Introduction
Technologies Driving the Field
Biological Insights
Therapeutic Applications
Clinical Translation
Conclusions
References
Further Reading
Spatial RNA medicine integrates spatial transcriptomics with RNA-based therapeutics to map gene expression within intact tissues. It advances precision therapeutics by linking molecular patterns to microenvironments for targeted drug design and clinical translation.
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Introduction
Spatial ribonucleic acid (RNA) transcriptomics measures gene expression while preserving each molecule’s coordinates in intact tissue, tying transcripts to histology and local microenvironments. Location matters because position-dependent signaling and cell-cell contacts shape differentiation, function, and disease. Bulk RNA sequencing (RNA-seq) averages many cells, obscuring heterogeneity, whereas single-cell RNA sequencing (scRNA-seq) profiles individual cells but requires dissociation that erases spatial context.
Spatial transcriptomics maps transcripts back to positions using slide-based barcoded capture with next-generation sequencing (NGS) or imaging methods such as in situ hybridization (ISH) and in situ sequencing (ISS), enabling region-specific programs and niche-level biomarkers.1,2,3
This article demonstrates how spatially resolved transcriptomics enables the mapping of gene expression to tissue context, informing targeted therapies. It highlights key platforms, tumor-immune insights, spatial biomarkers, and practical paths to clinical translation.
Technologies Driving the Field
Spatial transcriptomics is powered by two complementary technology families. Imaging-based in situ hybridization methods such as multiplexed error-robust fluorescence in situ hybridization (MERFISH) and sequential fluorescence in situ hybridization (seqFISH/seqFISH+) use cyclic probe hybridization and coded readouts to quantify thousands of RNAs per cell. Commercial platforms include MERscope (Vizgen) and Spatial Genomics’ seqFISH. These systems routinely achieve single-cell to subcellular resolution, leaving tissue intact for parallel pathology and multiplex staining.3
Sequencing-based, spatial barcode approaches label positions on slides or beads, allowing captured transcripts to be mapped back to their corresponding coordinates. This strategy was popularized by 10x Genomics’ Visium, and probe-based upgrades (Visium V2/Visium HD) broaden sample compatibility beyond fresh frozen tissue. Typical gene capture and resolution differ across platforms, with Visium polyadenylated A spots (~55 μm) detecting thousands of genes per spot and higher-resolution formats trading some depth for finer grids.3
Beyond these commercial platforms, emerging 3D spatial transcriptomics and multi-omic integrations now combine RNA with epigenetic and proteomic mapping, enabling whole-tissue molecular atlases that capture signaling gradients and rare cell states inaccessible to 2D sectioning.1,3
Newer imaging instruments, such as 10x Genomics Xenium in situ and NanoString CosMx Spatial Molecular Imager (SMI), deliver targeted high-plex panels on formalin-fixed, paraffin-embedded (FFPE) sections, with head-to-head studies highlighting strong sensitivity and broad applicability.3
Critically, these RNA maps integrate seamlessly with protein phenotyping workflows, which pair spatial transcriptomics with RNAscope, multiplex immunohistochemistry (IHC), cyclic immunofluorescence, and imaging mass cytometry (IMC) to validate targets, profile cell states, and anchor discoveries to routine pathology.3
Biological Insights
Tumors are mosaics of ecological niches where cancer, stromal, and immune cells arrange into distinct spatial patterns that shape progression and therapy response. Quantitative mapping of whole-slide histology shows that not only the density but also the neighborhood organization of tumor-infiltrating lymphocytes matters. Immune cells clustered with cancer nests signal more effective local immune pressure, whereas segregated patterns suggest immune evasion. These niche-level patterns, captured with computational ecology tools, reveal how spatial heterogeneity underpins prognosis and should inform biomarker design and patient selection.4
Spatial ecology analyses show that immune infiltration follows fractal or gradient-based distributions rather than uniform clustering, and these spatial metrics correlate strongly with survival and therapy response in multiple solid tumor types.4,6
Resource gradients are equally uneven. Disordered vasculature creates gradients in oxygen and nutrients, driving local differences in proliferation and receptor expression. For example, estrogen-receptor variation aligns with vascular area, underscoring the microenvironmental control of phenotype and treatment relevance. Temporal fluctuations such as intermittent hypoxia can further select for resistant clones.
Together, these data argue that signaling fields, shaped by perfusion and cell-cell interactions, form spatial gradients of pro-angiogenic factors and cytokines (for example, vascular endothelial growth factor (VEGF), Wingless/INT-1 (Wnt), and interferon-γ (IFN-γ)). Mapping these gradients helps predict where drugs penetrate, which targets are engaged, and which pockets will harbor resistance, guiding drug-delivery strategies, niche-matched target validation, and rational combinations that modulate vasculature and immunity alongside tumor-intrinsic pathways.4,6
Therapeutic Applications
Identifying treatment-ready RNA signatures can guide the delivery of messenger ribonucleic acid (mRNA) and small interfering ribonucleic acid (siRNA) therapies to the target cells. Receptor or ligand profiles on tumor and stromal microenvironments inform the selection of ligands for nanoparticles, improving tissue-specific uptake beyond the liver. Studies highlight the active development of tissue-targeting strategies layered onto lipid nanoparticles and other carriers, alongside the chemical optimization of RNA, to enhance stability and intracellular delivery in cancer settings.
Practical examples include folate-decorated deoxyribonucleic acid (DNA) tetrahedron carriers that accumulate in tumors and silence targets in vivo, as well as the broader push toward tissue-specific ligands analogous to N-acetylgalactosamine for liver delivery, aiming to expand precise extrahepatic targeting. These approaches address transport efficiency, bioavailability, and endosomal escape, which are key barriers for durable, tumor-localized RNA action.5
Advances in lipid nanoparticle chemistry and self-amplifying RNA (saRNA) constructs have further improved the potency of intratumoral delivery and the duration of protein expression, providing new options for local immunomodulation and neoantigen presentation.5
Mapping drug response spatially within tissues is equally critical because heterogeneous microenvironments and dense extracellular matrices create uneven intratumoral distributions that blunt efficacy. Characterizing where RNA carriers penetrate, release their cargo, and trigger immune responses helps validate targets and refine dosing or combination strategies.
Various studies frame this need within the context of tumor heterogeneity and precision oncology goals, noting that abnormal vasculature and matrix limit distribution, and that delivery, specificity, and immunogenicity must be engineered with the tissue context in mind. Spatial readouts of uptake and response can therefore guide ligand choice, route of administration, and pairings with checkpoint blockade or matrix-modifying agents to convert poorly accessible regions into responsive niches.5,6
Clinical Translation
Clinical translation of spatial transcriptomics is maturing across oncology and fibrotic disease. In immuno-oncology, spatial biomarkers such as programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1) expression, mismatch-repair status with microsatellite instability, tumor mutational burden, and inflammation or Cluster of Differentiation 8 (CD8)-cell gene signatures guide patient selection and companion diagnostics, improving response prediction and trial design. Spatial profiling also supports “seamless” precision trials and liquid-biopsy-enabled monitoring, accelerating approvals in biomarker-defined populations.2,6
In fibrosis-rich tumors, spatial maps of cancer-associated fibroblasts reveal fibroblast activation protein (FAP)-positive clusters, transforming growth factor-beta activity, and elastin microfibril interface-derived protein 1 (EMILIN1) gradients that associate with cytotoxic CD8-cell infiltration and prognosis, positioning stromal-immune architectures as actionable biomarkers for immunotherapy and antifibrotic strategies.2,6
Emerging evidence also links spatial transcriptomic readouts to digital pathology and AI-driven prognostic scoring, allowing automated quantification of immune exclusion, fibrosis, and angiogenesis as predictive endpoints in adaptive trial design.2,6
Pharma adoption is rising as spatial readouts inform target discovery, responder enrichment, and combination design. Large drug developers such as Roche and Novartis, and data-driven platforms like Tempus, are integrating spatial data streams alongside genomic signatures to prioritize mechanisms, stratify cohorts, and operationalize companion diagnostics within adaptive, biomarker-led studies. The broader shift to biomarker-defined, tumor-agnostic development, along with approved companion diagnostics, underscores this integration path.2,6
Together, these advances translate spatial biology into clinical decisions by aligning immune context, fibrotic stroma, and drug design, moving from descriptive maps to predictive, trial-ready biomarkers.2,6
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Conclusions
Spatial RNA medicine will shift disease taxonomy from organ- and histology-based labels to spatially defined ecosystems of cells, signals, and matrix. Mapping transcripts to neighborhoods, gradients, and interfaces yields predictive biomarkers that stratify patients by their microenvironmental context, rather than averages. These readouts de-risk targets, guide mRNA and siRNA delivery to the right cells, and monitor on-tissue pharmacodynamics.
In clinics, spatial profiles will power companion diagnostics, adaptive trial enrichment, and tumor-agnostic, niche-specific therapies, including immuno- and antifibrotic combinations. Ongoing integration of single-cell and spatial omics promises a unified atlas of cell states and drug responses that redefines precision medicine’s feedback loop from discovery to clinic.
References
- Molla Desta, G., & Birhanu, A. G. (2025). Advancements in single-cell RNA sequencing and spatial transcriptomics: transforming biomedical research. Acta Biochimica Polonica. 72. DOI:10.3389/abp.2025.13922.
- Zhang, Y., Gong, S., & Liu, X. (2024). Spatial transcriptomics: a new frontier in accurate localization of breast cancer diagnosis and treatment. Frontiers in Immunology. 15. DOI:10.3389/fimmu.2024.1483595.
- Wang, N., et al. (2024). Next-generation spatial transcriptomics. MedComm. 5(10). DOI:10.1002/mco2.765.
- Yuan, Y. (2016). Spatial heterogeneity in the tumor microenvironment. CSH Perspectives in Medicine. 6(8). DOI:10.1101/cshperspect.a026583.
- Yan, Y., et al. (2025). Advances in RNA-based cancer therapeutics. Molecular Cancer. 24(1). DOI:10.1186/s12943-025-02463-y.
- Franklin, M. R., et al. (2022). Immuno-oncology trends. Journal for ImmunoTherapy of Cancer. 10(1). DOI:10.1136/jitc-2021-003231.
Further Reading
Last Updated: Dec 5, 2025