Spatial Transcriptomics Service
What Is Spatial Transcriptomics?
Spatial Capture Array Architecture of Visium HD and Stereo-seq.
(a) 10x Genomics Visium HD features a 6.5 × 6.5 mm capture area composed of 2 × 2 μm spatially barcoded squares, which can be aggregated into larger bins for downstream analysis. (b) STOmics Stereo-seq employs a high-density DNA nanoball (DNB) array with coordinate identifiers (CIDs) and RNA capture oligonucleotides to enable high-resolution spatial gene expression mapping.
Why Choose MetwareBio for Spatial Transcriptomics Service?
Which Spatial Transcriptomics Platform Should You Choose?
| Feature | Stereo-seq | Visium HD |
| RNA Capture Strategy | Poly(A)-based capture for fresh-frozen tissues; random-probe capture for FFPE | Whole-transcriptome probe-based detection |
| Species Compatibility | Human, animal, and plant tissues | Human and mouse |
| Capture Area | 0.5 × 0.5 cm and 1 × 1 cm; larger custom formats available | 6.5 × 6.5 mm |
| Capture Feature Size | ~220 nm DNB spot | 2 × 2 μm barcoded square |
| Native Spatial Resolution | 500 nm array spacing | 2 μm barcoded square |
| Sample Compatibility | Fresh-frozen and FFPE tissues | Fresh-frozen and FFPE tissues |
| Sequencing Platform | DNBSEQ | Illumina |
| Sequencing Data Output | 5 × 5 mm: FF 75 Gb; 10 × 10 mm: FF/FFPE 300 Gb | 6.5 × 6.5 mm: FF 250 Gb; FFPE 100 Gb |
| Primary Processing | STOmics SAW pipeline | 10x Genomics Space Ranger |
| Recommended Applications | Cross-species studies, large tissue mapping, and exploratory spatial transcriptomics | Human and mouse studies requiring standardized, probe-based spatial gene expression profiling |
Spatial Transcriptomics Data Analysis and Deliverables
MetwareBio's spatial transcriptomics service provides a comprehensive bioinformatics workflow from sequencing data quality control to spatial gene expression analysis and biological interpretation. Sequencing data are processed to generate spatially resolved gene expression matrices, which are integrated with tissue images for spatial visualization. Standard analysis includes gene and UMI statistics, dimensionality reduction, spatial clustering, marker gene identification, differential gene expression analysis, and GO/KEGG functional enrichment. These results are consolidated into a comprehensive report to characterize regional transcriptional patterns, tissue heterogeneity, and spatial molecular organization. For studies with matched single-cell RNA-seq data, integrative analysis can further support cell-type annotation, spatial cell-type distribution mapping, and cell–cell communication inference. Additional analyses, including spatial trajectory inference, gene co-localization, pathway activity analysis, WGCNA, and CNV analysis, can be performed according to the research objectives and data suitability. Contact Us for Demo
Our Experience & Proven Track Record
| Species | Tissue | Total Genes Detected | Mean Genes per Bin8 (16 µm) |
| human | brain | 17,296 | 1,956 |
| human | lungs | 17,915 | 653 |
| mouse | brain | 17,150 | 1,828 |
| mouse | heart | 16,590 | 1,686 |
| mouse | skin | 18,297 | 1,890 |
| mouse | lung | 17,308 | 1,223 |
| mouse | Kidney | 17,089 | 948 |
| mouse | liver | 17,020 | 2,227 |
| Species | Tissue | Total Genes Detected | Mean Genes per Bin200 (100 µm) |
| human | lung | 49,582 | 7,764 |
| human | liver | 45,956 | 15,317 |
| mouse | brain | 27,182 | 8,871 |
| mouse | skin | 27,393 | 12,360 |
| mouse | colon | 28,341 | 10,184 |
| mouse | Lymph node | 26,594 | 10,246 |
| monkey | bone marrow | 20,830 | 3,538 |
| rabbit | skin | 26,556 | 3,331 |
| Sample Type | Median Genes per Bin200 (100 µm) |
| Wheat Stem | 19,182 |
| Cotton Shoot Apex | 18,486 |
| Rice Young Panicle | 9,325 |
| Rice Stem | 9,323 |
| Rice Embryo | 8,952 |
| Rice Caryopsis | 7,091 |
| Soybean Root Nodule | 5,230 |
| Tomato Fruit | 2,062 |
Key Applications of Spatial Transcriptomics
Spatial transcriptomics reveals how gene expression varies across intact tissue regions, helping define spatial domains, molecular niches, and region-specific transcriptional programs. This approach is valuable for resolving tissue heterogeneity in human samples, animal models, and plant organs where spatial organization is closely linked to biological function.
Spatial gene expression profiling helps characterize how neighboring cell populations and signaling programs are organized within local microenvironments. Applications include tumor–immune interactions, inflammatory niches, tissue-specific signaling, and spatial communication patterns in both animal and plant tissues.
By linking pathological changes with their spatial transcriptional context, spatial transcriptomics supports research on cancer, neurological disorders, inflammation, infection, fibrosis, and other complex diseases. It can also reveal region-specific molecular responses to drugs, therapeutic interventions, and experimental treatments in clinical tissues and animal models.
Spatial transcriptomics enables tissue- and region-specific analysis of molecular responses to physiological and environmental stress. It supports studies of injury, regeneration, hypoxia, and metabolic stress in biomedical research, as well as drought, salinity, temperature, nutrient limitation, pathogen challenge, and agronomic trait variation in plant research.
Spatial Transcriptomics Case Studies
Spatial Transcriptomics Reveals ZFP36-Mediated Immunometabolic Reprogramming in Bladder Cancer
In a 2026 PNAS study, researchers investigated the spatial heterogeneity and immune–metabolic regulation of bladder cancer (BLCA) using paired tumor and adjacent normal tissues. MetwareBio’s spatial transcriptomics service enabled spatially resolved gene expression profiling, cell-population annotation, and characterization of region-specific transcriptional programs across the bladder cancer microenvironment, revealing distinct distributions of tumor cells, lymphocytes, macrophages, fibroblasts, and other cell populations. Spatial transcriptomic analysis further supported the identification of ZFP36 as a key immunoregulatory factor, with spatial expression analysis linking ZFP36 to immune-related pathways and identifying C1QBP as a potential downstream target involved in T-cell regulation. Combined with MetwareBio’s spatial metabolomics service, the integrated spatial multi-omics analysis connected localized gene expression with metabolic reprogramming in lymphocyte-rich tumor regions, helping uncover the ZFP36–C1QBP regulatory axis and providing mechanistic insights into tumor immune evasion and potential immunotherapeutic targets in bladder cancer.
Sample Preparation & Submission Guidelines
- FFPE tissue blocks
- Fresh-frozen tissue blocks embedded in OCT or FSC 22
- Available capture area sizes: Stereo-seq: 0.5 × 0.5 cm and 1 × 1 cm, with custom sizes available upon request; Visium HD: 6.5 × 6.5 mm.
- Tissue length: 1.5 mm ≤ length ≤ chip length
- Tissue width: 1.5 mm ≤ width ≤ chip width
- Tissue area: ≤ 80% of the chip capture area
- Tissue thickness: 2–20 mm
- FFPE tissue blocks: Store at 4 °C and ship with ice packs
- Fresh-frozen tissue blocks: Store at −80 °C and ship on dry ice
Frequently Asked Questions (FAQ)
Bulk RNA-seq measures averaged gene expression across a tissue sample, whereas spatial transcriptomics measures gene expression while preserving its original spatial location within the tissue. Spatial transcriptomics therefore adds an important spatial dimension to transcriptome analysis, enabling researchers to identify region-specific gene expression, spatial molecular heterogeneity, tissue architecture, and localized biological programs that may be masked by bulk RNA-seq.
| Feature | Bulk RNA-seq | Spatial Transcriptomics |
| Gene Expression Profiling | Average expression across the entire sample | Spatially resolved gene expression within tissue |
| Spatial Information | Not retained | Preserved |
| Tissue Heterogeneity | Signals from different regions are averaged | Regional molecular differences can be resolved |
| Spatial Resolution | Whole-sample level | Cellular to subcellular scale, depending on platform |
| Typical Applications | Differential expression, pathway analysis, transcriptome profiling | Tissue architecture, spatial heterogeneity, microenvironments, spatial interactions |
| Best Suited For | Studies focused primarily on overall transcriptional changes | Studies where the location of gene expression is biologically important |
Yes. MetwareBio's spatial transcriptomics service supports both fresh-frozen and formalin-fixed paraffin-embedded (FFPE) tissue samples, including suitable archived FFPE specimens. However, RNA quality, fixation conditions, tissue integrity, and preservation history can affect assay performance. Archived samples should therefore undergo a pre-experimental quality assessment to determine their suitability for spatial gene expression profiling. Researchers are encouraged to provide sample preparation and storage information before submission.
Not necessarily. The spatial resolution of a capture array describes the size or spacing of its measurement features, rather than guaranteeing that each measurement represents an individual cell. Spatial transcriptomics data can be analyzed at different bin sizes, and image-based cell segmentation may support cell-level expression assignment. However, the effective cellular resolution depends on tissue morphology, RNA distribution, gene detection sensitivity, and computational processing. High-resolution spatial profiling should therefore not be considered equivalent to conventional single-cell RNA-seq.
Spatial transcriptomics data quality is influenced by tissue preservation, RNA integrity, section quality, capture efficiency, sequencing depth, and bioinformatics processing. Tissue-specific RNA abundance and the selected spatial bin size can also affect the number of genes and UMIs detected per analysis unit. For probe-based assays, probe compatibility and target coverage are additional considerations. Appropriate sample quality control, experimental design, and data processing are essential for obtaining interpretable spatial gene expression profiles.
Yes. Spatial transcriptomics can be integrated with single-cell RNA-seq and spatial metabolomics for multi-omics analysis of complex tissues. Matched single-cell RNA-seq data can strengthen cell-type annotation and support advanced analyses such as spatial cell-type mapping, cell–cell communication, spatial trajectory analysis, WGCNA, and inferred copy-number variation analysis. Integration with spatial metabolomics can further connect spatial gene expression patterns with localized metabolic phenotypes, providing complementary molecular information for investigating tissue heterogeneity, microenvironments, and spatial biological regulation.
Reference
Ye F, Han X, Li W, et al. Spatial multiomics profiling reveals ZFP36-mediated immunometabolic reprogramming in bladder cancer. Proc Natl Acad Sci U S A. 2026;123(15):e2505125123. doi:10.1073/pnas.2505125123
