Spatial Transcriptomics: Mapping Gene Expression in Tissue Context
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Highlights
Spatial transcriptomics is an innovative method that maps gene expression directly within tissue. By combining sequencing, spatial barcoding, and imaging, it provides high-resolution insights for cancer research, neuroscience, immunology, and clinical biomarker discovery.
What Is Spatial Transcriptomics?
Spatial transcriptomics is a set of molecular profiling methods that measure gene expression directly within intact tissue, preserving the spatial organization of cells and structures. It reveals which genes are active, how much they are expressed, and exactly where they are located in the tissue.
Why It Matters:
Spatial transcriptomics bridges sequencing data with histological context, enabling discoveries in a lot of biological fields like oncology, neuroscience, immunology, developmental biology, or clinical pathology.
How Spatial Transcriptomics Works
Tissue sectioning – A thin tissue slice is placed on a specialized slide or capture surface.
Molecule capture – mRNA molecules are captured using barcodes or probes.
Labeling – Each captured transcript receives a spatial barcode representing its original position.
Imaging or sequencing – Data are collected either via fluorescence microscopy or next-generation sequencing (NGS).
Spatial mapping – Gene expression is mapped back to tissue coordinates, often integrated with H&E or immunofluorescence images.
Two Main Approaches
Spatial transcriptomics technologies fall into two broad categories:
Targeted / Probe-Based Spatial Transcriptomics
Concept:
Uses designed probe panels to detect a predefined set of genes with high sensitivity.
Typical Workflow (Probe-Based):
Tissue preparation and placement on a slide.
Probe hybridization to selected target transcripts.
Signal detection via imaging or sequencing.
Mapping detected transcripts to spatial coordinates.
Strengths:
High sensitivity
Lower cost
Ideal for biomarker validation and clinical pathology
Unbiased / Whole-Transcriptome (NGS-Based) Spatial Transcriptomics
Concept:
Captures thousands of transcripts without prior gene selection.
Typical Workflow (NGS-Based):
Tissue section deposited on a slide containing spatially barcoded features.
Permeabilization and mRNA release.
Reverse transcription with barcoded oligos.
Library preparation and NGS sequencing.
Alignment and spatial reconstruction.
Strengths:
Discovery of new biomarkers
Comprehensive tissue profiling
Best for research and exploratory science
Comparison Table: NGS-Based vs Probe-Based
Feature | NGS-Based Spatial Transcriptomics | Probe-Based Spatial Transcriptomics |
|---|---|---|
Scope | All detectable transcripts | Predefined gene panel |
Use Case | Discovery, exploratory biology | Validation, clinical and targeted studies |
Sensitivity | Broad but lower for rare transcripts | High for selected targets |
Cost | Higher | Lower |
Data Complexity | High (large datasets) | Lower (simpler analysis) |
Flexibility | Open-ended, unbiased | Limited to designed probes |
Typical Applications | Tumor heterogeneity, cell atlases, developmental biology | Biomarker validation, drug target studies |
Example of Platforms | 10X Visium, STOmics Stereo-seq | NanoString GeoMx, MERFISH |
Spotlight: Stereo-seq (STOmics)
Technology Type:
Whole-transcriptome, NGS-based spatial transcriptomics.
Key Feature:
Powered by DNBSEQ™ technology, a high-accuracy sequencing method developed by MGI.
DNBSEQ™ Technology Explained
What Is DNBSEQ?
A sequencing approach that uses DNA Nanoballs (DNBs) and Rolling Circle Replication (RCR) to reduce errors and improve sequencing performance.
RCR vs PCR: Why It Matters
Rolling Circle Replication (RCR) – Used by DNBSEQ
Low amplification bias
Uses Phi29 DNA polymerase (error rate: ~1 in 10⁶–10⁷ bases)
Minimizes accumulated errors during amplification
PCR (Used in Traditional NGS) – Known Issues
False SNPs due to amplification errors
False insertions/deletions (InDels)
GC bias affecting coverage
Index hopping, causing sample cross-talk
Why DNBSEQ™ Enhances Spatial Transcriptomics (Stereo-seq)
High fidelity sequences improve transcript detection
Lower background noise, fewer false variants
Better representation of low-abundance transcripts
Enhanced data quality for spatial mapping and clustering
Applications of Spatial Transcriptomics
Cancer research: tumor heterogeneity, microenvironment mapping
Neuroscience: brain atlas generation
Immunology: immune landscape profiling
Drug discovery: pathway analysis, mechanism-of-action studies
Clinical research: biomarker validation, diagnostics development
Summary
Spatial transcriptomics combines gene expression measurement with spatial localization.
Two approaches exist:
NGS-based: unbiased, discovery-driven
Probe-based: targeted, sensitive, and cost-efficient
Stereo-seq leverages DNBSEQ™, which uses RCR instead of PCR to significantly reduce sequencing errors and improve data accuracy.
