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Spatial transcriptomics methods differ mainly in how they identify RNA and preserve its location. Sequencing-based spatial capture uses barcodes to map captured transcripts back to tissue positions; imaging-based methods detect selected or encoded transcripts in place. Newer research approaches can avoid sequencing, amplification, or both—but those labels describe different properties. The right method depends on the discovery scope, spatial scale, tissue, performance requirements, and workflow your study needs.
How the main spatial transcriptomics approaches work
Spatial transcriptomics measures gene expression while retaining information about where the RNA came from in a tissue. The two broad method families use different measurement strategies: one captures transcripts at spatially barcoded locations for sequencing, while the other detects transcripts within tissue through probes and imaging.
Sequencing-based spatial capture
In spatial capture, tissue is placed on a substrate containing spatial barcodes. Transcripts captured at those locations are converted into a sequencing library; the barcode links each sequenced transcript to its position in the tissue. This approach can support broad discovery, including whole-transcriptome analysis, but the effective resolution depends on the platform’s capture geometry and how the data are assigned to locations or cells.
A 2024 systematic comparison in Nature Methods evaluated 11 sequencing-based methods and found differences in performance across methods and reference tissues. That count describes the methods in that study, not the full number available.
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Imaging-based in situ methods
In situ methods use probes to bind RNA targets in intact tissue, then identify those targets through one or more imaging rounds. Depending on the method and assay design, they can provide cellular or subcellular localization and can be targeted to a selected gene panel or use more elaborate encoding schemes.
The trade-off is that performance depends on more than the nominal panel size. Probe design, signal detection, imaging cycles, tissue autofluorescence, segmentation, and computational decoding can all affect what is measured and how reliably molecules are assigned to cells. A 2025 Nature Communications benchmark compared high-throughput subcellular platforms across human tumors using measures that included sensitivity, specificity, diffusion control, segmentation, cell annotation, spatial clustering, and transcript–protein alignment.
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In situ sequencing is a distinct example
Expansion Sequencing (ExSeq), described in a 2021 Science paper, illustrates that spatial mapping can combine in-place measurement with sequencing. The report describes targeted and untargeted mapping, including thousands of genes in mouse brain, but its workflow uses rolling-circle amplification. It is therefore not an amplification-free example.
What “sequencing-free” and “amplification-free” mean
These terms are not interchangeable. “Sequencing-free” means the method does not use sequencing to read out the assay. “Amplification-free” means it does not use an amplification step. A method can be sequencing-free while still relying on amplification chemistry, so neither label should be inferred from the other.
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Nanoneedle arrays
A 2026 Nature Biomedical Engineering report describes a nanoneedle-array approach that extracts RNA from individual cells in fresh, minimally processed tissue and decodes multiplexed fluorescence without sequencing or amplification. The report establishes a research approach; it does not by itself establish routine commercial availability.
RAEFISH
A 2025 Cell paper describes RAEFISH as sequencing-free whole-genome spatial transcriptomics at single-molecule resolution. The authors report a profiling scope of 23,000 human genes or 22,000 mouse genes. Those figures describe the reported research method, not equal measurement performance for every gene or a commercially available product. Its amplicon-encoding approach also demonstrates why sequencing-free should not be treated as synonymous with amplification-free.
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Compare methods against your study’s needs
No single metric captures whether a platform is the best fit. Choose the method by weighting the following criteria for the biological question and sample at hand.
- Discovery scope: Decide whether the experiment is exploratory and needs broad transcript coverage, or whether a defined set of genes is sufficient. A targeted panel and a broad discovery assay answer different questions.
- Spatial unit: Specify whether you need spot-, region-, cell-, or subcellular-level assignment. Ask how the method defines a location and how individual molecules are assigned to cells.
- Sample compatibility: Check fresh or frozen versus FFPE requirements, tissue thickness, morphology preservation, and whether the specific tissue has been validated for the method.
- Measurement performance: Compare sensitivity, specificity, capture efficiency, diffusion control, segmentation accuracy, and reproducibility using benchmarks relevant to your tissue and task.
- Throughput and workflow: Include sample throughput, probe or library preparation, imaging or sequencing cycles, instrument access, and computational analysis burden.
- Operational fit: Compare total experimental cost and access requirements using current, geographically relevant information. The cited comparisons do not establish a stable cross-platform price ranking.
The evidence does not establish a broadly accepted gold-standard ranking across sequencing and imaging families. A benchmark’s result depends on the methods, tissue, and metrics it evaluates; weight those metrics according to the study rather than collapsing them into a universal score.
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What platform panel figures can—and cannot—tell you
In the 2025 Nature Communications benchmark, CosMx 6K and Xenium 5K were described as targeted imaging configurations with panels of 6,175 and 5,001 genes, respectively. These are configurations reported in that study, not permanent product specifications. Panel size alone does not show that every gene is detected with equal sensitivity, nor does it establish how a platform will perform in a different tissue or experiment.
Quick Recap
A practical method-selection sequence
- Write down the biological question. State whether you need broad discovery, localization of a known gene set, or both.
- Set the required spatial scale. Define the unit of analysis and confirm how the candidate method assigns transcripts to positions or cells.
- Check the actual sample. Verify compatibility with the tissue preparation, morphology needs, and tissue type rather than relying on a general platform description.
- Choose task-relevant performance measures. Prioritize metrics such as sensitivity, specificity, diffusion control, or segmentation accuracy according to the question, and look for evidence from a relevant tissue or benchmark.
- Map the workflow and access needs. Account for preparation, imaging or sequencing steps, instrument availability, throughput, and analysis demands.
- Verify current specifications and logistics. Product configurations, compatibility, availability, and prices can change; confirm them with current vendor documentation before making a procurement decision.
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