Spatial transcriptomics is a technique that provides information about gene expression patterns within intact tissues. This technology employs various methodologies, including in situ sequencing (ISS), in situ hybridization (ISH), and in situ capturing (ISC), which offer high-throughput analysis to localize specific gene expression in tissues.1 This article focuses on spatial transcriptomics' inception, methods, and research applications.
Image credit: Jose Luis Calvo/Shutterstock.com
Importance of Spatial Information in Genetic Research
High-throughput next-generation sequencing (NGS) technologies have enabled scientists to study tissue cellular heterogeneity. For instance, the single-cell RNA sequencing (scRNA-seq) technique facilitates the identification of rare cell types often masked in bulk profiling.
Despite the advantages of the scRNA-seq approach, it fails to provide spatial information, which is crucial to explain cell signaling patterns.2 Normal cell behavior and functions are influenced by their interaction with diverse neighboring cell types, and a lack of spatial information could limit the precise biological interpretation of the cell.
Spatial transcriptomics overcomes the shortcomings of traditional single-cell analyses by interpreting how changes in gene expression occur across different cell types in a coordinated manner, influenced by their spatial proximity to one another.3 This approach also sheds light on cellular organization and interaction within tissues.
Spatial transcriptomics has significantly contributed to uncovering biological insights related to neuroscience and developmental biology and understanding the pathogenesis of various diseases, including cancer. Additionally, it enhances traditional histopathological analysis by enabling molecular readouts while preserving tissue morphology.
Spatial Transcriptomic Methodologies and Commercial Platforms
Spatial transcriptomic methods are classified into three main categories: probe-based, sequencing-based, and imaging-based techniques. These also include hybrid approaches such as image-guided spatially resolved scRNA-seq methods.4
Each method differs in terms of resolution and the number of detected genes. The resolution of various spatial-based transcriptomic platforms is associated with how exactly a location is retained for any particular RNA transcript, ranging from subcellular localization to a 55 μm diameter capture spot with distinct x-y spatial coordinates.
In probe-based methods, scientists select a specific probe based on the final readout of targets. In some studies, whole-transcriptome analysis is performed, which provides comprehensive information on the complete set of RNA transcripts in a cell or organism. Typically, sequencing techniques rely on the whole transcriptome based on spatially barcoded DNA to uncover specific gene expression. GeoMx Digital Spatial Profiler (DSP), 10× Visium, and BMKMANU S1000, a China-based commercial platform, are popular sequencing-based platforms.5
Most sequencing-based technologies combine current next-generation sequencing approaches with classic microarray technologies to provide spatial information on gene expression. For instance, Visium, developed by 10× Genomics, is based on a combination of microarrays coated with spatially barcoded RNA-binding oligonucleotides. Unlike many sequencing platforms, the Visium technology can analyze fresh tissues and Formalin-Fixed Paraffin-Embedded (FFPE) tissues. Stereo-seq from MGI is based on DNA nanoball (DNB) technology, offering whole transcriptome information without requiring pre-designed probes at single-cell resolution.5
Spatial barcoding in these systems enables capturing and mapping transcripts to precise coordinates, a key innovation that bridges molecular analysis with tissue morphology.
While not always classified as spatial transcriptomics, image-guided spatially annotated single-cell sequencing methods identify and isolate single cells in regions of interest (ROIs) using microscopic images, followed by single-cell sorting and state-of-the-art single-cell omics sequencing. Contemporary imaging-based spatial transcriptomics integrates highly multiplexed single-molecule fluorescence in situ hybridization (smFISH) with histology, molecular, and imaging tools to analyze gene expression within tissue.
Different companies have developed imaging-based platforms for spatial transcriptomics, including MERSCOPE, Molecular Cartography (MC), and 10× Xenium. For instance, Resolve Biosciences launched MC in 2021, which is based on the sequential FISH (seqFISH) approach using a three-probe hybridization strategy. Vizgen’s MERSCOPE offers a tissue-wide view of up to one thousand custom genes at single-cell resolution.
Spatial Transcriptomics
Video credit: Journal of Investigative Dermatology (JID)/youtube.com
Applications of Spatial Transcriptomics
Spatial transcriptomics is widely applied in immunology, oncology, developmental biology, and neuroscience research. It has played a crucial role in the study of embryogenesis. Spatiotemporal analysis unravels how zygotes develop into highly complex organisms in a regulated manner.6 This strategy enabled scientists to visualize the entire process of embryonic development from different dimensions in situ.
Stereo-seq technology was used to visualize spatiotemporal transcriptomics of mouse organogenesis.7 It enabled scientists to image the dynamic tissue- or site-specific gene expression changes over different developmental phases. Spatiotemporal transcriptomic technology sheds light on the natural cell ecosystem of cell fate specification, organ formation, and cell-cell interaction.
A combination of unsupervised clustering of the gene-by-cell matrix, uniform manifold approximation and projection (UMAP) visualization, and spatial consistency-based clustering (SCC) methods enabled researchers to establish spatial relationships between distinct gene signatures and heterogeneous cell types, specific anatomic regions, and cell type locations. Using this dataset, a mouse organogenesis spatiotemporal transcriptomic atlas (MOSTA) was developed to study mouse organogenesis and other species in the future.
Spatial transcriptomic technologies were used to develop spatial maps of human kidney, lungs, heart, and brain tissue atlases in health and disease. Spatial maps helped researchers identify ZBTB11, a regulator of cardiomyocyte degeneration in arrhythmogenic cardiomyopathy. Gene expression mapping within tissue sections, while preserving spatial context, enabled the determination of the unknown neuron population that regulates the fever response in humans and other species.8
Spatial transcriptomic techniques offer molecular insights into the processes occurring in the dorsolateral prefrontal cortex of individuals with schizophrenia and Alzheimer's disease. Platforms, such as 10× Visium technology, enable the spatially resolved analysis of cellular and molecular host-microbe interactions in colorectal cancer and oral squamous cell carcinoma. This strategy introduced bacteria to the cancer map. Spatial transcriptomics has also highlighted the significance of spatial tumor heterogeneity and its entire microenvironment.
Analytical and Computational Challenges
Despite its promise, spatial transcriptomics presents several computational challenges. High-dimensional datasets require extensive preprocessing and normalization to account for technical noise, batch effects, and spatial variability. Integrating spatial data with other omics layers, such as proteomics or epigenomics, also demands sophisticated multimodal analysis frameworks. Additionally, no unified standard for resolution, sensitivity, or data formats across platforms complicates cross-study comparisons. As spatial technologies evolve, scalable and interpretable analytical tools will be critical to unlocking their full potential.
References and Future Reading
- Jain S, Eadon MT. Spatial transcriptomics in health and disease. Nat Rev Nephrol. 2024;20(10):659-671. doi: 10.1038/s41581-024-00841-1.
- Molla Desta G, Birhanu AG. Advancements in single-cell RNA sequencing and spatial transcriptomics: transforming biomedical research. Acta Biochim Pol. 2025;72:13922. doi: 10.3389/abp.2025.13922.
- Piñeiro AJ, et al. Research Techniques Made Simple: Spatial Transcriptomics. J Invest Dermatol. 2022 142(4):993-1001.e1. doi: 10.1016/j.jid.2021.12.014.
- Chen TY, et al. Spatial Transcriptomic Technologies. Cells. 2023 12(16):2042. doi: 10.3390/cells12162042.
- Wang Y, et al. Spatial transcriptomics: Technologies, applications, and experimental considerations. Genomics. 2023;115(5):110671. doi: 10.1016/j.ygeno.2023.110671.
- Ávila-González D, et al. Unraveling the Spatiotemporal Human Pluripotency in Embryonic Development. Front Cell Dev Biol. 2021;9:676998. doi: 10.3389/fcell.2021.676998.
- Chen A, et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell. 2022;185(10):1777-1792.e21. doi: 10.1016/j.cell.2022.04.003..
- Jung N, Kim TK. Spatial transcriptomics in neuroscience. Exp Mol Med. 2023;55(10):2105-2115. doi: 10.1038/s12276-023-01093-y.