Organ-Scale Stereo-seq Spatial Transcriptomics Service
Large tissues and whole-organ sections create a study-design problem: how can broad anatomical context be retained without losing the local transcriptomic patterns that define regions, boundaries, and cell neighborhoods? Our organ-scale Stereo-seq service combines large-format spatial capture, high-density DNA nanoball arrays, and study-specific analysis for continuous tissue mapping.
Designed for studies that need:
- Large or continuous tissue coverage within a coordinated capture plan
- Approximately 500 nm center-to-center DNA nanoball spacing
- Multi-resolution visualization from organ regions to fine spatial domains
- Serial-section, developmental, atlas, or optional three-dimensional analysis
Why Organ-Scale Spatial Transcriptomics Requires Dedicated Planning
Organ-scale projects are not simply standard spatial experiments with a larger image. Tissue dimensions, orientation, section continuity, capture-area allocation, sequencing depth, data volume, and computational strategy must be coordinated before sectioning. Stereo-seq uses patterned DNA nanoball arrays with approximately 500 nm center-to-center spacing and supports large capture formats, enabling scalable bin-level analysis and cell-resolved analysis when image and segmentation quality permit.
Continuous Context
Retain anatomical relationships across broad regions instead of analyzing disconnected fields.
Flexible Resolution
Review data at multiple bin sizes and apply CellBin analysis when tissue imaging supports segmentation.
Serial Sections
Coordinate section order, spacing, and landmarks for cross-section comparison or reconstruction.
Atlas Analysis
Compare regions, stages, groups, or specimens within a harmonized spatial framework.
For a general platform overview, see our Stereo-seq Spatial Transcriptomics Service. This page focuses specifically on large-tissue and organ-scale project design.
Match Tissue Geometry to Capture and Analysis Goals
Figure 2. Tissue dimensions, orientation, and capture format are planned together.
Information Needed Before Sectioning
- Maximum tissue length, width, thickness, and expected usable area
- Anatomical orientation and landmarks that must remain visible
- Number of sections, spacing, order, and allocation to histology or validation
- Primary endpoint: region discovery, boundary analysis, cell mapping, atlas construction, or reconstruction
- Study groups, biological replicates, batch structure, and comparison priorities
- Whether matched single-cell or other omics data will support annotation and integration
Chip format and sequencing allocation are selected from the actual tissue footprint and biological goal. Very large specimens may require coordinated capture regions or serial-section strategies rather than a single universal format.
Organ-Scale Stereo-seq Service Workflow
- Study Design and Chip Sizing
Define tissue coverage, groups, resolution, section count, sequencing needs, and analysis endpoints.
- Tissue Orientation and Section Planning
Record anatomical axes, landmarks, section order, spacing, and allocation before cutting.
- Sample QC and Sectioning
Review preservation and RNA integrity, optimize cryosectioning, and inspect tissue morphology.
- Stereo-seq Spatial Capture and Library Preparation
Place qualified sections on the approved capture format and prepare spatially barcoded libraries.
- DNBSEQ Sequencing
Sequence libraries according to tissue area, complexity, and the approved analytical resolution.
- CellBin or Spatial Analysis and Delivery
Generate multi-resolution matrices, spatial domains, cell-level outputs when supported, and integrated maps.
Figure 3. Coordinated workflow from large-tissue planning to spatial delivery.
Sample Requirements and Section Strategy
Fresh-frozen or OCT-embedded tissue is the primary starting material for organ-scale projects. The values below are planning references. Final sample acceptance and chip choice depend on morphology, RNA quality, tissue footprint, and capture-format availability. Large-format FFPE requires a separate feasibility review.
| Sample Item | Recommended Quantity / Specification | QC or Planning Requirement |
|---|---|---|
| Fresh-frozen / OCT block | 1 intact block per biological replicate; ship on dry ice and store at −80°C | Orientation, maximum length × width × thickness, and a reference image are required before chip sizing |
| Cryosections | Approximately 10 µm per Stereo-seq section; reserve 10–20 additional 10 µm sections for RNA QC when needed | Sections must remain flat, intact, and free of folds, cracks, and excess OCT over the tissue |
| RNA integrity | RIN ≥4 accepted for routine fresh-frozen or OCT projects; RIN ≥6 recommended for higher-stringency projects | RNA integrity is assessed with morphology and tissue-specific performance, not as the only acceptance criterion |
| Capture formats | Common large formats include 1 × 2 cm, 2 × 2 cm, and 2 × 3 cm; custom formats may extend to 13 × 13 cm | Tissue should remain within the confirmed usable capture area; availability must be checked before sectioning |
| Adjacent morphology | At least 1 adjacent section for H&E or ssDNA imaging per analyzed level | Anatomical landmarks and section order must be recorded |
| Biological replication | At least 3 biological replicates per group are recommended for group-level comparisons | Groups, batches, section levels, and the primary statistical contrast should be defined in advance |
Sequencing and Primary Data Processing
| Item | Recommended Configuration |
|---|---|
| Sequencing platform | DNBSEQ-T7 or another validated MGI / Complete Genomics DNBSEQ system compatible with the selected Stereo-seq library |
| Read configuration | 50 bp Read 1 + 100 bp Read 2; a 10 bp sample barcode read is included when the library design requires it |
| Reference data amount | Approximately 300–600 million reads for a 0.5 × 0.5 cm chip and 1.5–2.0 billion reads for a 1 × 1 cm fresh-frozen chip; large-format projects are scaled by captured tissue area and pilot QC |
| Primary pipeline | Validated Stereo-seq Analysis Workflow and StereoMap release selected at project start; current projects can be configured around SAW v8.x and StereoMap v4.x |
| Output resolution | Bin 1 coordinate grid at 500 nm spacing; Bin 20 corresponds to 10 µm; CellBin output is provided when image segmentation quality supports cell assignment |
| Core QC | Valid CID rate, mapped reads, unique molecules, genes per bin or cell, tissue coverage, saturation, and replicate concordance |
Multi-Resolution and Organ-Scale Data Analysis
Analysis starts with the scale of the biological question. Broad bins can reveal anatomical domains and gradients, while smaller bins or CellBin outputs can examine local cell populations when morphology and segmentation quality support that interpretation.
Core Spatial Analysis
- Read, barcode, and spatial-coordinate QC
- Gene-by-bin matrices at approved resolutions
- Dimensionality reduction and spatial clustering
- Marker genes and region annotation
- Spatial expression maps and tissue-domain comparisons
- CellBin processing when image quality permits
Advanced Study Modules
- Serial-section alignment
- Optional three-dimensional reconstruction
- Developmental trajectory and spatial gradient analysis
- Cell–cell neighborhood and communication analysis
- Integration with single-cell references
- Cross-specimen atlas harmonization
Figure 4. Stereo-seq processing branches into multi-bin tissue analysis, optional CellBin analysis, and serial-section or atlas integration.
Custom analysis and integration can be extended through our Spatial Transcriptomics Data Analysis Service.
Representative Deliverables
Figure 5. Representative multi-scale tissue maps and cross-section outputs.
- Raw sequencing data and sequencing QC
- Spatial-coordinate and expression matrices
- Multi-resolution clustering and annotation files
- Whole-section and region-specific expression maps
- CellBin matrices and masks when applicable
- Serial-section or atlas outputs when included
- Methods, parameters, figures, tables, and analysis report
Research Questions Enabled by Organ-Scale Mapping
Developmental Biology
Following coordinated change across an embryo or developing organ requires both temporal and anatomical context. Continuous spatial maps connect gene-expression programs with regions, gradients, and emerging structures, helping you determine when and where developmental transitions occur. Explore our Spatial Omics Solutions for Developmental Biology.
Brain and Nervous System
Neurological disease and regeneration can affect several connected brain regions rather than one isolated field. Organ-scale profiling preserves long-range anatomy while revealing region-specific cell states and lesion-associated transitions, allowing you to distinguish local pathology from coordinated system-wide change.
Tumor Boundaries and Heterogeneity
Small sampling fields may miss invasive fronts, discontinuous clones, or distant immune niches. Large continuous sections map tumor, stromal, and immune programs from the core through the boundary and surrounding tissue, helping you identify spatial transitions that could explain invasion or regional treatment resistance.
Regeneration and Injury
To determine how repair signals propagate away from an injury site, broad tissue coverage can track progenitor states, inflammatory zones, and spatial expression trajectories across the affected organ. These maps help you relate the lesion core to transitional and recovering regions instead of analyzing each area separately.
Plant and Comparative Biology
Comparative studies often need to align gene programs across leaves, roots, embryos, developmental stages, or related organisms. Coordinated whole-structure mapping reveals conserved domains and region-specific expression patterns, supporting comparisons that retain anatomical context.
Three-Dimensional Tissue Atlases
A single two-dimensional section may not represent how a domain extends through a complex organ. Planned serial sections provide aligned spatial maps across tissue depth, helping you determine whether structures persist, connect, branch, or change between levels.
Case Study: A Spatiotemporal Atlas of Mouse Organogenesis
Source: Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays
Figure 6. Illustrative reconstruction linking whole-embryo spatial domains with developmental stages and organ-specific programs; not reproduced from the source paper.
Background
Mid- and late-gestation embryos contain multiple developing organs whose transcriptional programs change across both anatomical space and developmental time. The study examined whether large-field, high-density spatial transcriptomics could retain whole-embryo context while resolving local cell populations and organ-specific programs.
Methods
Chen and colleagues used Stereo-seq to build the Mouse Organogenesis Spatiotemporal Transcriptomic Atlas from 53 sagittal whole-embryo sections spanning embryonic day E9.5 to E16.5. The continuous sections captured multiple developing organs in their anatomical context rather than sampling isolated regions.
- Spatial count matrices were evaluated at multiple bin sizes to identify tissue domains and developmental gene-expression patterns.
- Image-based segmentation was applied to an E16.5 whole-embryo section to generate cell-resolved expression profiles.
- Unsupervised clustering, marker-based annotation, spatial gene analysis, and regulatory-network analysis were used to define cell types and region-specific programs.
- Sections across developmental stages were compared to infer the timing and direction of transcriptional changes during organ formation.
Results
After filtering, the E16.5 analysis retained 281,377 segmented cells, with averages of 1,107 unique molecules and 529 detected genes per cell. Unsupervised analysis identified 25 major cell types. Their spatial distributions matched organ anatomy, including cardiomyocytes, hepatocytes, neurons, chondrocytes, epithelial cells, and myoblasts. The atlas resolved spatial heterogeneity in the developing dorsal midbrain, reconstructed developmental trajectories, and mapped disease-associated loci to specific spatiotemporal windows of tissue vulnerability.
Conclusion
The atlas demonstrated that large continuous sections can connect cell-level or multi-bin expression patterns with organ anatomy and developmental stage. In a comparable service project, whole-section cluster maps, expression gradients, region-specific markers, spatial cell distributions, stage comparisons, trajectories, and aligned serial-section outputs can reveal where a lineage emerges, which boundary separates molecular programs, and when a disease-related pathway becomes active.
When Organ-Scale Stereo-seq Is the Right Choice
Organ-scale Stereo-seq is most useful when broad anatomical continuity is central to the research question and the tissue, sectioning strategy, capture format, sequencing allocation, and computational plan can be coordinated before data generation.
Best for
- Large fresh-frozen or OCT-embedded sections
- Whole-organ gradients, boundaries, and spatial domains
- Serial-section, developmental, or atlas-scale comparisons
Consider another approach when
If the project focuses on a small region, archived FFPE material, or a short list of known targets, a smaller-field discovery platform or Targeted Spatial Multi-Omics Profiling may be more efficient than an organ-scale design.
Frequently Asked Questions
References
- Chen A, Liao S, Cheng M, et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell. 2022;185:1777–1792.e21.
- Wei X, Fu S, Li H, et al. Single-cell Stereo-seq reveals induced progenitor cells involved in axolotl brain regeneration. Science. 2022;377:eabp9444.
- Bian X, Wang W, Abudurexiti M, et al. Integration Analysis of Single-Cell Multi-Omics Reveals Prostate Cancer Heterogeneity. Advanced Science. 2024;11:e2305724.
- Ren P, Zhang R, Wang Y, et al. Systematic benchmarking of high-throughput subcellular spatial transcriptomics platforms across human tumors. Nature Communications. 2025;16:9232.