Stereo-seq Spatial Transcriptomics Service | 500 nm Sub-Cellular Resolution
Stereo-seq is a high-resolution spatial transcriptomics technology that captures RNA molecules directly on tissue sections while preserving spatial coordinates. Built on a DNA nanoball (DNB) patterned array with ~500 nm resolution, it delivers subcellular-level gene expression maps across large tissue sections (up to 13 × 13 cm), enabling researchers to map cell types, tumor heterogeneity, and transcriptional boundaries.
CD Genomics provides end-to-end Stereo-seq services from tissue sectioning and library prep to DNBSEQ sequencing and spatial bioinformatics. We deliver expression matrices, cell clustering, differential analysis, and publication-ready visuals, supporting fresh frozen and FFPE samples across developmental biology, oncology, neuroscience, and pathology.
- Sub-cellular resolution at approximately 500 nm using DNA Nanoball patterned arrays with CID spatial barcodes
- Fresh frozen and FFPE tissue compatibility; maximum chip field of view up to 13 × 13 cm for organ-scale studies
- Spatial gene expression profiling, clustering, and cell-type deconvolution with scRNA-seq reference data
- End-to-end service from sample receipt through DNBSEQ sequencing to publication-ready figures and data tables
Services are provided for research use only.
What is Stereo-seq spatial transcriptomics?
Stereo-seq (Spatial Enhanced REsolution Omics-sequencing) is a sequencing-based spatial transcriptomics technology that captures and maps mRNA molecules directly from tissue sections while preserving their original spatial coordinates. Developed on a DNA Nanoball (DNB) patterned array platform, Stereo-seq uses Coordinate ID (CID) spatial barcodes to record the precise position of each captured transcript before sequencing on the DNBSEQ platform.
The defining feature of Stereo-seq is its sub-cellular resolution. With DNB spacing of approximately 500 nm — roughly 1/20 the diameter of a typical mammalian cell (~10 μm) — each cell is covered by hundreds of capture spots on the chip. This density enables transcript localization within cells, distinguishing cytoplasmic from nuclear signals, and supports accurate cell boundary segmentation without relying solely on computational deconvolution.
Unmatched spatial resolution
The spacing between adjacent spots on the Stereo-seq chip is approximately 500 nm, achieving a spatial resolution of 500 nm at the sub-cellular level. With a typical mammalian cell measuring roughly 10 μm in diameter, each cell corresponds to several hundred capture spots on the chip — a resolution level not matched by other commercially available spatial omics platforms.
Organ-scale capture area
The standard Stereo-seq chip covers a 1 cm × 1 cm detection area, with a smaller 0.5 cm × 0.5 cm chip also available. Custom chip dimensions — including 2 cm × 3 cm, 5 cm × 5 cm, and up to 13 cm × 13 cm — can be configured to accommodate large or whole-organ tissue sections, preserving spatial architecture at scale.
Higher gene capture per unit area
Parallel comparison of Molecular barcode counts captured per unit area across spatial transcriptomics platforms shows that the Stereo-seq chip detects substantially more unique molecular identifiers within the same detection area, yielding richer gene expression information from each tissue section.
Compared with standard spot-based spatial transcriptomics platforms that capture signals from multiple cells within a single 55 μm spot, Stereo-seq provides true sub-cellular resolution. This matters most when you need to:
- Resolve gene expression boundaries between adjacent but functionally distinct cell populations
- Detect transcript localization within subcellular compartments, such as nucleus versus cytoplasm
- Segment individual cells directly from the spatial data without relying on external single-cell references
- Map large tissue regions at organ scale without sacrificing resolution, using chips up to 13 × 13 cm
By combining nanoscale resolution with centimeter-scale field of view, Stereo-seq bridges the gap between single-cell sequencing and tissue-level spatial biology.
How our Stereo-seq service workflow works
Our Stereo-seq service follows an integrated five-step pipeline, with quality control checkpoints at key transitions to protect your samples and ensure data quality.
- Tissue sectioning and QC
Fresh frozen or FFPE tissue is sectioned to the recommended thickness and placed onto the Stereo-seq DNB capture chip. RNA integrity (RIN) is assessed on receipt for fresh frozen samples, and DV200 is evaluated for FFPE blocks before proceeding.
- In-situ RNA capture with CID spatial barcodes
Tissue sections are permeabilized to release mRNA, which binds to DNB probes carrying spatial Coordinate ID (CID) barcodes on the chip surface. Each transcript is tagged with its precise tissue location before further processing.
- cDNA synthesis and library preparation
Captured mRNA is reverse-transcribed into cDNA on the chip, incorporating the spatial barcode into each cDNA molecule. Libraries are constructed and amplified for sequencing, with a library QC checkpoint to confirm fragment size distribution and concentration.
- DNBSEQ high-throughput sequencing
Libraries are sequenced on the DNBSEQ platform with read depth matched to your tissue type and research goals. Sequencing configuration — including read length and depth — is agreed during study design to balance cost and data resolution.
- Spatial data reconstruction and bioinformatics
Sequencing reads are demultiplexed, aligned to the reference genome, and mapped back to tissue coordinates using CID information. Spatial gene expression matrices are then generated, followed by clustering, visualization, and optional advanced analyses such as cell-type deconvolution or spatial trajectory inference.
Sample requirements and submission
Both fresh frozen and FFPE tissue types are accepted for Stereo-seq. Samples are screened against standardized QC criteria upon receipt, and you are notified before any processing proceeds if quality thresholds are not met.
| Parameter | Fresh frozen tissue | FFPE tissue |
|---|---|---|
| Sample format | OCT-embedded tissue blocks; recommended section thickness ~10 μm | Standard pathology blocks or charged slides; recommended section thickness 4–5 μm |
| Minimum tissue size | 500 μm × 500 μm | Recommend area sufficient for histological evaluation |
| Maximum coverage | Tissue area should not exceed 80% of chip capture surface | |
| Shipping condition | Dry ice; avoid freeze-thaw cycles | Room temperature |
| QC checkpoint | RNA integrity (RIN > 8) assessed at sample receipt | DV200 evaluation performed on receipt; section integrity screening |
| Backup aliquots | Provide at least 2 backup aliquots or sections for initial QC and protocol optimization | |
| Staining option | H&E or ssDNA staining available (single selection per section) | |
QC and sample interception policy: If a sample fails initial quality assessment (RIN below threshold for fresh frozen, or insufficient DV200 for FFPE), the workflow is paused and you are contacted before any library preparation or sequencing proceeds. No charges are incurred for downstream processing of samples that do not pass QC.
For multi-tissue projects where several regions of interest are embedded in a single block, contact us during study design to confirm layout compatibility. Tissue sections should be intact, without cracks, tears, or excessive necrosis. Supporting documentation such as prior histology images or RNA QC reports for the same tissue cohort is welcomed to expedite the feasibility review.
Demo results: what your data looks like
Below are representative Stereo-seq data visualizations from our spatial transcriptomics pipeline, illustrating the spatial gene expression outputs you can expect upon project completion.
Spatial clustering maps with tissue histology overlay
Unsupervised spatial clustering identifies transcriptionally distinct tissue domains directly from Stereo-seq data. Each color represents a cluster of capture spots with shared gene expression profiles. Overlaying these clusters on the H&E-stained tissue image reveals how molecular boundaries align with visible histology — for example, separating cortical layers, tumor stroma from tumor nests, or distinct developmental zones within an organ.
These maps are delivered as publication-ready figures, with cluster labels, scale bars, and legends. Underlying cluster membership data and spatial coordinates are provided in structured tables for your own downstream analysis.
Spatial gene expression heatmaps
Gene-specific expression is visualized as a color-gradient heatmap projected onto the tissue image. This reveals where a gene of interest is actively transcribed — for instance, showing a tumor marker enriched within the lesion core, an immune checkpoint concentrated at the invasive margin, or a developmental transcription factor restricted to a specific embryonic layer.
Expression intensity data for all detected genes are delivered as a count matrix in standard formats (MTX, HDF5) compatible with Seurat, Scanpy, Stereopy, and other spatial analysis toolkits. The full gene-by-spot matrix allows you to interrogate any gene without re-running the pipeline.
Bioinformatics analysis and deliverables
Every Stereo-seq project includes a standard bioinformatics data package, with advanced analyses available as optional add-ons based on your study objectives. Below is an overview of the analysis modules we deliver.
Standard data analysis
Data quality control
- Sequence quality assessment and filtering
- Read alignment to reference genome
- Gene-level quantification with spatial coordinates
- Post-quantification QC metrics and reporting
Dimensionality reduction and clustering
- Spot proportion and coverage visualization
- Marker gene identification per spatial cluster
- Gene expression visualization on tissue coordinates
Inter-group differential analysis
- Differential gene expression between sample groups or regions
- Functional enrichment analysis of differentially expressed genes
Advanced data analysis
Spatial cell-type identification
- Cell-type deconvolution via MIA, SPOTlight, and RCTD algorithms
- Integration with 10x Single-cell RNA-Seq reference data for cell-type proportion mapping
Spatial neighborhood analysis
- Characterization of cellular neighborhoods and spatial co-localization patterns
- Quantification of cell-type adjacency and spatial organization metrics
Spatial pseudotime trajectory
- Reconstruction of spatial differentiation and migration gradients using monocle2 and STlearn
- Inference of developmental or disease progression trajectories across tissue space
Spatial communication analysis
- Ligand-receptor interaction mapping via cellphoneDB and STlearn
- Identification of signaling hotspots and paracrine communication axes
Tumor boundary analysis
- Delineation of tumor margins and invasive fronts using Cotrazm
- Characterization of gene expression gradients at tumor-normal interfaces
WGCNA and functional enrichment
- Weighted gene co-expression network analysis (WGCNA) for module discovery
- Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA)
- Pathway-level functional interpretation of spatial gene modules
All deliverables include raw FASTQ files, processed count matrices in standard formats (MTX, HDF5), spatial coordinates, tissue images, and publication-quality figures. Advanced analyses are scoped during study design to match your biological questions. For projects requiring integrated spatial multi-omics — combining transcriptomic and proteomic layers on the same or adjacent tissue sections — additional consultation is available.
Stereo-seq vs other spatial transcriptomics platforms
Selecting the right spatial platform depends on your resolution requirements, sample type, and tissue size. The table below compares Stereo-seq with widely used alternatives to help you evaluate fit.
| Stereo-seq | 10x Visium | 10x Visium HD | |
|---|---|---|---|
| Spatial resolution | ~500 nm (DNB spacing) | 55 μm (spot diameter) | ~2 μm |
| Sequencing platform | DNBSEQ | Illumina | Illumina |
| Fresh frozen tissue | Supported | Supported | Supported |
| FFPE tissue | Supported (Stereo-seq OMNI) | Supported | Supported |
| Maximum field of view | Up to 13 × 13 cm | 6.5 × 6.5 mm | 6.5 × 6.5 mm (standard); 11 × 11 mm (large) |
| Capture principle | DNB patterned array + CID spatial barcodes | Oligo-dT capture probes on array | Oligo-dT capture on high-density array |
Specifications are based on publicly available platform documentation and are provided for general comparison. Contact us to discuss which platform best fits your study design and sample type.
When to choose Stereo-seq: Choose Stereo-seq when your research requires sub-cellular resolution to resolve fine cell boundaries, when your tissue area exceeds standard capture array dimensions and demands organ-scale mapping, or when you need DNBSEQ platform compatibility. For projects where FFPE compatibility with near-single-cell resolution is the primary requirement, discuss assay selection with our team to match your biological question to the most suitable platform.
Research applications
Stereo-seq is applicable wherever tissue architecture and spatial gene expression jointly drive biological interpretation. The following areas benefit particularly from its sub-cellular resolution and organ-scale field of view.
Tumor microenvironment mapping
Stereo-seq resolves the spatial organization of malignant, stromal, and immune cells within the tumor microenvironment at sub-cellular resolution. You can distinguish inflamed from immune-excluded regions, map checkpoint molecule expression gradients, and identify spatial niches where cell-cell interactions drive immune evasion or therapy resistance.
Neuroscience and brain mapping
The mammalian brain contains hundreds of transcriptionally distinct cell types organized into precise anatomical layers and nuclei. Stereo-seq's 500 nm resolution captures this complexity across entire brain sections, from cortical laminae to deep subcortical structures. Spatial gene expression maps support studies of neural circuit organization, neurodegenerative disease pathology, and region-specific transcriptional programs.
Developmental biology
Embryonic development unfolds through precisely coordinated spatial and temporal gene expression programs. Stereo-seq captures these gradients across whole embryos or organ anlagen, revealing morphogen signaling boundaries, cell fate specification trajectories, and transient transcriptional states that are lost in dissociated single-cell workflows. Its large chip area (up to 13 × 13 cm) accommodates whole-organism sections at key developmental stages. For studies of later developmental processes and tissue patterning, cross-reference our developmental biology solutions.
Pathology and biomarker discovery
Archival FFPE blocks from clinical cohorts represent a vast resource for retrospective spatial studies. Stereo-seq OMNI supports FFPE tissue, enabling transcriptome-wide spatial profiling of pathology specimens. This unlocks biomarker discovery in well-annotated tissue collections — for example, identifying spatially restricted gene signatures associated with treatment response, disease progression, or histopathological subtypes.
Case study: benchmarking subcellular spatial transcriptomics across human tumors
Source: Ren, Pengfei, et al. "Systematic benchmarking of high-throughput subcellular spatial transcriptomics platforms across human tumors." Nature Communications, vol. 16, article 9232, 2025. DOI: 10.1038/s41467-025-64292-3
Background: Subcellular-resolution spatial transcriptomics has created new opportunities to study complex tissue architecture, but researchers still need practical evidence to choose the right platform for each project. Human tumors are particularly challenging because malignant cells, stromal compartments, immune populations, and microbial signals can be spatially intermixed across heterogeneous regions. This study addressed that challenge by systematically benchmarking high-throughput subcellular spatial transcriptomics platforms across human tumor samples, using matched orthogonal references to evaluate platform performance in a biologically relevant setting.
Methods: The researchers analyzed serial tissue sections from treatment-naïve human colon adenocarcinoma, hepatocellular carcinoma, and ovarian cancer samples. Spatial transcriptomics data were generated using four high-throughput subcellular platforms: Stereo-seq v1.3, Visium HD FFPE, CosMx 6K, and Xenium 5K. Adjacent tissue sections were profiled by CODEX, and matched single-cell RNA sequencing data were produced as reference datasets. This design enabled cross-platform evaluation of capture sensitivity, specificity, diffusion control, cell segmentation, cell annotation, spatial clustering, transcript-protein concordance, and tissue-domain interpretation.
Results: The study showed that each subcellular spatial transcriptomics platform has distinct strengths depending on tissue type, sample preparation, and analysis goals. Stereo-seq v1.3 provided unbiased poly(A)-based transcriptome capture at approximately 0.5 μm resolution, supporting broad gene discovery and region-level spatial analysis across complex tumor tissues. By integrating spatial transcriptomics with CODEX and single-cell RNA-seq references, the authors compared platform performance across multiple analytical dimensions and released a uniformly processed multi-omics resource through the SPATCH portal for visualization and downstream method development.
Conclusion: This benchmarking study supports the practical value of Stereo-seq for research projects that require subcellular spatial resolution, wide transcriptome coverage, and spatially resolved analysis of complex tissue architecture. For tumor microenvironment studies and other heterogeneous tissue systems, Stereo-seq can help researchers move beyond region-level histology toward transcriptome-wide molecular mapping in tissue context, while allowing platform selection to be guided by resolution, tissue type, and biological objective.
Integration with single-cell and multi-omics analysis
Stereo-seq spatial data can be integrated with complementary omics layers to build a more complete picture of tissue biology.
Stereo-seq + scRNA-seq
- Use single-cell RNA-seq data as a reference to deconvolve Stereo-seq spatial spots, mapping cell-type proportions onto tissue coordinates and linking rare cell populations to their spatial niches.
Stereo-seq + spatial proteomics
- Combine Stereo-seq transcriptomic maps with mIHC spatial immune profiling or other protein-level readouts on adjacent sections, connecting gene expression programs to protein-level immune architecture.
Stereo-seq + epigenomics
- Pair spatial transcriptomics with spatial ATAC-seq or CUT&Tag to link chromatin accessibility and histone modification landscapes to gene expression domains in the same tissue context.
Multi-omics integration is scoped during study design. Our team can advise on which complementary assays add the most value for your specific research question and how sample preparation should be coordinated across platforms.
Frequently asked questions (FAQ)
References
- Ren, Pengfei, et al. "Systematic benchmarking of high-throughput subcellular spatial transcriptomics platforms across human tumors." Nature Communications, vol. 16, article 9232, 2025.
- Chen, Ao, et al. "Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays." Cell, vol. 185, no. 10, 2022, pp. 1777–1792.e21.
- "Stereo-seq V2: Spatial mapping of total RNA on FFPE sections with high resolution." Cell, 2025.
- STOmicsDB. "Stereo-seq public dataset repository." CNGBdb.
- STOmics. "Stereopy: Spatial transcriptomics analysis toolkit for Stereo-seq." GitHub.
- Han, Lei, et al. "Single-cell spatial transcriptomic atlas of the whole mouse brain." Neuron, vol. 113, no. 13, 2025, pp. 2141–2160.e9.
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