{ "id": "epamx8bg23", "slug": "spatial-transcriptomics", "title": "Spatial transcriptomics", "updated": "2026-09-30", "topic_path": [ { "id": "life", "label": "Life and health", "api_url": "https://www.edgepedia.org/api/v1/topics/life" }, { "id": "life.biology", "label": "Biological foundations", "api_url": "https://www.edgepedia.org/api/v1/topics/life.biology" }, { "id": "life.biology.rna", "label": "RNA and gene regulation", "api_url": "https://www.edgepedia.org/api/v1/topics/life.biology.rna" }, { "id": "life.biology.rna.rna-elements-and-technologies", "label": "RNA elements, catalytic RNAs, and technologies", "api_url": "https://www.edgepedia.org/api/v1/topics/life.biology.rna.rna-elements-and-technologies" }, { "id": "life.biology.rna.rna-methods-resources", "label": "RNA methods, databases, and resources", "api_url": "https://www.edgepedia.org/api/v1/topics/life.biology.rna.rna-methods-resources" } ], "geo": [ { "id": "geo.nongeo.t2001.life.biology.rna", "label": "Non-geographic · 2001 to 2020: RNA and gene regulation", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life.biology.rna", "path": [ { "id": "geo.nongeo", "label": "Non-geographic", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo" }, { "id": "geo.nongeo.t2001", "label": "Non-geographic · 2001 to 2020", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001" }, { "id": "geo.nongeo.t2001.life", "label": "Life and health", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life" }, { "id": "geo.nongeo.t2001.life.biology", "label": "Biological foundations", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life.biology" }, { "id": "geo.nongeo.t2001.life.biology.rna", "label": "RNA and gene regulation", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life.biology.rna" } ] } ], "excerpt": "Spatial transcriptomics is a family of molecular biology methods that maps gene expression across intact tissue while recording each transcript's position, introduced by Patrik Ståhl in 2016.", "snippet": "Spatial transcriptomics is a family of molecular biology methods that maps gene expression across intact tissue while recording each transcript's position, introduced by Patrik Ståhl in 2016.", "node": "life.biology.rna.rna-methods-resources", "markdown": "# Spatial transcriptomics\n\nSpatial transcriptomics is a family of molecular biology methods that measures gene expression across an intact tissue section while recording where each transcript came from, producing a map of transcriptomic profiles onto positions in the sample. Unlike single-cell RNA-seq, which requires dissociating tissue and loses the physical relationships between cells, these methods keep the two-dimensional (or three-dimensional) coordinates of the data, so cell types, signaling interactions, and tissue architecture can be analyzed in place. The term was introduced by Patrik L. Ståhl and colleagues in a 2016 Science paper that positioned tissue sections on arrayed reverse transcription primers carrying unique positional barcodes.[1](https://doi.org/10.1126/science.aaf2403)\n\n| Key fact | Detail |\n|---|---|\n| Defining paper | Ståhl et al., Science 353 (6294): 78–82, 2016; mouse brain and human breast cancer sections[1](https://doi.org/10.1126/science.aaf2403) • [2](https://publications.scilifelab.se/publication/4be007a3a4414a64b991907f260116cc) |\n| Core principle | In situ capture on spatially barcoded oligo-dT arrays, in situ sequencing, or cyclic fluorescent imaging of targeted transcripts[3](https://link.springer.com/article/10.1186/s13073-022-01075-1) • [4](https://pmc.ncbi.nlm.nih.gov/articles/PMC10453065/) |\n| Resolution range | From 100 µm spots (original ST) to 2 µm (Visium HD, HDST) and 0.5 µm (Stereo-seq); imaging methods reach single-molecule, subcellular precision[3](https://link.springer.com/article/10.1186/s13073-022-01075-1) • [5](https://www.nature.com/articles/s41467-025-64292-3) |\n| Gene coverage | Sequencing-based: whole transcriptome; imaging-based: panels of several hundred to ~6,000 genes (Xenium 5K: 5,006; CosMx 6K: 6,175)[6](https://link.springer.com/article/10.1186/s12864-025-11235-3) • [5](https://www.nature.com/articles/s41467-025-64292-3) |\n| Tissue input | Fresh-frozen (RIN ≥ 7 recommended) or FFPE (DV200 ≥ 30%)[6](https://link.springer.com/article/10.1186/s12864-025-11235-3) |\n| Main trade-off | Whole-transcriptome coverage with lower sensitivity and multi-cell spots, versus targeted panels with single-cell or subcellular resolution[6](https://link.springer.com/article/10.1186/s12864-025-11235-3) |\n\n## How it works\n\nThree readout mechanisms dominate. Array capture methods place a tissue section on a slide carrying millions of capture oligonucleotides, each with a spatial barcode unique to its spot; released polyadenylated mRNA binds locally, is reverse transcribed, and is sequenced, so every read inherits the barcode of the position it was captured on.[1](https://doi.org/10.1126/science.aaf2403) • [7](https://assets-web.prd-web.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/10x-visium-tech-note-m-gl-00177/10x-visium-tech-note-m-gl-00177.pdf) [In situ](https://www.edgepedia.org/in-situ) sequencing methods, such as STARmap, combine hydrogel-tissue chemistry, targeted signal amplification, and sequencing inside the tissue itself, detecting 160 to 1020 genes in mouse brain at single-cell resolution.[4](https://pmc.ncbi.nlm.nih.gov/articles/PMC10453065/) Image-based targeted methods, such as MERFISH and seqFISH+, decode combinatorial, error-robust barcodes over sequential hybridization and imaging rounds, resolving individual transcripts as fluorescent spots at subcellular resolution.[4](https://pmc.ncbi.nlm.nih.gov/articles/PMC10453065/) Reviews classify the field into sequencing-based, probe-based, imaging-based, and image-guided single-cell approaches.[4](https://pmc.ncbi.nlm.nih.gov/articles/PMC10453065/)\n\n## How it is done\n\nA typical experiment, described for the Visium platform, runs tissue preparation, microscopy imaging of the section, library preparation, sequencing, and analysis.[7](https://assets-web.prd-web.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/10x-visium-tech-note-m-gl-00177/10x-visium-tech-note-m-gl-00177.pdf) Fresh-frozen tissue is sectioned onto a capture slide and permeabilized so released mRNA binds the spatially barcoded probes, and reverse transcription produces cDNA. FFPE tissue, whose RNA is chemically modified and fragmented, instead uses an RNA-templated ligation chemistry: in the Visium FFPE V2 workflow, a pair of adjacent probes hybridizes to the target mRNA and is ligated, and the poly-A tail on one probe is captured by poly(dT) on the slide.[7](https://assets-web.prd-web.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/10x-visium-tech-note-m-gl-00177/10x-visium-tech-note-m-gl-00177.pdf) • [6](https://link.springer.com/article/10.1186/s12864-025-11235-3) Libraries are sequenced on Illumina systems such as NovaSeq 6000 or NextSeq 2000/1000; a Visium slide carries four capture areas, with two- and eight-area formats also available.[7](https://assets-web.prd-web.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/10x-visium-tech-note-m-gl-00177/10x-visium-tech-note-m-gl-00177.pdf)\n\nComputationally, the Space Ranger pipeline aligns reads to the genome, matches read barcodes to spatial locations, and counts transcripts into a gene-by-spot matrix.[3](https://link.springer.com/article/10.1186/s13073-022-01075-1) Downstream analysis then covers batch-effect correction, dimensionality reduction and spatial clustering, cell-type annotation against scRNA-seq references, detection of spatially variable genes, cell-cell and gene-gene interaction analysis, spatial trajectories, and three-dimensional modeling.[8](https://pmc.ncbi.nlm.nih.gov/articles/PMC10781722/) For subcellular data, segmentation reconstructs single-cell transcriptomes using nuclear staining priors, deep neural networks, or scRNA-seq references; Baysor exploits such priors, while SSAM avoids segmentation entirely.[3](https://link.springer.com/article/10.1186/s13073-022-01075-1)\n\n## Origin\n\nThe method was reported by Patrik L. Ståhl and colleagues as \"spatial transcriptomics\" in Science in 2016, in a paper with a long author list from the KTH/SciLifeLab environment; the study demonstrated high-quality RNA-sequencing data with maintained two-dimensional positional information from mouse brain and human breast cancer sections.[1](https://doi.org/10.1126/science.aaf2403) • [2](https://publications.scilifelab.se/publication/4be007a3a4414a64b991907f260116cc) Earlier approaches addressed the same problem with lower throughput or resolution: laser capture microdissection excised specific tissue regions for microarray profiling but was limited by spatial resolution, laser-induced mRNA degradation, and the need to process many samples; Tomo-seq profiled successive cryosections by RNA-seq; and single-molecule fluorescence in situ hybridization made individual labeled transcripts visible as single microscopy spots, founding the imaging branch of the field.[3](https://link.springer.com/article/10.1186/s13073-022-01075-1)\n\n## Variants\n\nSequencing-based platforms differ mainly in spot or bead size. Visium, the commercial version of the original array method, uses hexagonal 55 µm spots with 100 µm center-to-center spacing, so each spot covers a small cluster of cells rather than one cell; its sensitivity has risen to more than 10,000 transcripts per spot.[3](https://link.springer.com/article/10.1186/s13073-022-01075-1) • [4](https://pmc.ncbi.nlm.nih.gov/articles/PMC10453065/) [Slide-seq](https://www.edgepedia.org/slide-seq) uses randomly barcoded 10 µm beads capturing roughly 500 transcripts per bead, and Slide-seqV2 reaches near-cellular resolution with more than 1,000 transcripts per bead, recovering about 30–50% as much transcriptomic information per bead as droplet-based 10X single-cell RNA-seq.[3](https://link.springer.com/article/10.1186/s13073-022-01075-1) • [4](https://pmc.ncbi.nlm.nih.gov/articles/PMC10453065/) HDST confines beads to wells etched in the slide at 2 µm resolution.[3](https://link.springer.com/article/10.1186/s13073-022-01075-1) Stereo-seq deposits barcoded rolling-circle amplification products in wells 0.5 µm apart, giving sub-micron resolution over centimeter-scale fields, the largest detection area among NGS-based platforms.[3](https://link.springer.com/article/10.1186/s13073-022-01075-1) • [8](https://pmc.ncbi.nlm.nih.gov/articles/PMC10781722/) A systematic comparison groups sequencing-based methods into bead-array technologies (HDST, BMKMANU S1000, Slide-seq V2, Curio Seeker, Slide-tag), polony- or nanoball-based technologies (Stereo-seq, PIXEL-seq, Salus), and microfluidics (DBiT-seq).[9](https://www.nature.com/articles/s41592-024-02325-3)\n\nImaging-based platforms trade coverage for resolution. MERFISH and seqFISH+ are described as detecting around 10,000 genes at subcellular resolution, although a 2025 benchmark lists earlier MERFISH implementations at 1,000 genes, so panel size depends on the implementation.[4](https://pmc.ncbi.nlm.nih.gov/articles/PMC10453065/) • [5](https://www.nature.com/articles/s41467-025-64292-3) STARmap detects 160 to 1020 genes at single-cell resolution.[4](https://pmc.ncbi.nlm.nih.gov/articles/PMC10453065/) Earlier CosMx SMI panels quantified up to 1,000 targeted transcripts with 64 protein analytes, while the newer CosMx 6K panel targets 6,175 genes, profiling up to 1 million cells per sample at 3D subcellular resolution.[4](https://pmc.ncbi.nlm.nih.gov/articles/PMC10453065/) The region-of-interest GeoMx Digital Spatial Profiler uses photocleavable gene-specific barcoded probes for coarser sampling.[3](https://link.springer.com/article/10.1186/s13073-022-01075-1) • [10](https://journals.plos.org/ploscompbiol/article/file?id=10.1371%2Fjournal.pcbi.1014757&type=printable)\n\n## Applications\n\nThe original demonstration covered mouse brain and human breast cancer.[1](https://doi.org/10.1126/science.aaf2403) A 2025 benchmarking study profiled high-throughput subcellular platforms (Stereo-seq v1.3, Visium HD, CosMx 6K, Xenium 5K) across human tumors, giving systematic cross-platform comparisons on cancer tissue.[5](https://www.nature.com/articles/s41467-025-64292-3) [Developmental biology](https://www.edgepedia.org/developmental-biology) benefits from large-field, fine-resolution mapping: Hi-C-RNA, a spatial multi-omics method, simultaneously maps 3D chromatin architecture and whole-transcriptome profiles from the same section at near-single-cell resolution in developing mouse embryos, adult mouse brain, and human tissue.[11](https://doi.org/10.1016/j.cell.2026.07.039) Imaging depth is also extending: 3D MERFISH with confocal microscopy and deep learning has been demonstrated on mouse brain sections up to 200 µm thick, compared with the roughly 10 µm thin sections used previously.[12](https://elifesciences.org/articles/90029)\n\n## Limitations and alternatives\n\nThe central trade-off is coverage versus resolution. Sequencing-based methods provide whole-transcriptome analysis but mostly lack single-cell resolution and have lower RNA capture efficiency and detection sensitivity than imaging-based methods, especially for low-abundance transcripts; deeper sequencing partially compensates.[6](https://link.springer.com/article/10.1186/s12864-025-11235-3) Imaging-based methods offer single-cell or subcellular resolution with high sensitivity, specificity, and reproducibility, but use targeted panels whose size varies by implementation rather than having a universal six-thousand-gene ceiling; they require imaging times from two days to a week or more and have low throughput.[6](https://link.springer.com/article/10.1186/s12864-025-11235-3) Reported detection efficiencies span a wide range: seqFISH 84%, MERFISH 80%, seqFISH+ 49%, EEL FISH 13.2% on fresh-frozen tissue, and below 1% for ISS and below 0.005% for FISSEQ.[8](https://pmc.ncbi.nlm.nih.gov/articles/PMC10781722/)\n\nRNA quality is a practical gatekeeper: a RIN of at least 7 is recommended for fresh tissue and a DV200 of at least 30% for FFPE tissue; Xenium tolerates degraded RNA better, while Merscope outperforms Xenium when RNA quality is high.[6](https://link.springer.com/article/10.1186/s12864-025-11235-3) Multi-cell spots mix transcripts from several cells, which deconvolution and segmentation methods partially correct, and batch effects require explicit correction in downstream analysis.[3](https://link.springer.com/article/10.1186/s13073-022-01075-1) • [8](https://pmc.ncbi.nlm.nih.gov/articles/PMC10781722/) Newer platforms narrow the gap: Visium HD uses spatially barcoded poly(dT) probes to capture polyadenylated transcripts from whole-transcriptome probe sets, with gene-expression measurements reported in 2 µm bins, Stereo-seq v1.3 captures poly(A)-tailed RNA at 0.5 µm, and CosMx 6K and Xenium 5K profile 6,175 and 5,006 genes with single-molecule precision.[5](https://www.nature.com/articles/s41467-025-64292-3) Whole-transcriptome-scale, isoform-resolved spatial imaging of single cells has also been reported, addressing the limitation that imaging methods detect only targeted gene sets while sequencing-based methods lack single-cell resolution.[13](https://doi.org/10.1016/j.cell.2026.06.027)\n\n## References\n\n1. [Patrik L. Ståhl and colleagues (2016). Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science.](https://doi.org/10.1126/science.aaf2403)\n2. [Visualization and analysis of gene expression in tissue sections by spatial transcriptomics (SciLifeLab publication record)](https://publications.scilifelab.se/publication/4be007a3a4414a64b991907f260116cc)\n3. [An introduction to spatial transcriptomics for biomedical research (Genome Medicine)](https://link.springer.com/article/10.1186/s13073-022-01075-1)\n4. [Spatial Transcriptomic Technologies (Cells, 2023)](https://pmc.ncbi.nlm.nih.gov/articles/PMC10453065/)\n5. [Systematic benchmarking of high-throughput subcellular spatial transcriptomics platforms across human tumors (Nature Communications, 2025)](https://www.nature.com/articles/s41467-025-64292-3)\n6. [A practical guide for choosing an optimal spatial transcriptomics technology from seven major commercially available options (BMC Genomics, 2025)](https://link.springer.com/article/10.1186/s12864-025-11235-3)\n7. [Resolve the whole transcriptome within tissue architecture (10x Visium technical note, Illumina)](https://assets-web.prd-web.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/10x-visium-tech-note-m-gl-00177/10x-visium-tech-note-m-gl-00177.pdf)\n8. [A guidebook of spatial transcriptomic technologies, data resources and analysis approaches (Genome Biology)](https://pmc.ncbi.nlm.nih.gov/articles/PMC10781722/)\n9. [Systematic comparison of sequencing-based spatial transcriptomic methods (Nature Methods, 2024)](https://www.nature.com/articles/s41592-024-02325-3)\n10. [Ten quick tips for spatial transcriptomics analysis (PLOS Computational Biology)](https://journals.plos.org/ploscompbiol/article/file?id=10.1371%2Fjournal.pcbi.1014757&type=printable)\n11. [Integrative spatial profiling of 3D genome organization and gene expression in tissue (Cell, 2026)](https://doi.org/10.1016/j.cell.2026.07.039)\n12. [Three-dimensional single-cell transcriptome imaging of thick tissues (eLife)](https://elifesciences.org/articles/90029)\n13. [Whole-transcriptome-scale isoform-resolved spatial imaging of single cells in tissues (Cell, 2026)](https://doi.org/10.1016/j.cell.2026.06.027)\n\n---\n*Topic: Encyclopedia › Life and health › Biological foundations › RNA and gene regulation › RNA elements, catalytic RNAs, and technologies › RNA methods, databases, and resources*\n\n*Initially written Sep 29, 2026 · Reviewed: Sep 30, 2026 · Edited: Sep 30, 2026 · Last review: Sep 30, 2026*\n\n*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*\n\nLicense: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license\n", "same_as": [], "url": "https://www.edgepedia.org/spatial-transcriptomics", "markdown_url": "https://www.edgepedia.org/spatial-transcriptomics.md", "license": { "name": "Edgepedia Community License 1.0", "url": "https://www.edgepedia.org/edgepedia/license", "summary": "Free with credit, commercial use included. AI training is open to everyone. For other uses, organizations over USD 100M in revenue or 100M monthly users license separately.", "spdx": "LicenseRef-Edgepedia-Community-1.0" }, "credit": "\"Spatial transcriptomics\", Edgepedia (EdgeChat), https://www.edgepedia.org/spatial-transcriptomics. Edgepedia Community License 1.0.", "credit_md": "\"[Spatial transcriptomics](https://www.edgepedia.org/spatial-transcriptomics)\", Edgepedia (EdgeChat), [https://www.edgepedia.org/spatial-transcriptomics](https://www.edgepedia.org/spatial-transcriptomics). [Edgepedia Community License 1.0](https://www.edgepedia.org/edgepedia/license).", "credit_html": "\"Spatial transcriptomics\", Edgepedia (EdgeChat), https://www.edgepedia.org/spatial-transcriptomics. Edgepedia Community License 1.0.", "speakable": "Spatial transcriptomics is a family of molecular biology methods that maps gene expression across intact tissue while recording each transcript's position, introduced by Patrik Ståhl in 2016." }