage, workflow version, and sequencing and analysis requirements. |
| Imaging-based | 10x Genomics Xenium; NanoString CosMx SMI; Vizgen MERSCOPE | Panel contents, tissue compatibility, target detection, imaging workflow, and how the data will be segmented and analyzed. |
| ROI-oriented spatial profiling | GeoMx Digital Spatial Profiler | Whether selecting regions of interest matches the study’s sampling and resolution needs. |
Exact chemistry, panel availability, tissue compatibility, throughput, pricing, and configuration depend on the product and version. Use a current product specification and a provider or core-facility consultation to confirm details for the intended specimens.
Plan analysis and budget as part of assay selection
Do not treat computation as a downstream detail. The workflow may require specialized data-science expertise for quality control, segmentation, and spatial statistics. Higher-resolution datasets can also be sparse, increasing the effort needed to distinguish biological structure from missing or weakly detected transcripts. Ask who will perform each analysis step and whether your lab has the storage and computing capacity for the expected data.
No current, comparable platform pricing is established across the options discussed here. Request quotes for the same study assumptions, including the exact assay configuration, specimen type, sample and section counts, sequencing, service or core-facility access, analysis, and staff time. If purchasing a system is not justified for a first study, ask whether an institutional core or service provider can run a pilot using the relevant specimen type.
A practical selection sequence
- Define the result: State whether the study needs broad discovery, localization of known markers, cell-state mapping, or comparison of tissue regions.
- Describe the specimen: Record preservation, species, tissue, fixation, and processing history; eliminate workflows that do not support it.
- Choose the measurement scope: Decide whether the question calls for broad sequencing-based coverage, detailed imaging of a panel, or region-of-interest profiling.
- Check the real spatial unit: Ask how transcripts are captured or localized, how cells are segmented or assigned, and what evidence supports the desired level of inference.
- Review performance evidence: Compare sensitivity, background, controls, and segmentation using studies that match your tissue and workflow as closely as possible.
- Confirm operational fit: Validate throughput, analysis ownership, storage, computing, local access, and study-specific total cost.
- Run a relevant pilot when feasible: Use representative specimens and the planned analysis workflow so the pilot tests practical assay fit, not just whether the instrument produces data.
The National Cancer Institute cautions that finer cellular resolution can make lower-hierarchy populations harder to identify when gene dropout is substantial. Treat advertised resolution as one dimension of performance, alongside capture efficiency, transcript density, segmentation, and analysis—not as a stand-alone promise of complete cell profiles.
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