Visium HD vs Xenium vs CosMx vs MERSCOPE: choosing a spatial transcriptomics platform
Visium HD, Xenium, CosMx, MERSCOPE and GeoMx compared: resolution, gene coverage, sample types, analysis challenges and which to choose.
Key takeaways
- Sequencing-based platforms (Visium HD, Stereo-seq) give near whole-transcriptome coverage; resolution comes from tiny barcoded bins.
- Imaging-based platforms (Xenium, CosMx, MERSCOPE) give true single-cell and subcellular resolution for a targeted gene panel.
- Choose by question: discovery suits whole transcriptome; cell-level neighborhoods and validation suit imaging panels.
- Every platform's biggest analysis challenge is assigning transcripts to the right cell.
Spatial transcriptomics now offers several mature commercial platforms, and choosing between them is mostly a choice between breadth of genes and precision of location. This guide compares the platforms we analyze most often and explains what each means for the analysis.
Two families of technology
Sequencing-based platforms capture RNA onto a barcoded surface, then sequence it. Each barcode encodes a location. Gene coverage is broad, close to whole transcriptome, and resolution depends on barcode size.
Imaging-based platforms detect individual transcripts in place by cyclic fluorescent hybridization. Resolution is subcellular, but you measure only the genes in your panel.
Platform comparison
| Criterion | Visium HD | Xenium | CosMx SMI | MERSCOPE | GeoMx DSP |
|---|---|---|---|---|---|
| Technology | Sequencing, probe-based | Imaging, padlock probes | Imaging, cyclic ISH | Imaging, MERFISH | Region capture + sequencing |
| Resolution | 2 µm bins (binned to 8/16 µm or cells) | Subcellular | Subcellular | Subcellular | Regions of interest |
| Genes | Whole transcriptome (~18,000) | Hundreds; ~5,000 with Prime 5K | Panels up to several thousand | Hundreds, up to ~1,000 | Whole transcriptome per region |
| Protein | Via IF image | Protein add-ons | Protein panels | Protein add-ons | Protein panels |
| Best for | Unbiased discovery, gradients, domains | Single-cell neighborhoods, validation | High-plex single-cell imaging | Targeted single-cell imaging | Compartment comparisons |
| Main analysis challenge | Bins to cells, diffusion | Segmentation errors | Segmentation, FOV effects | Segmentation | Normalization, few ROIs |
Platform specifications change frequently. Check current vendor documentation for panel sizes and capture areas when you plan.
When to choose Visium HD
Choose Visium HD when you do not know which genes matter: discovery in a new disease, unbiased tissue domains, pathway gradients, or when you need the whole transcriptome for deconvolution and signaling analysis. The analysis has to turn 2 µm bins (each holding a fraction of a cell) into biologically meaningful units. That means binning at 8 µm or segmenting nuclei on the H&E image and assigning bins to cells. Our Visium HD analysis guide explains how.
When to choose Xenium, CosMx or MERSCOPE
Choose an imaging platform when your question is about which cells sit next to which, such as T cells contacting tumor cells, niches at an invasive front, or rare cell types in tissue architecture. You also need to know, or be able to choose, the genes that define your cell states. The biggest risk is segmentation: when cell boundaries are wrong, transcripts from neighbors create false co-expression. Our Xenium analysis guide and segmentation case study cover this.
When to choose GeoMx
Choose GeoMx when you want to compare defined compartments, for example tumor versus stroma, or glomeruli versus tubules, across many patients with whole-transcriptome depth, and when single-cell resolution is not needed. Analysis focuses on normalization and on mixed models that treat regions from the same patient as non-independent.
A combined design that works
For many projects the strongest design is:
- Single-cell or single-nucleus RNA-seq to define cell states (reference).
- Visium HD for whole-transcriptome discovery in tissue context.
- Xenium or CosMx on serial sections to validate key cell states and neighborhoods at single-cell resolution.
This uses each technology for what it does best and gives reviewers orthogonal evidence.
Replicates still matter
Spatial data is expensive, and many studies have one section per condition. One section is a case study. For condition comparisons, plan biological replicates (independent patients or animals), and use pseudobulk statistics by domain, niche or cell type. Multiple sections from one patient are technical replicates, not biological ones.
How OmicsQuest can help
We analyze every platform in this table and help choose between them during free scoping calls. See our Visium HD, Xenium and other spatial platforms services.
Frequently asked questions
What is the difference between Visium HD and Xenium?
Visium HD captures whole-transcriptome, probe-based expression on a continuous grid of 2 µm barcoded squares, which are binned or assigned to segmented cells during analysis. Xenium images individual transcripts in situ for a targeted panel (hundreds of genes, or about 5,000 with Xenium Prime 5K) at subcellular resolution. Visium HD is better for unbiased discovery; Xenium is better for precise single-cell spatial organization of known genes.
Can I use Visium HD and Xenium together?
Yes, and it is often the strongest design: Visium HD on one section for discovery, Xenium on a serial section for single-cell validation of selected genes, both anchored to matched or atlas single-cell RNA-seq.
Which spatial platform is best for FFPE samples?
Visium HD, Xenium, CosMx and GeoMx all support FFPE tissue. RNA quality (DV200) and section handling matter more than the platform for FFPE success. We recommend a pilot section when tissue is precious.
Work with OmicsQuest. Related services:
References
- Moses L, Pachter L. Museum of spatial transcriptomics. Nat Methods. 2022;19:534-546.
- Janesick A, Shelansky R, Gottscho AD, et al. High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis. Nat Commun. 2023;14:8353.
- Petukhov V, Xu RJ, Soldatov RA, et al. Cell segmentation in imaging-based spatial transcriptomics. Nat Biotechnol. 2022;40:345-354.
- Cable DM, Murray E, Zou LS, et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat Biotechnol. 2022;40:517-526.
- Kleshchevnikov V, Shmatko A, Dann E, et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat Biotechnol. 2022;40:661-671.
- Palla G, Spitzer H, Klein M, et al. Squidpy: a scalable framework for spatial omics analysis. Nat Methods. 2022;19:171-178.