Sequencing-Based Spatial Transcriptomics: Visium, Slide-seq, and Stereo-seq

Visium spatial transcriptomics, along with Slide-seq and Stereo-seq, captures gene expression across a tissue section by trading single-cell precision for whole-transcriptome coverage. Each spot-based platform reads out thousands of barcoded positions at once, but the size of each spot, not the depth of sequencing, sets the resolution ceiling. Knowing where that trade-off lands is the first step to choosing the right method for a given tissue and research question.

How barcoded capture works in spatial transcriptomics

Barcoded spatial transcriptomics platforms convert location into a DNA barcode before sequencing ever begins. A slide, bead array, or patterned surface is printed with millions of oligonucleotide probes, each carrying a unique spatial barcode tied to a precise coordinate. When a tissue section is placed on top, mRNA diffuses a short distance and hybridizes to the nearest probes, tagging each transcript with the barcode of its capture spot.

After reverse transcription and library preparation, the tagged genetic material is sequenced on standard sequencing instruments rather than imaged under a microscope. Computational pipelines then match each sequencing read's spatial barcode back to its original coordinate, reconstructing a gene expression map across the section. This barcode-first design separates Visium, Slide-seq, and Stereo-seq from imaging-based methods that read transcripts directly through iterative fluorescence imaging.

Tissue permeabilization time is one of the most consequential steps in this workflow, since it controls how far mRNA travels before reaching a barcoded probe. Too little permeabilization limits capture efficiency, while too much allows transcripts to diffuse away from their true location before hybridization occurs. Laboratories typically optimize permeabilization empirically for each tissue type, since a protocol tuned for brain sections will not necessarily work as well on liver, tumor, or embryonic tissue.

This distinction matters for anyone planning a spatial transcriptomics experiment, not just for method developers. Choosing among Visium, Slide-seq, and Stereo-seq means weighing whole-transcriptome coverage against near-cellular or subcellular precision, and that choice shapes which biological questions the resulting data can credibly answer. That choice also sits within a much larger spatial biology toolkit, which spans imaging-based and multi-omics approaches to the same tissue architecture questions.

Visium spatial transcriptomics and Visium HD

Visium is a whole-transcriptome, probe-based capture platform and remains the highest-volume sequencing-based spatial transcriptomics product in routine use. The platform is compatible with fresh frozen and formalin-fixed, paraffin-embedded (FFPE) tissue, which has made it a practical entry point for laboratories working from archived clinical material as well as fresh research samples.

The original Visium chemistry prints spots larger than 50 µm in diameter, so each spot typically captures RNA from several cells rather than one, a limitation confirmed in a 2024 benchmarking study of sequencing-based spatial platforms published in Nature Methods. That same study noted that Visium's probe-based chemistry can outperform its earlier polyA-based capture chemistry in sensitivity, though both remain constrained by the same physical spot size.

Visium HD replaces that spot array with a continuous lawn of 2 µm barcoded squares. A 2025 comparative pathology study in the Journal of Experimental & Clinical Cancer Research described this as part of a broader shift toward true single-cell resolution in spatial transcriptomics, achieved alongside imaging-based platforms such as Xenium, which reaches submicron detection through a different, fluorescence-based approach. Data are commonly binned to 8 µm for initial analysis and can be rebinned to other sizes depending on the biological question, balancing single-cell-scale detail against computational load.

A 2025 tumor microenvironment study in Nature Genetics applied Visium HD to colorectal cancer tissue and used its single-cell-scale resolution to resolve distinct macrophage subpopulations with potential pro-tumor and anti-tumor roles, a level of spatial detail that the original Visium spot size could not have resolved. That application illustrates why the resolution jump matters beyond a spec sheet. It changes which biological questions a whole-transcriptome sequencing-based method can actually answer, from broad tissue mapping to fine-grained immune cell characterization within a single tumor section.

Slide-seq and Stereo-seq

Slide-seq and its improved version, Slide-seqV2, both use dense arrays of DNA-barcoded beads deposited onto a patterned surface. Each 10 µm bead carries a unique spatial barcode, so tissue placed on top and permeabilized allows RNA to hybridize onto beads positioned directly beneath it, preserving near-cellular resolution across the section.

Slide-seqV2 improved on the original bead chemistry enough to reach roughly half the mRNA capture efficiency of droplet-based single-cell sequencing, a tenfold gain over the first Slide-seq version, as researchers reported when introducing the improved bead-based method in Nature Biotechnology. That sensitivity gain made dendritically localized transcripts in neurons and fine-grained developmental gene programs newly detectable, applications that the original, lower-sensitivity Slide-seq chemistry could not reliably support.

One practical limitation of Slide-seq-based methods is capture area. The bead array, often called a puck, covers a smaller physical footprint than Visium or Stereo-seq, so a single puck typically captures a section of tissue rather than an entire organ. Laboratories working with small, targeted regions, such as a specific brain nucleus or a defined tumor margin, are less affected by this constraint than those studying whole organs.

Stereo-seq, developed at BGI-Research, uses a different substrate. Its DNA nanoball-patterned chip has spots spaced roughly 0.5 to 0.7 µm apart, achieving subcellular capture density across some of the largest continuous tissue areas available on any sequencing-based platform. Researchers used the platform to build a spatiotemporal transcriptomic atlas of mouse organogenesis, published in Cell, spanning whole embryo sections that exceed the field of view of most other spot-based methods.

That same mouse organogenesis work also demonstrated that Stereo-seq captures intronic transcripts alongside mature mRNA, making it possible to estimate RNA velocity, a computational measure of transcriptional direction and speed, directly within a spatial context. This capability has made Stereo-seq a common choice for developmental biology studies that track how gene expression programs shift across large, rapidly changing tissue structures.

Spot size and resolution trade-offs in spatial transcriptomics

Spot size sets the ceiling on resolution, but molecular diffusion determines how close a platform gets to that ceiling in practice. The same benchmarking study found that lateral diffusion of RNA away from its true position varied substantially by platform and by tissue type, meaning printed spot size alone does not predict real-world spatial accuracy.

That study also found Stereo-seq showed pronounced lateral diffusion in some tissues despite its submicron spot pitch, while Slide-seqV2 controlled diffusion more consistently across the tissues tested. The same analysis found that diffusion behavior was not fixed for a given platform, shifting instead with tissue type and permeabilization time, which means researchers should validate diffusion control on their own tissue rather than assuming a platform's published specifications will hold universally.

Visium, with its larger spots, inherently mixes several cells per measurement regardless of diffusion, which is why cell-type deconvolution is typically required to interpret Visium data at single-cell resolution. Deconvolution methods use a matched single-cell RNA sequencing reference to statistically estimate the proportion of each cell type within a mixed spot, effectively recovering cell-level information that the spot size alone cannot provide directly.

Sequencing depth interacts with all of these resolution considerations. None of the platforms compared in the benchmarking study reached data saturation at typical sequencing depths, meaning that deeper sequencing can still meaningfully improve sensitivity for a given spot size. This makes sequencing budget, not just platform choice, a real factor in how much biological detail a given spatial transcriptomics experiment can extract.

The table below summarizes how the three spot-based spatial transcriptomics platforms compare on the metrics that matter most when choosing a method.

Table 1: A comparison of resolution, capture area, and primary trade-offs across three sequencing-based spatial transcriptomics platforms.

Platform	Approximate resolution	Capture area	Primary trade-off
Visium (standard)	More than 50 µm per spot	6.5 x 6.5 mm	Whole-transcriptome coverage, but each spot averages several cells
Visium HD	2 µm bins, commonly analyzed at 8 µm	6.5 x 6.5 mm	Single-cell-scale resolution with a much larger data volume to process
Slide-seqV2	Approximately 10 µm	Smaller, limited by puck size	Near-cellular resolution with moderate capture efficiency
Stereo-seq	Roughly 0.5 to 0.7 µm spot pitch	Up to several centimeters	Subcellular density across very large tissue areas, with variable diffusion control

Strengths and limitations of Visium, Slide-seq, and Stereo-seq

Each platform earns its place in a spatial transcriptomics toolkit through a different combination of resolution, coverage, and practicality. Whole-transcriptome, probe-based Visium remains a reasonable default when tissue architecture, rather than single-cell precision, is the primary research question, and its compatibility with FFPE tissue makes it a practical option for retrospective studies using archived samples.

Visium HD and Stereo-seq push toward single-cell and subcellular analysis, respectively, but bin-based segmentation can still split irregularly shaped cells into separate compartments. The same comparative pathology study documented this limitation directly, finding that Visium HD segmentation fragmented elongated colorectal tumor cells that the study's imaging-based comparison data kept intact. Cell shape matters more than it might seem, since densely packed or elongated cells, common in epithelial and tumor tissue, are more prone to this kind of segmentation error than round, well-separated cells.

Slide-seqV2 sits between these extremes, offering near-cellular resolution without requiring whole-transcriptome probe panels or the largest capture areas. It has proven particularly useful in neuroscience applications, where its resolution is well matched to the scale of individual neurons and their processes, and where the smaller puck size is less of a constraint given the targeted nature of many brain region studies.

Cost and throughput also factor into platform selection, though neither has a fixed value that applies universally across laboratories and vendors. Whole-transcriptome, high-resolution methods generally require deeper sequencing and more computational infrastructure than lower-resolution alternatives, so a laboratory's available sequencing budget and bioinformatics capacity are practical constraints alongside the underlying biological question.

Researchers choosing among these platforms should weigh the following considerations:

• Tissue architecture: Visium suits large, well-organized tissue sections where regional patterns matter more than single-cell boundaries.
• Cell-type precision: Visium HD and Stereo-seq better resolve tightly packed or heterogeneous tissue, provided downstream segmentation is validated against histology.
• Sample size and cost: Stereo-seq's large capture area suits whole-organ or whole-embryo studies, while Slide-seqV2's smaller puck size fits more targeted regions.
• Downstream analysis: Spot-based Visium data commonly require cell-type deconvolution, whereas near-single-cell platforms need robust cell segmentation instead.
• Sample type: Visium's FFPE compatibility supports archived clinical cohorts, while Slide-seq and Stereo-seq are more commonly used with fresh frozen tissue.

Some research programs combine platforms rather than committing to a single method for an entire project. A common staged approach uses a broad, lower-resolution Visium or Visium HD survey to identify regions of interest across a tissue section, then follows up with more resolution-intensive Stereo-seq or Slide-seqV2 profiling limited to that specific region. This kind of staged workflow can make more efficient use of a finite sequencing budget than applying the highest-resolution method across an entire section by default.

Choosing the right sequencing-based spatial transcriptomics platform

Choosing the right sequencing-based spatial transcriptomics platform depends less on marketed resolution figures and more on what a specific tissue and question demand, a decision that extends to selecting a spatial transcriptomics platform of any kind, sequencing-based or imaging-based. Visium remains a practical whole-transcriptome default. Visium HD and Stereo-seq push toward single-cell and subcellular detail, while Slide-seqV2 offers a proven middle ground for near-cellular applications, particularly in neuroscience.

None of these platforms eliminates the resolution-versus-coverage trade-off entirely. They each shift where that trade-off sits. Matching a platform's effective resolution, not just its printed spot size, to the biological question, tissue type, and available sequencing budget remains the most reliable way to get useful, reproducible spatial transcriptomics data.

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