Sample Submission Guidelines
Stereo-seq Spatial Transcriptomics for High-Resolution Tissue Mapping
Stereo-seq is a spatial transcriptomics approach used to map gene expression across tissue sections while keeping spatial location information. Instead of dissociating tissue and losing its original architecture, Stereo-seq allows researchers to study where transcripts are detected, how expression patterns change across tissue regions, and how molecular signals relate to morphology.
This service is a strong fit when location is central to your question. Your project may need to compare tumor regions, map developmental structures, study brain architecture, examine immune tissue organization, build organ atlases, or place scRNA-seq-defined cell states back into tissue space. In these studies, spatial context is not just an added layer. It is often the key to interpreting the biology.
What Stereo-seq Can Help Reveal
- Which genes show region-specific expression across a tissue section?
- How are spatial clusters or tissue domains organized?
- Which marker genes define different spatial regions?
- How do tissue architecture and gene expression patterns align?
- Where are inferred cell populations distributed across the tissue?
- How do spatial regions differ between groups, conditions, or developmental stages?
- Can scRNA-seq cell states be mapped back into tissue context?
When Researchers Use Stereo-seq
Researchers often come to us when standard RNA sequencing, scRNA-seq, or imaging alone cannot answer a spatial question. Common use cases include tumor microenvironment research, developmental biology, embryo mapping, brain and neuroscience studies, organ atlas construction, immune microenvironment studies, plant tissue mapping, model organism research, and spatial multi-omics planning.
Why Spatial Context Matters Beyond scRNA-seq and Histology
Many tissue biology questions cannot be answered by expression values alone. Location matters. A gene expression pattern may look clear in a matrix, but its meaning often depends on where it appears in the tissue, which region it belongs to, and which neighboring structures surround it.
scRNA-seq Loses Tissue Organization
Single-cell RNA sequencing is powerful for identifying cell states and transcriptional programs. However, tissue dissociation removes the original spatial relationships among cells. Stereo-seq can complement scRNA-seq by adding spatial location back to the analysis.
Histology and IHC Are Marker-Limited
Histology and marker-based staining provide valuable tissue morphology information, but they usually do not provide transcriptome-wide expression information across the full tissue section.
Stereo-seq Bridges Gene Expression and Tissue Architecture
Stereo-seq generates spatially indexed expression data, helping your team review spatial expression maps, tissue domains, marker genes, and integrated visualizations.
How Stereo-seq Works: From Tissue Review to Spatial Expression Maps
A successful Stereo-seq project starts before the tissue section enters the spatial workflow. Tissue quality, preservation, section orientation, morphology, RNA quality, and research goals all affect the final data. We review these details with you before project setup so the workflow fits your biological question.
1. Project Design and Tissue Review
We begin by reviewing your tissue type, species, preservation method, region of interest, biological groups, and analysis goals. We also discuss whether your project needs spatial clustering, marker gene mapping, scRNA-seq integration, multi-sample comparison, or custom visualization.
QC checkpoint: tissue type, preservation method, project groups, region of interest, spatial resolution goal, and analysis plan.
2. Tissue Preparation and Section Quality Review
Stereo-seq depends strongly on tissue section quality. Fresh frozen or OCT-embedded samples may be suitable, depending on tissue type and project design. Tissue morphology, freezing quality, section integrity, RNA preservation, and region orientation should be reviewed before the project proceeds.
QC checkpoint: tissue integrity, freezing quality, morphology, RNA preservation risk, section orientation, and region suitability.
3. Tissue Imaging and Spatial Capture
The tissue section is imaged and then processed for spatial transcript capture. The spatial capture array links captured transcripts to coordinates, allowing gene expression to be mapped back to tissue position.
QC checkpoint: tissue coverage, imaging quality, spatial capture area, tissue-to-array alignment, and region visibility.
4. Library Preparation and Sequencing
After spatial capture, captured transcripts are converted into sequencing libraries. Sequencing generates reads that are processed into a spatial expression matrix. Each expression value is linked to a coordinate, making it possible to visualize gene expression across the tissue section.
QC checkpoint: library quality, sequencing output, read quality, spatial barcode performance, and gene detection profile.
5. Spatial Data QC and Coordinate Alignment
After sequencing, we process the data to generate spatial expression outputs. We review read quality, mapping performance, gene detection, tissue coverage, coordinate consistency, and image alignment.
QC checkpoint: expression matrix quality, coordinate mapping, image registration, tissue coverage, detected gene distribution, and sample metadata.
6. Spatial Bioinformatics and Report Delivery
We analyze spatial clusters, tissue domains, marker genes, region-level expression patterns, and optional scRNA-seq integration. At delivery, you receive raw and processed data, QC summaries, spatial maps, tables, figures, and a project report.
Stereo-seq vs scRNA-seq, Visium HD, Visium FF, Xenium, and Histology
Different tissue-based questions need different methods. Stereo-seq is strong when the goal is high-resolution spatial gene expression mapping across tissue sections, but it is not the only spatial option. We help you select the approach that best fits your sample type, tissue condition, resolution needs, and downstream analysis goals.
| Dimension | Stereo-seq | 10x Visium HD | 10x Visium FF | 10x Xenium In Situ | scRNA-seq | Histology / IHC |
|---|---|---|---|---|---|---|
| Main goal | High-resolution tissue-scale spatial transcriptomics | High-resolution spatial profiling in the 10x workflow ecosystem | Spatial transcriptomics for FFPE-compatible projects | Targeted in situ RNA panel analysis with cell segmentation | Cell-state discovery from dissociated cells | Tissue morphology and selected marker review |
| Tissue context | Preserved | Preserved | Preserved | Preserved | Lost after dissociation | Preserved |
| Molecular scope | Transcriptome-scale spatial expression | Platform-dependent spatial gene expression | Platform-dependent spatial gene expression | Targeted gene panel | Transcriptome-wide single-cell expression | Marker-limited |
| Spatial output | Coordinates, expression maps, clusters, spatial domains | Coordinates, expression maps, spatial features | Coordinates and FFPE-compatible spatial outputs | In situ gene maps and segmented cell-level outputs | Cell clusters and gene expression profiles | Tissue images and marker staining patterns |
| Best-fit question | Where are gene expression programs located across tissue regions? | How can I run high-resolution spatial profiling in a 10x workflow? | How can I study archived FFPE tissues spatially? | Where are selected transcripts located at high resolution? | What cell states are present in a tissue sample? | What does the tissue look like and where are selected markers found? |
| scRNA-seq integration | Useful for reference-informed annotation | Useful | Useful | Useful for targeted interpretation | Can serve as reference | Not transcriptome-wide |
| Key limitation | Tissue quality and data interpretation require careful planning | Platform and sample constraints should be reviewed | FFPE RNA quality and assay design matter | Targeted panel limits discovery breadth | Spatial organization is removed | Molecular breadth is limited |
| Best-fit user | Tissue atlas, tumor heterogeneity, developmental, brain, immune, and organ mapping projects | Researchers preferring a 10x high-resolution spatial workflow | Researchers working with FFPE tissue material | Researchers focused on targeted in situ marker panels | Researchers building cell-state references | Researchers needing morphology or selected marker confirmation |
How We Help You Select the Right Spatial Strategy
Choose Stereo-seq when you need high-resolution tissue-scale spatial gene expression mapping and broad transcriptomic discovery across tissue regions.
Choose 10x Visium HD Spatial Transcriptomics Service when you prefer a 10x high-resolution spatial workflow.
Choose 10x Visium FF Spatial Transcriptomics Service when FFPE tissue compatibility is a primary project requirement.
Choose 10x Xenium In Situ Service when targeted in situ gene panels and cell segmentation are more important than broad discovery.
Choose Spatial Multi-Omics Sequencing Services when transcriptomics should be combined with additional spatial or omics layers.
Bioinformatics Analysis and Deliverables
Stereo-seq projects generate complex spatial data. We process these data into clear outputs so your team can review expression patterns, spatial structure, gene markers, and integrated annotations.
Standard Spatial Transcriptomics Analysis
- Raw sequencing data QC
- Spatial barcode processing
- Spatial expression matrix generation
- Image and coordinate alignment
- Tissue coverage review
- Detected gene and read distribution assessment
- Spatial clustering
- Spatial domain detection
- Marker gene analysis
- Region-level expression comparison
- Spatial gene expression visualization
Integrated Spatial Analysis
For projects with additional data or custom questions, we can extend the analysis with scRNA-seq reference integration, inferred cell-type distribution, spatial niche or neighborhood analysis, multi-sample comparison, group-level region comparison, pathway or GO enrichment, tumor-stroma-immune region comparison, custom tissue annotation, and custom spatial visualization.
| Deliverable | What It Shows | Why It Matters |
|---|---|---|
| Raw sequencing data | Original sequencing output | Supports data storage and downstream reanalysis |
| Spatial expression matrix | Gene counts linked to spatial coordinates | Core data for spatial transcriptomics analysis |
| Tissue image | Morphology and section context | Helps connect gene expression with tissue architecture |
| Spatial coordinate file | Location information for expression values | Enables spatial mapping and visualization |
| QC summary | Read quality, mapping, detected genes, tissue coverage, and processing metrics | Helps evaluate data quality |
| Spatial cluster map | Tissue regions grouped by expression similarity | Reveals spatial organization |
| Spatial domain results | Region-level expression patterns | Supports biological interpretation of tissue structure |
| Marker gene table | Genes enriched in clusters or regions | Helps define molecular features of tissue regions |
| scRNA-seq integration outputs | Reference-informed cell-type or cell-state distribution | Connects single-cell information to tissue space |
| Final report | Methods, QC, figures, tables, and interpretation notes | Provides a structured project summary |
Sample Requirements for Stereo-seq
Sample requirements depend on tissue type, preservation method, section format, tissue morphology, RNA quality, and the selected spatial workflow. We confirm the final plan before sample submission because tissue section needs can vary by project.
The table below keeps Stereo-seq-specific section values in project review status. General RNA integrity expectations are included only as practical reference points for transcriptomics projects, while tissue section dimensions, slide format, section thickness, and capture area should be confirmed before submission.
| Sample Type | Preservation | Recommended Format | Section Requirements | Tissue / RNA Quality | Imaging / Morphology Notes | Shipping | Best-Fit Workflow | Notes |
|---|---|---|---|---|---|---|---|---|
| Fresh frozen tissue | Fresh frozen | Tissue block or prepared section | Confirm before submission | Preserve RNA quality; general transcriptomics reference: RIN ≥7 | Region of interest and tissue orientation should be clear | Dry ice | Stereo-seq | Avoid repeated freeze-thaw cycles |
| OCT-embedded tissue | OCT-embedded frozen tissue | Block or section | Confirm before submission | Confirm before submission | OCT quality and section morphology should be reviewed | Dry ice | Stereo-seq | Suitable format depends on tissue and section plan |
| Tumor tissue | Fresh frozen or OCT-embedded | Tissue block or section | Confirm before submission | Necrosis and low-quality regions should be reviewed | Tumor, stromal, immune, and boundary regions may require careful selection | Dry ice | Stereo-seq or integrated spatial analysis | Region annotation improves interpretation |
| Brain tissue | Fresh frozen or OCT-embedded | Tissue block or section | Confirm before submission | Confirm before submission | Anatomical orientation is important | Dry ice | Stereo-seq | Useful for region and layer mapping |
| Embryo or organ tissue | Fresh frozen or OCT-embedded | Whole section or selected region | Confirm before submission | Confirm before submission | Developmental stage and orientation should be documented | Dry ice | Stereo-seq | Metadata are important for stage comparison |
| Plant tissue | Fresh frozen or optimized preparation | Tissue block or section | Confirm before submission | Confirm before submission | Autofluorescence, cell wall structure, and tissue handling should be reviewed | Confirm before submission | Stereo-seq with custom review | Reference support may affect analysis depth |
| Customer-supplied spatial data | Data files | Expression matrix, coordinates, images, and metadata | Not applicable | Not applicable | Image and coordinate files should be complete | Secure data transfer | Custom reanalysis | Reference files and group metadata are required |
If you are unsure whether your sample is suitable, contact us before collection or sectioning. Early review helps reduce avoidable sample risk and makes the downstream analysis plan clearer.
Discuss Your ProjectApplications of Stereo-seq in Spatial Biology
Stereo-seq supports tissue biology studies where gene expression needs to be interpreted with spatial location, tissue structure, and region-level context.
Tumor Microenvironment and Tissue Heterogeneity
Stereo-seq can help researchers map region-specific expression across tumor tissue, stromal areas, immune-rich regions, invasive boundaries, and tissue niches.
Developmental Biology and Embryo Mapping
Developmental studies often require spatial context because gene expression changes across anatomical regions and developmental structures.
Brain, Neuroscience, and Organ Atlas Research
Brain and organ atlas projects benefit from spatial gene expression maps that preserve tissue architecture.
Immunology and Inflammatory Tissue Research
Stereo-seq can help examine immune-enriched regions, inflammatory niches, tissue boundaries, and region-level gene expression patterns.
Plant and Model-Organism Spatial Biology
Stereo-seq can support spatial mapping of tissue layers, developmental structures, and region-specific gene expression when sample preparation and annotation resources are suitable.
Why Choose CD Genomics for Stereo-seq
We support Stereo-seq as a complete spatial transcriptomics workflow, not just a sequencing run. Our team helps you think through tissue suitability, workflow selection, spatial data quality, bioinformatics strategy, and final deliverables.
- End-to-End Spatial Transcriptomics Support: We support tissue review, section planning, spatial capture workflow support, sequencing, QC, spatial clustering, spatial domain analysis, marker gene mapping, and report delivery.
- Custom Spatial Bioinformatics: We help process spatial expression matrices, align coordinates, detect spatial clusters, map marker genes, compare tissue regions, and integrate scRNA-seq references where appropriate.
- Integration with Spatial Multi-Omics and Single-Cell Data: We can help connect Stereo-seq results with single-cell references, spatial multi-omics strategies, or related spatial platforms.
- Clear Deliverables for Research Teams: We organize outputs into raw data, processed matrices, QC summaries, spatial maps, marker tables, integrated visualizations, and final analysis reports.
Demo Results: What Stereo-seq Data Can Look Like
Stereo-seq data can be delivered as spatial maps, expression matrices, cluster results, marker gene tables, and integrated visual reports. The exact outputs depend on sample quality, tissue type, project design, and analysis goals.
Spatial Gene Expression and Tissue Region Map
A spatial gene expression view shows where selected genes are detected across the tissue section. In a typical report, this may include a tissue image paired with expression overlays, helping your team see whether marker genes localize to specific tissue regions, layers, boundaries, or niches.
- Spatial gene expression maps
- Region-specific marker visualization
- Tissue image overlay
- Coordinate-based expression views
- Region-level expression comparison
Spatial Clustering and Marker Gene Heatmap
Spatial clustering groups tissue locations with similar expression patterns. Marker gene analysis then helps define the molecular features of each spatial cluster or tissue domain.
- Spatial cluster map
- Spatial domain annotation
- Marker gene table
- Marker gene heatmap
- Cluster-level expression comparison
scRNA-seq Integration and Inferred Cell-Type Distribution
When a suitable scRNA-seq reference is available, spatial data can be integrated with single-cell profiles to infer where cell types or cell states are enriched.
- Reference-informed cell-type distribution map
- Cell-state enrichment by tissue region
- Spatial niche or neighborhood view
- Integrated UMAP-to-space interpretation
- Multi-sample distribution comparison
Frequently Asked Questions About Stereo-seq Service
1. What is Stereo-seq?
Stereo-seq is a spatial transcriptomics approach used to map gene expression across tissue sections while preserving spatial coordinates. It helps researchers study where genes are expressed and how expression patterns relate to tissue structure.
2. How is Stereo-seq different from scRNA-seq?
scRNA-seq profiles dissociated cells and is useful for identifying cell states, but it removes tissue location. Stereo-seq preserves spatial information, allowing gene expression to be mapped across tissue regions.
3. How is Stereo-seq different from 10x Visium HD?
Stereo-seq and 10x Visium HD are both spatial transcriptomics approaches, but they differ in platform design, workflow, and project fit. CD Genomics can help you choose between Stereo-seq and 10x Visium HD Spatial Transcriptomics Service based on tissue type, resolution goals, and analysis needs.
4. How is Stereo-seq different from Xenium?
Stereo-seq is used for spatial transcriptomics discovery across tissue sections. 10x Xenium In Situ Service is more suitable when the project focuses on targeted in situ gene panels and cell segmentation. The best choice depends on whether you need broad discovery or targeted in situ mapping.
5. What tissue samples are suitable for Stereo-seq?
Fresh frozen tissue, OCT-embedded tissue, tumor tissue, brain tissue, embryo or organ tissue, plant tissue, and model-organism tissue may be considered. Final suitability depends on tissue quality, preservation method, section format, and project goals.
6. Can Stereo-seq be integrated with scRNA-seq data?
Yes. When a suitable scRNA-seq reference is available, Stereo-seq data can be integrated with single-cell profiles to support reference-informed cell-type or cell-state mapping in tissue space.
7. What deliverables are included?
Typical deliverables include raw sequencing data, spatial expression matrices, tissue images, coordinate files, QC summaries, spatial cluster maps, marker gene tables, gene expression maps, integrated analysis outputs, and a final report.
8. Can Stereo-seq identify cell types?
Stereo-seq can support reference-informed cell-type or cell-state annotation when suitable reference data are available. These annotations should be interpreted with reference quality, tissue context, and marker evidence in mind.
9. What are the main limitations of spatial transcriptomics interpretation?
Major limitations include tissue quality, section preparation, RNA preservation, spatial resolution fit, reference availability, data complexity, and uncertainty in cell-type annotation when reference datasets are incomplete.
10. Can CD Genomics analyze customer-supplied spatial transcriptomics data?
Yes. We can review customer-supplied spatial transcriptomics data when expression matrices, coordinates, tissue images, metadata, and reference files are available.
Case Study: Stereo-seq for Mouse Organogenesis Spatial Atlas Construction
Background
Mouse organogenesis is a highly spatial process. During development, tissues form through coordinated changes in gene expression, cell-state distribution, anatomical structure, and regional organization. Standard transcriptome profiling can measure gene expression, but it does not preserve where those expression programs occur in the embryo. A spatial transcriptomics approach is therefore valuable for building developmental atlases that connect molecular patterns with anatomical structures.
The study Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays used DNA nanoball-patterned arrays to build a spatiotemporal transcriptomic atlas of mouse organogenesis. This paper is a strong public literature example for Stereo-seq because it directly demonstrates tissue-scale spatial gene expression mapping in a developmental system.
Methods
The study used a DNA nanoball-patterned array strategy to capture transcriptomic information with spatial coordinates. Mouse embryo sections were processed so gene expression could be assigned back to spatial locations across developing tissues. The workflow connected spatial capture, sequencing, image-based tissue context, gene expression mapping, and computational analysis.
Figure 1 of the paper presents the study design and technology overview. It illustrates how spatially indexed transcriptomic information can be generated from tissue sections and used to build spatial maps across mouse organogenesis.
Results
The study generated spatial transcriptomic maps that retained tissue-scale location information across mouse organogenesis. These maps showed that gene expression patterns could be connected to anatomical structures and developmental regions rather than analyzed as a location-free expression matrix.
- Spatial gene expression maps preserved embryo tissue organization.
- Region-specific expression patterns could be assigned to developing structures.
- Spatially resolved data supported atlas-level interpretation across organogenesis.
- The DNA nanoball-patterned array strategy enabled large-area spatial transcriptomic mapping with high spatial detail.
- Figure 1 shows the overall Stereo-seq study design and spatial atlas workflow, linking tissue structure with transcriptome-wide expression.
Figure 1 from Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays shows the Stereo-seq study design and spatial atlas workflow connecting tissue structure with transcriptome-wide expression.
Conclusion
This study shows how Stereo-seq can support high-resolution spatial transcriptomics for large developmental tissue systems. For researchers planning tissue atlas, developmental biology, organ mapping, or region-specific expression studies, the key value is not only data generation. The value comes from connecting tissue preparation, spatial capture, sequencing, coordinate-aware analysis, and interpretable visual outputs into one project workflow.
