Spatial transcriptomics & spatial multi-omics

01Overview and value chain

Markers: [EC: Human Cell Atlas / EU Single-Cell & Spatial Omics | OECD: Genomics & Bioinformatics | Regulator: FDA (USA), EMA (EU), NMPA (China)]

Spatial transcriptomics measures gene expression while keeping each transcript’s physical position on an intact tissue section, turning a dissociated cell suspension into a coordinate-addressed molecular map. Two complementary chemistry families do this: sequencing-based methods (Visium, Stereo-seq) lay the tissue onto a barcoded array of spots or DNA-nanoball features, capture the released mRNA in situ, then sequence the barcodes back to their coordinates for unbiased whole-transcriptome coverage; imaging-based methods (Xenium, MERSCOPE/MERFISH, CosMx, Molecular Cartography) hybridise fluorescent probe panels to RNA directly on the slide and read out hundreds-to-thousands of targets at subcellular resolution through cyclic microscopy. The global spatial-transcriptomics imager market grew from about 420 million USD in 2023 to a projected 1.28 billion USD in 2026 — a 32.5% CAGR — and by the end of 2025 more than 380 research institutes and pharmaceutical groups had purchased or leased an instrument, with oncology accounting for 54% of revenue and pharmaceutical end-customers rising from 28% in 2023 to 43% in 2026. The technology is shifting from a research curiosity into a drug-discovery and biomarker workflow: a single CosMx Whole-Transcriptome run images over 18,000 human RNA targets per cell in situ, MERSCOPE Ultra maps around 1,000 custom genes across 3 cm² of tissue at ~100 nm pixel resolution, and the same-slide RNA-plus-protein “spatial multi-omics” readout (over 19,000 RNAs with 72 proteins on CosMx Same-Cell Multiomics) is now the competitive frontier.

The key directions of spatial transcriptomics & spatial multi-omics are:

1. NGS-based spatial capture (Visium HD, Stereo-seq): tissue placed on a barcoded spot or DNA-nanoball array for unbiased, whole-transcriptome coverage at multicellular down to subcellular resolution, then sequenced.
2. Imaging-based in situ mapping (Xenium, MERSCOPE, CosMx): cyclic fluorescent probing of targeted 500–1,000+ gene panels at true subcellular resolution, detecting individual transcript molecules.
3. Spatial multi-omics (RNA + protein, same section): CosMx Same-Cell Multiomics, Visium proteins and Akoya PhenoCycler read RNA and 70+ protein markers on one section, linking phenotype to transcriptome in the same cell.
4. AI spatial analysis: deep-learning cell segmentation, spatial-domain identification (Vision-Transformer models) and neighbourhood analysis that turn pixel-level signal into tumour-microenvironment maps and spatial biomarker signatures.

Sectoral value chain

[Tissue section on spatial slide] ──> [In-situ capture / probe hybridisation] ──> [NGS sequencing OR cyclic imaging]
                                                       │
                                            (barcode → gene decoding)
                                                       │
                                                       ▼
[Spatial biomarker signature] <─── [AI cell segmentation & neighbourhood analysis] <─── [Transcript coordinate map]

Value chain levels

Cross-cutting technologies of the sector:

• Spatial barcoded arrays: patterned slides — barcoded spots (Visium, ~2 µm for Visium HD) or rolled DNA-nanoball chips (Stereo-seq, features down to ~500 nm) — that tag every captured mRNA with its x/y coordinate before sequencing.
• Error-robust combinatorial barcoding (MERFISH): each gene gets a Hamming-distance-separated binary barcode read over sequential fluorescence rounds, so single-molecule RNA counting stays accurate even with per-round readout errors.
• Deep-learning cell segmentation & spatial AI: convolutional and Vision-Transformer models that assign each transcript to a cell from morphology plus signal, then identify spatial domains and cell-neighbourhood signatures the eye cannot.

02US

The United States dominates spatial-omics platform supply: the four leading instrument vendors — 10x Genomics, Vizgen, Akoya Biosciences and NanoString (now Bruker Spatial Biology) — are all US-based, and North America holds roughly 68% of global installed capacity.

10x Visium/Xenium, Vizgen MERSCOPE, Akoya PhenoCycler, NanoString/Bruker CosMx

• 10x Genomics and the Visium/Xenium stack: 10x owns the sequencing-based Visium line (Visium HD at ~2 µm near-single-cell resolution) and the imaging-based Xenium In Situ platform, whose Xenium Prime 5K panel maps 5,000 genes at subcellular resolution — a January 2026 Genome Biology study benchmarked Visium v1, v2/CytAssist, Visium HD, Xenium and CosMx head-to-head across six cancer types.
• Vizgen MERSCOPE: Vizgen’s MERSCOPE Ultra images up to 1,000 custom genes across 3 cm² of tissue at ~100 nm pixel resolution using MERFISH 2.0 chemistry, which an 2026 bioRxiv preprint reports gives an 8-fold sensitivity gain over MERFISH 1.0 while keeping quantitative concordance (Pearson r ≥ 0.8).
• Akoya Biosciences and NanoString/Bruker: Akoya’s PhenoCycler-Fusion is the spatial-proteomics leader, detecting 100-plus protein biomarkers per section at 0.25 µm/pixel with cyclic immunofluorescence plus in-situ-hybridisation RNA modules; NanoString’s CosMx (now Bruker Spatial Biology) launched a 6,000-plex RNA panel in February 2024 and a Whole-Transcriptome assay imaging about 19,000 genes, extended in 2025 to Same-Cell Multiomics measuring over 19,000 RNAs plus 72 proteins on one FFPE section.

03CN

China is the fastest-growing spatial-omics region — Asia-Pacific capacity expands at roughly 47% per year — anchored by the native Stereo-seq platform and a fast-rising patent base, with Chinese applicants’ share of spatial-transcriptomics patents climbing from 12% in 2020 to 31% in 2026.

BGI/STOmics Stereo-seq, DNB nanoball chips, MOST atlas programmes

• BGI STOmics and Stereo-seq: the STOmics division of BGI developed Stereo-seq, a sequencing-based platform built on DNA-nanoball (DNB) arrays whose feature size reaches nanometre scale, giving a uniquely large field-of-view at subcellular resolution — it is China’s leading native spatial-transcriptomics technology and the basis for whole-organ and embryogenesis atlases.
• Distribution and service network: Stereo-seq and the imported Visium HD / Akoya platforms are distributed and run as a service across the Chinese CRO market, with a publicly tendered Stereo-seq service priced around 78,000 yuan per project in 2025 indicative of routine commercial availability.
• MOST and national programmes: the Ministry of Science and Technology funds spatio-temporal omics atlas programmes that feed domestic drug discovery and the domestic patent surge that has shifted a third of global spatial-omics IP to Chinese applicants by 2026.

04EU

The European Union is a strong academic contributor and the home of one commercial spatial platform, but no EU-headquartered vendor rivals the four US instrument giants; its weight is in method development, the Human Cell Atlas and regulator-grade data quality.

Resolve Biosciences, academic hubs, EMA oversight

• Resolve Biosciences: the German company (Monheim am Rhein) commercialises the Molecular Cartography platform — an in-situ-sequencing, padlock-probe-based targeted spatial-transcriptomics system cited alongside CosMx and Xenium as one of the commercial imaging-based platforms (Nature Methods, 2025).
• Academic hubs: EMBL, the Sanger Institute and partner European genomics institutes drive Human Cell Atlas method development and open-source spatial-analysis tools.
• EMA oversight: the EMA applies data-integrity and reagent-quality expectations to spatial readouts used in regulated drug-discovery submissions, paralleling the FDA’s 21 CFR Part 11 controls on the imaging and analysis software stack.

05Leading companies and research institutes

06Tech stack and innovations

The spatial-omics stack couples an in-situ chemistry engine, a high-resolution readout (sequencer or cyclic imager) and a deep-learning analysis layer into one workflow that biopharma buys by instrument, reagent kit or full-service CRO project.

1. In-situ capture and probe chemistry:
   • Sequencing-based platforms pattern barcoded features onto the slide — Visium HD spots down to about 2 µm, Stereo-seq DNA-nanoball features down to roughly 500 nm — so each captured mRNA carries its coordinate before NGS.
   • Imaging-based platforms hybridise oligonucleotide probe panels (MERFISH error-robust barcodes, CosMx direct hybridisation, Xenium in-situ sequencing) that are then read over sequential fluorescence cycles to call single transcript molecules.
2. High-plex readout (NGS + cyclic fluorescence imaging):
   • NGS readout delivers unbiased whole-transcriptome coverage (CosMx ~19,000 genes, Stereo-seq and Visium HD transcriptome-wide), while cyclic imaging reaches 1,000-plus targeted genes at subcellular resolution and 200 nm optical scale.
   • Throughput has risen sharply — CosMx Same-Cell Multiomics now reads over 19,000 RNAs with 72 proteins on one FFPE section, and Akoya PhenoCycler images about 1 million cells in under 10 minutes at 100-plex protein.
3. Spatial bioinformatics and AI:
   • Cell segmentation, spatial-domain detection and neighbourhood analysis run in Seurat, Squidpy and dedicated deep-learning models (Vision-Transformer frameworks) that fuse H&E histology with transcript coordinates to reconstruct single-cell expression.
   • Spatial signatures — the organisation and proximity of cell subsets in the tumour microenvironment — have been shown in a JAMA Oncology meta-analysis of over 8,000 patients to predict anti-PD-1/PD-L1 response more accurately than PD-L1, TMB or GEP alone.

07Value chains and production pipelines

Industrial pipeline of a spatial-transcriptomics service project (ISO 9001 / CLIA-compatible data handling, FDA 21 CFR Part 11)

┌───────────────────────────┐      ┌───────────────────────────┐
│ 1. Tissue sectioning onto │ ───> │ 2. In-situ capture /      │
│    spatial slide or chip  │      │    probe hybridisation    │
└───────────────────────────┘      └───────────────────────────┘
                                                  │
                                                  ▼
┌───────────────────────────┐      ┌───────────────────────────┐
│ 4. NGS sequencing OR      │ <─── │ 3. Library build /        │
│    cyclic imaging readout │      │    imaging cycles         │
└───────────────────────────┘      └───────────────────────────┘
              │
              ▼
┌───────────────────────────┐      ┌───────────────────────────┐
│ 5. Decoding & cell        │ ───> │ 6. AI spatial analysis &  │
│    segmentation           │      │    signature report       │
└───────────────────────────┘      └───────────────────────────┘

Stage 1: Tissue sectioning onto a spatial slide or chip

A fresh-frozen or FFPE tissue block is sectioned onto a spatially barcoded capture slide (Visium, Stereo-seq) or an imaging chip (Xenium, MERSCOPE, CosMx), preserving morphology and RNA integrity, and a matched H&E or immunofluorescence image is captured for downstream registration.

Stage 2: In-situ capture or probe hybridisation

Released mRNA is captured by the barcoded array features (NGS-based) or hybridised by the fluorescent probe panel (imaging-based) directly on the section, with on-slide chemistry run through automated fluidic modules; in multi-omics workflows, barcoded antibodies are applied in the same pass to tag proteins.

Stage 3: Library build or imaging cycles

For sequencing-based platforms the captured cDNA is amplified into an NGS library; for imaging-based platforms the slide cycles through sequential fluorescence rounds (tens of cycles) to read each gene’s combinatorial barcode, with MERFISH error-robust coding keeping per-round readout errors from corrupting transcript identity.

Stage 4: NGS sequencing or cyclic imaging readout

The library is sequenced on an Illumina-class NGS instrument for whole-transcriptome coverage, or the chip is imaged on the platform’s microscope (Xenium Analyzer, MERSCOPE Ultra, CosMx SMI) at ~100–250 nm pixel resolution to localise individual transcript molecules — CosMx Whole-Transcriptome and Stereo-seq deliver tens of thousands of genes per section.

Stage 5: Decoding and cell segmentation

Barcodes are assigned back to spatial coordinates, transcripts are called against the gene panel, and deep-learning segmentation models fuse the morphology image with the transcript signal to draw single-cell boundaries — the January 2026 Genome Biology benchmark shows Xenium Prime 5K reaching a median of 242 transcripts per cell versus 58 for Xenium v1.

Stage 6: AI spatial analysis and signature report

Spatially variable genes, tissue domains, cell-type deconvolution and cell-neighbourhood interactions are computed (Seurat, Squidpy, Vision-Transformer models), and the CRO delivers to the client a tissue-domain map, a cell-neighbourhood graph and a spatial biomarker signature — increasingly tied by the client to clinical outcome such as immunotherapy response.