Spatial transcriptomics
Spatial transcriptomics refers to a collection of technologies that enable the measurement of gene expression profiles while preserving the spatial context within intact tissues, allowing for the mapping of RNA transcripts at cellular or subcellular resolution.[1] These methods bridge the limitations of traditional bulk transcriptomics, which averages signals across cell populations, and single-cell RNA sequencing, which disrupts spatial information, by integrating high-throughput molecular profiling with histological architecture.[2] By capturing the location-specific dynamics of thousands of genes, spatial transcriptomics reveals how cellular identity, interactions, and functions are orchestrated in complex biological environments.[3] The foundations of spatial transcriptomics lie in early *in situ* hybridization techniques, first demonstrated in 1969 with RNA-DNA hybrid detection for cytological analysis and advanced in 1987 for localizing genomic regulatory elements in model organisms like Drosophila.[4] The modern field coalesced in the 2010s, propelled by advances in next-generation sequencing, oligonucleotide synthesis, and fluorescence microscopy; key milestones include the introduction of seqFISH in 2014 for multiplexed single-cell RNA profiling and the 2016 development of array-based spatial transcriptomics, which laid the groundwork for commercial platforms like Visium.[4] Since then, the technology has evolved rapidly, achieving genome-wide coverage and near-single-cell resolution, with over 100 methods published by 2022.[1] Spatial transcriptomics technologies are broadly divided into imaging-based and sequencing-based categories. Imaging-based approaches, such as MERFISH and seqFISH+, employ multiplexed fluorescence in situ hybridization to detect hundreds to thousands of targeted transcripts with single-molecule precision, often enhanced by expansion microscopy for subcellular detail.[5] Sequencing-based methods, including Slide-seq, Stereo-seq, and Visium, use barcoded arrays or beads to capture mRNA from tissue sections, followed by high-throughput sequencing to reconstruct spatial gene expression maps at resolutions ranging from 0.5 μm to 100 μm.[5] These complementary strategies have enabled scalable profiling of entire organs, such as the human brain or developing heart, integrating with multi-omics data for deeper insights.[3] In biology and medicine, spatial transcriptomics has transformed the study of tissue organization, cell-cell interactions, and developmental processes, such as mapping 23 cell types and 264 subtypes in the macaque brain or tracing clonal evolution in tumors.[5] Applications extend to disease research, elucidating mechanisms in cancer microenvironments, immune responses, and organ injury— for instance, identifying novel cell states in kidney disease models—while supporting precision medicine through spatial atlases like the Human Tumor Atlas Network.[3] Despite challenges in resolution, throughput, and data integration, ongoing innovations continue to expand its utility across neuroscience, oncology, and regenerative biology.[1]Fundamentals
Definition and scope
Spatial transcriptomics refers to a suite of technologies that profile the transcriptome—the complete set of RNA transcripts in a cell or tissue—while preserving the spatial organization of molecules within intact biological samples.[6] This approach enables the mapping of gene expression patterns at defined locations, revealing how transcriptional activity correlates with tissue architecture and cellular positioning.[2] Pioneered in seminal work demonstrating spatial barcoding on tissue sections, it allows quantitative analysis of thousands of genes across histological contexts.[7] The scope of spatial transcriptomics spans a spectrum of spatial resolutions, from low-resolution methods that aggregate transcripts in tissue spots (typically 50–100 μm in diameter) to high-resolution techniques achieving single-cell or subcellular precision (down to <1 μm).[8] These methods maintain tissue integrity during profiling, facilitating studies in two-dimensional sections or three-dimensional structures, and encompass applications from basic cellular interactions to complex multicellular dynamics.[6] Key concepts include spatial context, which encompasses cellular neighborhoods, expression gradients, and microenvironments that drive phenotypic diversity and functional regulation.[2] Emerging in the 2010s, spatial transcriptomics addresses gaps in prior transcriptomic paradigms: bulk RNA sequencing yields averaged expression without positional detail, while single-cell RNA sequencing captures individual cell identities but dissociates them from their native tissue surroundings.[8][7]Underlying principles
Spatial transcriptomics relies on the principle of spatial preservation, which begins with the preparation of tissue sections to maintain the integrity of molecular content and anatomical structure. Tissues are typically sectioned using cryosectioning for fresh frozen samples or formalin-fixed paraffin-embedded (FFPE) processing for archived specimens, producing thin slices (e.g., 10 μm thick) that are mounted onto specialized arrays or slides.[9] This is followed by in situ capture, labeling, or sequencing techniques that bind and tag RNA molecules directly within the tissue context, ensuring that positional information is retained throughout the workflow. For instance, polyadenylated mRNA is captured via oligo(dT) primers attached to spatially barcoded substrates, preventing dissociation and preserving the original coordinates of transcripts relative to the tissue architecture. Cryopreservation is preferred for optimal RNA integrity in methods like Visium and Slide-seq, while FFPE compatibility allows analysis of clinically derived samples but requires additional deparaffinization and antigen retrieval steps to mitigate RNA degradation.[9] Readout strategies in spatial transcriptomics convert captured RNA into quantifiable signals while encoding spatial origin. Reverse transcription is commonly employed to synthesize complementary DNA (cDNA) from mRNA templates directly on the tissue or array, followed by spatial barcoding where unique oligonucleotide sequences (spatial barcodes) are ligated or hybridized to indicate the precise location of each transcript.[10] Amplification steps, such as PCR or rolling circle amplification (RCA), then enhance the signal for detection, enabling high-throughput sequencing or imaging readout. In sequencing-based approaches, barcoded cDNA libraries are generated and sequenced to produce count matrices, whereas imaging-based methods use fluorescent probes for direct visualization. These strategies ensure that gene expression data is demultiplexed and mapped back to two-dimensional coordinates, facilitating the reconstruction of spatial expression patterns. Resolution in spatial transcriptomics is determined by factors such as spot size, probe density, and noise mitigation techniques, which collectively define the granularity of positional information. Low-resolution methods often use array spots of 50-100 μm in diameter (e.g., 55 μm in 10x Genomics Visium), capturing transcripts from multiple cells (typically 1-10) within each bin.[11] Higher resolution is achieved through denser probe arrays or subcellular targeting, such as 10 μm beads in Slide-seqV2 or <1 μm in imaging methods like MERFISH, where probe density allows single-cell or sub-cellular profiling.[12] Signal-to-noise ratio is improved by incorporating unique molecular identifiers (UMIs), short random sequences that tag individual transcripts during capture, enabling deduplication and correction for amplification biases during data processing.[9] A basic conceptual model for spatial gene expression represents the profile at any location as G(x,y) = f(\text{transcripts at coordinates } (x,y)), where f encompasses normalization for capture efficiency, sequencing depth, and other technical variables to yield calibrated expression values. This framework underpins downstream analyses, treating the tissue as a continuous field of transcript abundances mapped to Cartesian coordinates.Comparison to other transcriptomic approaches
Spatial transcriptomics differs from bulk RNA sequencing (RNA-seq) by preserving spatial context within tissues, whereas bulk RNA-seq averages gene expression across entire cell populations, masking heterogeneity such as expression gradients in tumor cores versus edges.[13] This averaging in bulk methods obscures subtle variations driven by positional cues, limiting insights into tissue architecture, while spatial approaches map transcripts to specific coordinates, revealing localized patterns like those in developmental gradients or disease microenvironments.[13] For instance, bulk RNA-seq might detect overall elevated inflammatory markers in a tumor sample, but spatial transcriptomics can pinpoint their enrichment in immune-infiltrated regions.[6] In contrast to single-cell RNA sequencing (scRNA-seq), which dissociates tissues into individual cells to profile thousands of transcripts per cell but loses neighbor relationships, spatial transcriptomics maintains tissue integrity to capture cell-cell interactions, such as ligand-receptor signaling pairs in the tumor microenvironment.[13] scRNA-seq excels at identifying rare cell types and high-resolution cellular states (e.g., detecting 1,000–5,000 genes per cell), yet the dissociation process can introduce artifacts and eliminates spatial information essential for understanding functional neighborhoods.[13] Spatial methods thus complement scRNA-seq by integrating positional data, enabling analyses of how adjacent cells coordinate responses, though they often aggregate signals from multiple cells per capture site.[6] Compared to spatial proteomics, which targets proteins for direct functional readouts at ~1 μm resolution (e.g., via imaging mass cytometry targeting 40+ markers), spatial transcriptomics profiles RNA to assess dynamic gene expression across thousands of targets, offering higher throughput but indirect inference of protein levels due to post-transcriptional regulation.[14][15] Spatial proteomics provides subcellular protein localization and reveals active pathways, yet it is limited by antibody specificity and lower multiplexing (hundreds to thousands of proteins) compared to the genome-wide RNA coverage in transcriptomics.[14] Transcriptomic approaches thus prioritize transcriptional states and regulatory networks, while proteomic ones emphasize endpoint biology, with both benefiting from integration to bridge mRNA-to-protein discrepancies.[14] Quantitative trade-offs in spatial transcriptomics include detecting 100–10,000 genes per spot (depending on method resolution and tissue complexity), lower than the 1,000–10,000 genes per cell in scRNA-seq, but with the added value of XY coordinates for mapping.[13] Bulk RNA-seq achieves near-complete transcriptome coverage per sample but without resolution, and spatial proteomics typically profiles fewer analytes (10–1,000 proteins per region) at higher per-target costs.[14]| Approach | Resolution | Throughput | Approximate Cost per Sample | Data Type |
|---|---|---|---|---|
| Bulk RNA-seq | Tissue/population level | 10,000–20,000 genes per sample | $100–300 | Aggregated RNA |
| scRNA-seq | Single-cell (~1–10 µm) | 1,000–10,000 genes per cell; 10,000+ cells | $500–2,000 | Individual cell RNA |
| Spatial Transcriptomics | Subcellular to spot (~1–100 µm) | 100–10,000 genes per spot; 1,000–10,000 spots | $1,000–5,000 | Spatially mapped RNA |
| Spatial Proteomics | Subcellular (~1 µm) | 10–1,000 proteins per region; 100–1,000 regions | $2,000–10,000 | Spatially mapped proteins |