Spatial transcriptomics is a molecular profiling methodology that quantifies the entire transcriptome directly within a preserved tissue section, retaining the two-dimensional positional coordinates of each mRNA molecule. Unlike single-cell RNA sequencing (scRNA-seq), which requires tissue dissociation and loses spatial context, this approach maps gene expression onto the tissue's histological architecture, revealing how cellular neighborhoods, tissue domains, and anatomical regions coordinate molecular activity.
Glossary
Spatial Transcriptomics
Spatial transcriptomics encompasses molecular profiling technologies that measure gene expression within intact tissue sections, preserving the spatial context of each transcript to map where specific cell types and molecular activities occur within a tissue's architecture.
MOLECULAR PROFILING TECHNOLOGY
What is Spatial Transcriptomics?
Spatial transcriptomics is a family of molecular profiling technologies that measure gene expression within intact tissue sections, preserving the spatial context of each transcript to map where specific cell types and molecular activities occur within a tissue's architecture.
The technology encompasses multiple implementations, including in situ hybridization-based methods like MERFISH and seqFISH that image transcripts directly, in situ sequencing approaches that read out barcodes within cells, and spatially barcoded capture arrays such as 10x Visium that bind released mRNA to positionally indexed oligonucleotides. Downstream analysis relies on spatial deconvolution to estimate cell-type proportions per capture spot and cell-cell communication inference to map ligand-receptor interactions across tissue coordinates.
Spatial Biology
Key Features of Spatial Transcriptomics
Spatial transcriptomics bridges the gap between high-throughput sequencing and tissue histology, enabling the mapping of gene expression within the native architectural context of a tissue.
01
Spatial Barcoding and Capture
The foundational technology where mRNA from a tissue section is captured onto a surface arrayed with spatially indexed oligonucleotides. Each spot on the array contains millions of capture probes with a unique spatial barcode, ensuring that the transcript's location is recorded before sequencing. This converts a two-dimensional tissue map into a spatially resolved gene expression matrix.
~55 µm
Spot Diameter (Visium)
~5,000
Spots per Capture Area
02
Unsupervised Clustering in Space
Computational methods like Bayesian spatial factor analysis or SpatialPCA identify molecularly distinct tissue regions without prior knowledge. Unlike standard single-cell clustering, these algorithms incorporate spatial covariance, grouping spots that are both transcriptionally similar and physically adjacent. This reveals functional tissue domains, such as tumor cores versus invasive margins.
03
Ligand-Receptor Interaction Mapping
Tools like CellChat and NicheNet infer cell-cell communication by analyzing the co-expression of ligands and their cognate receptors across neighboring spatial spots. This moves beyond simple co-localization to predict the directional flow of signaling pathways, such as immune checkpoint interactions between a PD-L1+ tumor cell and a PD-1+ T-cell in the microenvironment.
04
Spatial Deconvolution
Since each capture spot may contain multiple cells, deconvolution algorithms (e.g., RCTD, SPOTlight) estimate the proportion of distinct cell types within each spot. By integrating a reference single-cell RNA-seq signature matrix, these methods transform a mixed spatial voxel into a probabilistic map of cell-type composition, resolving fine-grained tissue architecture.
05
In Situ Sequencing and Imaging
High-resolution alternatives like MERFISH (Multiplexed Error-Robust Fluorescence In Situ Hybridization) and Xenium directly image individual RNA molecules within cells. These methods bypass array-based capture, achieving subcellular resolution by assigning binary barcodes to transcripts and reading them out over multiple rounds of fluorescent hybridization and imaging.
< 100 nm
Resolution (MERFISH)
10,000+
Genes Profiled
06
3D Tissue Reconstruction
By aligning and integrating spatial transcriptomic data from serial tissue sections, algorithms like PASTE (Probabilistic Alignment of Spatial Transcriptomics Experiments) reconstruct a three-dimensional molecular atlas. This volumetric view is critical for understanding complex anatomical structures, such as neuronal projections in the brain or ductal networks in tumors.
SPATIAL TRANSCRIPTOMICS
Frequently Asked Questions
Clear, technically precise answers to the most common questions about spatial transcriptomics technologies, data analysis, and biological interpretation.
Spatial transcriptomics is a collection of molecular profiling technologies that measure gene expression within intact tissue sections, preserving the spatial context of each transcript. Unlike single-cell RNA sequencing (scRNA-seq), which dissociates tissue and loses positional information, spatial transcriptomics maps where specific RNA molecules are located within a tissue's native architecture.
Core methodologies include:
- In situ hybridization-based methods (e.g., MERFISH, seqFISH): Fluorescent probes bind directly to target transcripts, and sequential rounds of imaging resolve thousands of genes at subcellular resolution.
- In situ sequencing-based methods (e.g., FISSEQ, STARmap): Transcripts are amplified and sequenced directly within the tissue, reading out the nucleotide sequence in place.
- Spatial barcoding-based methods (e.g., 10x Visium, Slide-seq, HDST): Tissue sections are placed on arrays of spatially indexed capture probes. mRNA diffuses and binds to probes, which are then sequenced to assign transcripts back to their original grid coordinates.
The output is a gene expression matrix where each row is a gene, each column is a spatial coordinate, and each value represents transcript abundance at that location, enabling downstream analysis of tissue organization, cell-cell communication, and disease microenvironments.
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About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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