Spatial Transcriptomics Fresh Frozen Tissue Service
Profile gene expression across intact fresh frozen tissue sections with spatial context preserved. Our spatial transcriptomics service combines Visium poly-A capture technology with comprehensive bioinformatics to map transcriptomic architecture at approximately 55 µm resolution — enabling spatially resolved discovery in oncology, neuroscience, immunology, and developmental biology.
- Poly-A capture-based spatial transcriptomics for fresh frozen tissue sections
- Tissue morphology preserved with H&E staining and imaging integration
- Comprehensive bioinformatics — from raw data to publication-ready figures
- Flexible analysis options including cell-type deconvolution and pathway enrichment
Spatial Transcriptomics Frozen Tissue — Technology Overview
Spatial transcriptomics maps gene expression within the native tissue architecture, capturing both transcriptomic identity and spatial coordinates for every profiled spot. For fresh frozen tissues, the poly-A capture workflow preserves full-length mRNA transcripts, delivering whole-transcriptome coverage without the target-restricted limitations of probe-based approaches.
Visium Spatial Gene Expression for Fresh Frozen Tissue
Fresh frozen tissue sections are placed onto a Visium slide containing approximately 5,000 barcoded capture spots, each 55 µm in diameter. Each capture area measures 6.5 mm × 6.5 mm. During permeabilization, poly-adenylated mRNA released from the overlying tissue binds to spatially barcoded oligonucleotides on the slide surface. Reverse transcription incorporates the spatial barcode into the cDNA, linking each transcript to its original tissue location.
How the Poly-A Capture Workflow Preserves Spatial Context
The poly-A capture approach targets the 3′ poly-A tail of mRNA transcripts, capturing full-length coding sequences. Unlike probe-based methods that rely on pre-designed gene panels, poly-A capture provides unbiased whole-transcriptome coverage — detecting both annotated genes and novel transcripts. The spatial barcode embedded in each cDNA molecule ensures that every transcript can be mapped back to its tissue coordinate, enabling true spatial gene expression analysis.
Spatial transcriptomics has matured rapidly, with databases such as STOmicsDB (Nucleic Acids Research, 2024) now curating thousands of spatially resolved transcriptomic datasets across species and tissue types, reflecting the growing ecosystem of validated spatial methodologies.
Workflow — From Frozen Tissue to Spatial Expression Maps
Our fresh frozen spatial transcriptomics workflow integrates QC checkpoints at every stage — from tissue sectioning through data delivery — ensuring transparency and reproducibility.
Tissue Sectioning & Histological Staining
Frozen tissue blocks embedded in OCT medium are cryosectioned at 10 µm thickness. Sections are placed onto Visium slides and stained with hematoxylin and eosin (H&E) for morphological assessment. Brightfield imaging captures high-resolution tissue architecture before downstream processing.
QC Checkpoint: Tissue morphology is evaluated from H&E images. Sections with visible tissue damage, folding, or inadequate coverage of the capture area are flagged.
Permeabilization & cDNA Synthesis
Tissue permeabilization conditions are optimized for each tissue type. For new or challenging sample types, a tissue optimization step is performed to determine the optimal permeabilization time using fluorescent cDNA yield as a readout. Once optimized, mRNA is released, captured on the spatially barcoded slide, and reverse-transcribed into cDNA.
QC Checkpoint: Fluorescent cDNA signal intensity is assessed to confirm successful mRNA capture and cDNA synthesis.
Library Preparation & Sequencing
cDNA is amplified and converted into sequencing-ready libraries. Library quality is assessed via Bioanalyzer trace to confirm appropriate fragment size distribution and concentration before sequencing. Libraries are sequenced on Illumina platforms, with a recommended depth of 50,000–100,000 read pairs per spot, adjusted based on tissue complexity and project goals.
QC Checkpoint: Library size distribution and concentration are validated; libraries failing QC are re-prepared.
Data Processing with Space Ranger
Sequencing data are processed through the Space Ranger pipeline, which performs read alignment, spatial barcode demultiplexing, and tissue-spot matching. The pipeline generates a gene-by-spot expression matrix, spatial alignment images, and comprehensive QC metrics.
QC Checkpoint: Key metrics — reads mapped to transcriptome, fraction of reads with valid spatial barcodes, number of spots under tissue, and median genes detected per spot — are reviewed for each sample.
Overview of the fresh frozen spatial transcriptomics workflow — from cryosectioning through H&E staining, cDNA synthesis on Visium slides, to spatial gene expression mapping.
Bioinformatics Analysis
Standard Data Processing Pipeline
All datasets are processed through a standardized bioinformatics pipeline:
- Space Ranger — Read alignment, spatial barcode demultiplexing, and generation of the gene-by-spot expression matrix
- Quality filtering — Removal of low-quality spots based on gene/transcript counts and mitochondrial read fraction
- Normalization — Library-size normalization (e.g., SCTransform or log-normalization)
- Dimensionality reduction — PCA followed by UMAP or t-SNE for visualization
- Unsupervised clustering — Graph-based clustering (e.g., Leiden or Louvain algorithm) to identify transcriptionally distinct spatial domains
- Spatially variable gene (SVG) detection — Identification of genes whose expression exhibits spatial autocorrelation using methods such as SpatialDE or SPARK-X
Core Analysis Deliverables
| Analysis Component | Description |
|---|---|
| Unsupervised clustering | Identification of transcriptionally distinct spatial domains within tissue |
| Cluster marker identification | Differential expression analysis to identify marker genes per cluster |
| Spatial feature visualization | Gene expression intensity overlaid on tissue H&E images |
| Spatially variable gene (SVG) detection | Genes whose expression patterns show significant spatial structure |
| GO/KEGG pathway enrichment | Functional annotation of cluster-specific gene signatures |
| Interactive HTML report | Comprehensive analysis report with all figures and tables |
Advanced Analysis (Optional Add-ons)
For projects requiring deeper biological insight, the following add-on analyses are available:
| Analysis | Description | Requirements |
|---|---|---|
| Cell-type deconvolution | Estimation of cell-type proportions per spot using reference scRNA-seq data (RCTD, SPOTlight, cell2location) | Reference scRNA-seq dataset from matched tissue |
| Integrated single-cell + spatial analysis | Joint analysis of scRNA-seq and spatial transcriptomics data; label transfer, spatial mapping of cell states | scRNA-seq data from same or comparable tissue |
| Spatial trajectory analysis | Inference of differentiation or activation trajectories in spatial context | Sufficient tissue coverage of the dynamic process |
| Ligand-receptor interaction analysis | Mapping of cell-cell communication networks across spatial domains (CellChat, NicheNet) | Spatial data with sufficient gene coverage |
| Multi-sample integrated analysis | Batch-corrected comparative analysis across conditions, time points, or donors | ≥2 samples with comparable tissue regions |
| Custom publication figure generation | Tailored visualization for manuscript submission | Requirements discussed during consultation |
Data Deliverables
Primary Data Outputs
| Deliverable | Description |
|---|---|
| Raw sequencing data | Demultiplexed sequencing reads |
| Gene-by-spot expression matrix | Raw and normalized transcript counts per spot |
| Spatial alignment images | H&E images with spatial spot overlay and tissue position data |
| Loupe Browser file | Interactive visualization file compatible with Loupe Browser |
Analysis Reports
| Deliverable | Description |
|---|---|
| Publication-ready figures | High-resolution figures for manuscript submission |
| Analysis parameter log | Full record of software versions and parameter settings for reproducibility |
Specific deliverables are determined based on project scope and requirements. A detailed deliverable list will be confirmed during project consultation.
Demo Results — Representative Spatial Transcriptomics Outputs
The following figure illustrates representative results from fresh frozen spatial transcriptomics analysis. All visualizations are examples of standard outputs delivered with each project.
Representative spatial transcriptomics outputs. Top left: UMAP clustering and spatial cluster map on tissue H&E. Top right: Gene expression spatial feature plot. Bottom: Analysis composite with cluster marker heatmap (left), QC violin plots (center), and pathway enrichment dot plot (right).
Note: All figures shown are representative results. Actual results vary by tissue type, sample quality, and project parameters.
Frozen Tissue Sample Requirements
Seeing the results is one thing — sending us your samples is the next step. Here is everything you need to know about sample preparation and shipping.
| Sample Type | Recommended Input | Container / Embedding | Shipping Conditions | QC Assessment | Notes |
|---|---|---|---|---|---|
| Fresh frozen tissue (OCT-embedded block) | Tissue section ≤ 6.5 mm × 6.5 mm; block submitted as-is | OCT-embedded cryomold | Dry ice (−80°C) | RIN assessment, H&E morphology check | Avoid repeated freeze-thaw cycles |
| Pre-cut tissue sections on slides | 1–2 slides per sample; 10 µm thickness | Slide mailer | Dry ice (−80°C) | H&E morphology, RNA integrity on adjacent section | Sections must remain frozen during transport |
| Brain tissue | Fit within capture area; anatomical documentation recommended | OCT block or slides | Dry ice (−80°C) | RIN assessment recommended (context-dependent) | Anatomical orientation documentation facilitates atlas registration |
| Tumor biopsy | Fit within capture area; multiple sections recommended | OCT block preferred | Dry ice (−80°C) | RIN + DV200 assessment | Necrotic regions may reduce RNA quality |
| Other tissue types | ≤ 6.5 mm × 6.5 mm within capture area | OCT block | Dry ice (−80°C) | RIN, morphology, tissue orientation | Embedding orientation critical |
Tissue Quality Recommendations
RNA integrity is a primary determinant of spatial transcriptomics data quality for fresh frozen tissues. For optimal results, we recommend:
- RIN ≥ 7 for standard poly-A capture workflow
- DV200 ≥ 50% as a complementary metric, particularly for tissues with moderate degradation
- Morphological integrity — well-preserved tissue architecture without visible ice crystal damage, necrosis, or folding
Samples with RIN between 5 and 7 or DV200 between 30% and 50% may still be processed, with modified protocols evaluated on a case-by-case basis. For challenging specimens, RNA-rescue approaches developed in the broader spatial transcriptomics community have demonstrated improved mRNA recovery from partially degraded frozen tissues (Mirzazadeh et al., Nature Communications, 2023).
Shipping & Transport Guidelines
Tissues should be fresh frozen immediately after collection, embedded in OCT medium, and stored at −80°C. For shipping, samples must be packed with sufficient dry ice to maintain −80°C throughout transit. Pre-cut sections on slides should be shipped in sealed slide mailers on dry ice. Avoid freeze-thaw cycles, which degrade RNA integrity and compromise spatial transcriptomics data quality.
Case Study: Spatial Transcriptomics of Primary and Metastatic Liver Tumors
Source: Adapa SR, Porshe S, Talada DP, Nywening TM, Anderson ML, Shaw TI, Jiang RHY. Cancers, 2025, 17(19):3210. DOI: 10.3390/cancers17193210.
Background
Primary hepatocellular carcinoma (HCC) and liver metastases differ fundamentally in cellular origin, progression trajectory, and therapeutic response. Despite their clinical importance, a direct high-resolution spatial comparison of their tumor microenvironments (TMEs) within the liver had not been performed — leaving an open question as to whether these tumor types share architecturally encoded vulnerabilities.
Methods
Adapa et al. (2025) applied high-definition spatial transcriptomics (>16,000 genes per sample, >97% mapping rates) to fresh-frozen specimens of one HCC and one liver metastasis as a proof-of-principle pilot study. Transcriptional clustering was used to define spatially distinct compartments, identify rare cell states, and characterize pathway alterations. Findings were cross-validated against independent human proteomics datasets and patient survival data.
Results
HCC displayed an ordered lineage architecture, with transformed hepatocyte-like tumor cells broadly dispersed across the tissue and more differentiated hepatocyte-derived cells restricted to localized zones. In contrast, liver metastases showed two sharply compartmentalized domains: an invasion zone, where proliferative stem-like tumor cells occupied tumor-associated macrophage (TAM)-rich boundaries adjacent to hypoxia-adapted tumor-core cells, and a plasticity zone, which formed a heterogeneous niche of cancer-testis antigen-positive germline-like cells. Despite these fundamentally different spatial architectures, both tumor types converged on a conserved program of metabolic rewiring termed “porphyrin overdrive.”
Conclusion
This pilot study demonstrates how fresh frozen spatial transcriptomics can resolve fundamentally different spatial architectures between tumor types while simultaneously identifying shared metabolic vulnerabilities that transcend tissue organization. The finding that HCC and liver metastases converge on a common “porphyrin overdrive” program illustrates the power of spatial transcriptomics to reveal therapeutic targets that link local tumor niches to systemic host-tumor interactions.
Adapted from Adapa et al. (2025), Cancers 17(19):3210.
Spatial Transcriptomics Applications
The liver cancer case study above illustrates one domain where spatial transcriptomics excels. Fresh frozen spatial transcriptomics is equally powerful across a broad range of research areas where spatial gene expression context is essential for biological interpretation.
Oncology & Tumor Microenvironment
Map gene expression across tumor regions — including the tumor core, invasive margin, and surrounding stroma — to identify spatially restricted therapeutic targets, characterize immune infiltration patterns, and define transcriptional programs associated with tumor progression. Fresh frozen tissue preserves full-length transcriptomes, enabling detection of splice variants and fusion transcripts relevant to cancer biology. See also: Spatial Omics Solutions for Tumor Microenvironment.
Neuroscience & Brain Architecture
Resolve gene expression patterns across anatomically defined brain regions in frozen tissue sections. The 55 µm spot resolution captures laminar and regional transcriptomic differences in cortical structures, enabling spatial mapping of neuronal subtypes, glial populations, and region-specific disease signatures. See also: Spatial Omics Solutions for Neuroscience.
Immunology & Tissue Organization
Characterize the spatial organization of immune cells within lymphoid and non-lymphoid tissues. Identify tertiary lymphoid structures, map chemokine and cytokine gradients, and profile immune cell activation states in their native tissue context. Fresh frozen tissue preserves immune-relevant transcripts, including low-abundance cytokine mRNAs.
Developmental Biology
Profile spatially coordinated gene expression during organogenesis and tissue patterning. Fresh frozen embryonic or neonatal tissue sections capture dynamic transcriptional programs that define developing anatomical structures.
Method Comparison — Fresh Frozen vs FFPE Spatial Transcriptomics
Choosing between fresh frozen and FFPE spatial transcriptomics depends on sample availability, research objectives, and required transcriptome coverage. The following comparison highlights key differences to guide method selection.
| Dimension | Fresh Frozen | FFPE |
|---|---|---|
| RNA Quality | Higher mRNA integrity; full-length transcripts preserved | Partially degraded RNA; fragments dominate |
| Transcriptome Coverage | Whole-transcriptome (poly-A capture); unbiased gene detection | Probe-targeted; limited to pre-designed gene panel |
| Sample Availability | Requires prospective collection and −80°C storage | Compatible with archival clinical specimens |
| Tissue Morphology | Good (H&E staining) | Excellent (standard histology protocols) |
| Handling & Storage | Requires −80°C storage and dry ice shipping | Room temperature stable; routine pathology workflow |
| Recommended Platform | Visium FF (poly-A capture), Visium CytAssist | Visium CytAssist (probe-based), Visium HD |
| Sensitivity | Higher for poly-A transcripts; depends on RNA integrity | Consistent sensitivity across targets (probe-defined) |
| Clinical Sample Compatibility | Limited to prospectively collected fresh tissue | Broad compatibility with archived clinical samples |
| Bioinformatics Scope | Full transcriptome analysis; splice variant and fusion detection | Gene panel analysis; targeted pathway interrogation |
When to Choose Fresh Frozen Spatial Transcriptomics
- Whole-transcriptome coverage is required (not limited to a pre-defined gene panel)
- Detection of splice variants, fusion transcripts, or novel RNA species is important
- Tissue can be prospectively collected and immediately frozen
- Maximum transcript detection sensitivity is desired
- The research question requires unbiased, discovery-driven spatial profiling
When to Consider FFPE Alternatives
- Only archival FFPE tissue is available
- A focused gene panel is sufficient for the research question
- Tissue morphology preservation is the primary concern
- Room temperature sample handling is logistically necessary
- The study requires large clinical cohorts with historical samples
For projects with both fresh frozen and FFPE samples from the same tissue, combined analysis can provide complementary insights — fresh frozen for discovery and FFPE for validation across larger sample sets.
Frequently Asked Questions (FAQ)
References
- Adapa SR, Porshe S, Talada DP, Nywening TM, Anderson ML, Shaw TI, Jiang RHY. "Spatial Transcriptomics Reveals Distinct Architectures but Shared Vulnerabilities in Primary and Metastatic Liver Tumors." Cancers, vol. 17, no. 19, 2025, 3210.
- Mirzazadeh R, Andrusivova Z, Larsson L, et al. "Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples." Nature Communications, vol. 14, 2023, 509.
- Grases D, Porta-Pardo E. "A practical guide to spatial transcriptomics: lessons from over 1000 samples." Trends in Biotechnology, vol. 44, no. 5, 2026, pp. 1230–1242.
- Schott M, Leon-Periñán D, Splendiani A, et al. "Open-ST: High-resolution spatial transcriptomics in 3D." Cell, vol. 187, no. 15, 2024, pp. 3953–3972.e26.
- Wang Y, Liu B, Zhao G, et al. "Spatial transcriptomics: Technologies, applications and experimental considerations." Genomics, vol. 115, no. 5, 2023, 110671.
- Antico F, Gai M, Arigoni M. "Tissue RNA Integrity in Visium Spatial Protocol (Fresh Frozen Samples)." Methods in Molecular Biology, vol. 2584, 2023, pp. 191–203.
- Xu Z, Wang W, Yang T, et al. "STOmicsDB: a comprehensive database for spatial transcriptomics data sharing, analysis and visualization." Nucleic Acids Research, vol. 52, no. D1, 2024, pp. D1055–D1061.
- Fan Z, Chen R, Chen X. "SpatialDB: a database for spatially resolved transcriptomes." Nucleic Acids Research, vol. 48, no. D1, 2020, pp. D233–D237.
