Spatial Whole-Transcriptome Analysis Service

Why Spatial Whole-Transcriptome Analysis?

Spatial Whole-Transcriptome Analysis enables a deeper understanding of gene expression by preserving the tissue's native architecture. Unlike traditional transcriptomic methods that average data across heterogeneous samples or disrupt cellular organization, this approach maps gene expression directly within the tissue, offering a more accurate representation of biological processes. By capturing spatially resolved transcriptomes from thousands of genes, it reveals how gene expression varies across different tissue regions, providing insights into tissue-specific functions, disease mechanisms, and cellular interactions. This makes it an invaluable tool for studying complex tissues, identifying disease heterogeneity, and uncovering spatial patterns that drive critical biological processes.

Platforms

Our Spatial Whole-Transcriptome Analysis Service provides high-resolution mapping of gene expression within tissue architecture. Using state-of-the-art platforms, we achieve a spatial resolution of approximately 50–100 μm per capture spot, allowing robust profiling of tissue regions while maintaining spatial context. For studies requiring finer detail, our technology can resolve structures close to single-cell level, enabling exploration of cellular heterogeneity and microenvironment interactions. Key platform features include:

• Spatial Transcriptomics Technology: We use advanced high-throughput platforms to capture the complete transcriptome while preserving spatial context within tissue sections.
• Tissue Compatibility: Supports a wide range of sample types, including FFPE and fresh-frozen tissues, ensuring flexibility for various research applications.
• High-Resolution Imaging: Integrates high-resolution imaging to map gene expression patterns with tissue morphology.
• Data Processing & Analysis: Employs robust pipelines for quality control, normalization, and visualization, enabling comprehensive exploration of spatial gene expression.

Workflow

Our Spatial Whole-Transcriptome Analysis service combines advanced experimental and computational techniques to map gene expression across tissue sections while preserving spatial context. Tissue samples, either fresh-frozen or FFPE, are thinly sectioned and placed on spatially barcoded slides. Optional staining captures tissue morphology, while mRNA is released and hybridized to barcoded probes to retain positional information. Libraries are then prepared on-slide and subjected to high-throughput sequencing.

Following sequencing, reads are mapped to the genome and aligned with the tissue image to generate a spatial gene expression matrix. Comprehensive bioinformatics pipelines perform quality control, normalization, dimensionality reduction, clustering, spatially variable gene detection, and, if applicable, cell type deconvolution using scRNA-seq references. The resulting data are visualized interactively, allowing identification of spatial niches, cell–cell interactions, and novel biomarkers, ultimately providing new biological insights into tissue organization and function.

Deliverables

Depending on your project scope, deliverables may include:

• Spatial gene expression maps
• Cluster and region-specific marker genes
• Spatially variable gene lists
• Pathway and functional enrichment reports
• High-quality figures and tables
• Comprehensive analysis report with biological interpretation

Raw and processed data formats are provided to support downstream analysis and data sharing.

Applications

Our clients commonly use Whole-Transcriptome Spatial Analysis for:

• Tumor heterogeneity and microenvironment profiling
  Identify spatially distinct tumor regions, immune infiltration patterns, and stromal interactions.
• Disease vs. normal tissue comparison
  Reveal spatially regulated genes and pathways driving pathological changes.
• Developmental and tissue biology studies
  Track spatial gene expression dynamics during tissue organization and differentiation.
• Biomarker and target discovery
  Discover spatially restricted gene signatures associated with prognosis or treatment response.
• Validation of single-cell or bulk transcriptomics
  Map cell populations and expression programs back to their anatomical locations.

Why Choose Us?

• Client-Driven Experimental Design

We work closely with you to define study objectives, sample strategy, and analytical depth, ensuring the data generated directly supports your biological questions.

• Unbiased, Whole-Transcriptome Coverage

Unlike targeted approaches, our service captures global gene expression, enabling hypothesis-free discovery and downstream re-analysis.

• Advanced Spatial Bioinformatics

Our analysis emphasizes biological interpretability, not just data generation—connecting spatial patterns to disease mechanisms and phenotypes.

• Publication-Ready Deliverables

All outputs are formatted for direct use in publications, grant applications, and presentations.

FAQs

Learn about other Q&A.

Sample preparation guidelines

Please consult our technical team before shipping your samples to confirm they meet all specifications.

Whole-Transcriptome Spatial Analysis Case Study

Title: Spatial Transcriptome‐Wide Profiling of Small Cell Lung Cancer Reveals Intra‐Tumoral Molecular and Subtype Heterogeneity

Journal: Advanced Science

Published: 2024

• Background
• Meterials & Methods
• Results
• Conclusion
• Reference

SCLC is a highly aggressive form of lung cancer that exhibits rapid growth, early metastasis, and resistance to treatment. Despite advancements in immunotherapy and chemotherapy, the survival rate remains low. A critical challenge in SCLC treatment is the tumor's heterogeneity, both between different tumors (inter-tumoral) and within a single tumor (intra-tumoral). The study emphasizes the importance of understanding ITH for improving patient outcomes and therapeutic strategies. While bulk RNA sequencing and single-cell RNA sequencing have provided insights into tumor heterogeneity, this study aims to explore ITH at a spatially resolved level within tumor regions, using a novel method—Digital Spatial Profiling (DSP).

This study performed spatial transcriptomic profiling of tumor samples from treatment-naive small cell lung cancer (SCLC) patients. High-purity tumor regions were selected using Tissue Microarray (TMA) and analyzed with the GeoMx Digital Spatial Profiler. After data normalization and clustering, regions were categorized into three groups (high, medium, low) based on intratumoral heterogeneity (ITH) levels, quantified using the DEPTH algorithm.

To investigate spatial intratumoral transcriptional diversity, the reseachers applied t-SNE algorithm to visualize gene expression profiles from 79 regions of interest (ROIs) in two-dimensional space. Using the top 200 variable genes identified through dimensionality reduction, unsupervised hierarchical clustering revealed three distinct transcriptomic phenotypes (C1-C3). Quantification of intratumoral heterogeneity (ITH) using the DEPTH method demonstrated that C1 exhibited significantly higher ITH (designated high-ITH or h-ITH), while C2 and C3 showed medium-ITH (m-ITH) and low-ITH (l-ITH) levels, respectively.

Analysis showed no significant differences in spatial physical distance (SPD) among the three ITH groups (Kruskal-Wallis test, p=0.83), nor correlation between SPD and heterogeneity scores (Spearman R=-0.041, p=0.65). Pathological examination revealed distinct tissue characteristics across ITH phenotypes: h-ITH tumors displayed high cellular purity with sparse stromal components and lymphocyte infiltration, featuring cancer cells with typical bare nuclei morphology and minimal cytoplasm. Conversely, m-ITH and l-ITH regions exhibited increased fibrotic stroma, prominent lymphocytic infiltration, and cancer cells with enlarged nuclei and abundant cytoplasm.

These findings demonstrate spatially defined molecular heterogeneity within small cell lung cancer tumors that correlates with distinct pathological features, providing new insights into SCLC's biological complexity.

Figure 2. Distinct expression patterns associated with intra‐tumoral ROIs identified by DSP.

The study found distinct spatial patterns of ITH in SCLC, including significant differences in gene expression, immune cell infiltration, and molecular subtypes across tumor regions.

The ITHtyper, based on ITH levels, was able to predict patient outcomes, with low-ITH tumors showing better prognosis. These findings suggest that ITH contributes to tumor aggressiveness, immune evasion, and resistance to treatment, making it an essential factor in understanding SCLC biology. The spatial analysis approach provides a more detailed and clinically relevant understanding of the tumor microenvironment and could help personalize treatment strategies for SCLC patients.

Reference

1. Zhang, Zicheng et al. "Spatial Transcriptome-Wide Profiling of Small Cell Lung Cancer Reveals Intra-Tumoral Molecular and Subtype Heterogeneity." Advanced science (Weinheim, Baden-Wurttemberg, Germany) vol. 11,31 (2024): e2402716. doi:10.1002/advs.202402716