Spatial Omics: Mapping Tissue Architecture

Updated 13 November 2025

Spatial omics is a suite of high-plex molecular assays that preserve tissue context using imaging and sequencing modalities.
It integrates transcriptomic, proteomic, epigenomic, and metabolic data to elucidate cell interactions, developmental gradients, and pathological microenvironments.
Advanced AI and computational tools analyze spatial graphs and multi-modal data to uncover tissue architecture and facilitate biomedical insights.

Spatial omics comprises a set of high-plex molecular measurement technologies and computational methodologies that preserve and interrogate spatial organization within intact tissues. Unlike bulk or dissociated single-cell assays, spatial omics maintains the physical coordinates and contextual relationships of measurements—RNA, proteins, metabolites, and chromatin features—allowing direct analysis of cell–cell interactions, tissue microarchitectures, developmental gradients, and pathological microenvironments. This field encompasses both imaging-based and sequencing-based modalities at scales ranging from subcellular (~0.25 µm) to multicellular domains (>100 µm), and has recently expanded to multi-modal datasets integrating transcriptomic, proteomic, epigenomic, and morphological information. Advances in artificial intelligence and statistical modeling are enabling high-throughput computational analysis, biological hypothesis generation, and clinical translation.

1. Technological Foundations and Modalities

Spatial omics technologies can be classified into two main categories: imaging-based and barcoding/sequencing-based platforms. Imaging-based approaches include multiplexed in situ hybridization methods such as MERFISH and seqFISH, achieving subcellular resolution (~100–200 nm) and single-molecule sensitivity but requiring iterative probe hybridization and complex decoding schemes (Fan et al., 16 Sep 2025). Sequencing-based methods (Slide-seqV2, 10x Visium/VisiumHD, Xenium, CosMx) utilize spatial barcoding on beads or arrays, enabling unbiased whole-transcriptome profiling at single-cell to multi-cellular spot resolution (~2–55 µm) (Noorbakhsh et al., 30 Jun 2025).

Spatial proteomics platforms such as CODEX, IMC, and CellDive use antibody-based labeling for tens to hundreds of proteins at subcellular resolutions. Mass-spectrometry imaging (MALDI, DESI) and spatial metabolomics provide untargeted molecular profiles but with lower throughput and spatial precision (Camacho et al., 18 Dec 2024). Epigenomic spatial assays (spatial ATAC-seq, spatial CUT&Tag) enable mapping of chromatin accessibility domains. Emerging technologies are driving rapid expansion in resolution, throughput, and multi-layer measurement capabilities, often matched with histopathology images for contextual interpretation (Fan et al., 16 Sep 2025).

2. Computational Methodologies in Spatial Omics

The analysis of spatial omics data necessitates methods that accommodate high dimensionality, spatial autocorrelation, sparsity, and technological heterogeneity. Foundational computational frameworks include:

Graph-based neighborhood inference: Cells/spots are modeled as nodes in spatial graphs with adjacency matrices encoding neighbor relations. Graph neural networks (SpaGCN, STAGATE) propagate features using normalized adjacency and degree matrices, supporting clustering, smoothing, and domain identification (Fan et al., 16 Sep 2025).
Matrix and tensor factorizations: Techniques such as non-negative matrix factorization (NMF), coupled NMF, and tensor decomposition integrate multi-modal data and extract interpretable spatial programs (Fan et al., 16 Sep 2025, Camacho et al., 18 Dec 2024).
Generative modeling and autoencoders: Variational autoencoders (VAEs) and diffusion models such as SpaVI, stDiff, and SpaDiT denoise expression, impute missing modalities, and enable generative augmentation, often regularized with spatial priors (Noorbakhsh et al., 30 Jun 2025).
Bayesian nonparametric clustering and latent block models: Models like BNPMFA (Zhu et al., 26 Aug 2024) and BISON (Zhu et al., 19 Feb 2025) simultaneously infer the number of spatial domains and feature clusters under spatial Markov random field constraints, providing robust domain discovery and discriminating gene marker identification.
Topological data analysis (TDA): Methods such as PersiST (Boyle et al., 7 May 2025) employ persistent homology filtrations to quantify the spatial structure and robustness of gene or metabolite features, yielding continuous measures (Coefficient of Spatial Structure, CoSS) for spatial variability and heterogeneity.
Integrated frameworks: Multi-objective optimization (DOT (Rahimi et al., 2023)), interactive visualization workflows (Somarakis et al., 2020, Xu et al., 2021), and spatial statistics toolkits (pasta (Emons et al., 2 Dec 2024)) offer scalable, user-guided analyses ranging from neighborhood enrichment and co-localization to formal spatial autocorrelation statistics (Moran’s I, Getis-Ord G*).

3. AI Paradigms and Model Interpretability

Spatial omics is increasingly analyzed using three complementary AI modeling paradigms (Noorbakhsh et al., 30 Jun 2025):

Data-driven spatial AI: Minimal biological priors; architectures include CNNs/ViTs on image channels and GNNs on cell–cell graphs. Foundation models (DINOv2, Virchow, CellPLM, Nicheformer, SpaFormer) provide latent embeddings for large-scale classification, segmentation, and cell-type annotation.
Constraint-based spatial AI: Explicit incorporation of spatial regularization, continuity penalties, mutual information bottlenecks (bioIB), and known ligand–receptor patterns into loss functions, e.g., L = L_data + λ L_constraint. Diffusion models conditioned on neighborhood structure address denoising and latent augmentation (SpaDiT, stMCDI).
Mechanistic spatial modeling: Integration of physical and biological priors—reaction–diffusion PDEs, physics-informed neural networks (PINNs), Kolmogorov–Arnold Networks (KANs)—for causal inference of biophysical parameters, cell–cell communication, and evolutionary trajectories (HoloNet, SpaCCC).

Interpretability is supported post-hoc via feature attribution methods (SHAP, LIME), entropy- and thermodynamics-based latent dimension analysis, and spatial-frequency decomposition (graph Fourier, SpaGFT), with functional tissue units (FTUs) defined as minimal repeating multicellular structures (Noorbakhsh et al., 30 Jun 2025).

4. Biological and Clinical Applications

Spatial omics underpins discovery in developmental biology, neuroscience, oncology, and organ physiology:

Developmental architectures: High-resolution maps reveal gene expression gradients, spatially variable genes (SVGs, e.g., Fezf2, Satb2), and differentiation trajectories within tissue layers (MERFISH/Stereo-seq) (Fan et al., 16 Sep 2025).
Tumor microenvironments: Multiplexed spatial proteomics (e.g., CODEX, CELESTA annotation) and transcriptomics delineate cellular neighborhoods, immune phenotypes, ligand–receptor mediated niches (e.g., Vimentin⁺ macrophage–Treg co-localization in HCC) (Fan et al., 16 Sep 2025).
Epigenomic and metabolic zonation: Spatial-ATAC-seq and MSI map chromatin domains, metabolic hotspots, and spatial distribution of activity-regulating loci, aligned with tissue-specific functions such as hypoxia responses in the liver (Fan et al., 16 Sep 2025).
Trajectory inference: Flow matching and optimal transport methods (ContextFlow (Rathod et al., 3 Oct 2025)) reconstruct dynamic developmental or regenerative processes by contextualizing gene–space transitions with spatial priors (local tissue organization, ligand–receptor profiles).
Clinical diagnostics and precision medicine: Automated classification, segmentation, and multi-modal fusion (Histology–omics integration, STAMP, SpatialGlue) enable disease stratification and biomarker discovery. Neighborhood enrichment analytics (analytical z-scores (Andersson et al., 23 Jun 2025)) facilitate rapid, large-scale spatial co-localization analyses for tissue classification and cohort comparison.

5. Challenges, Limitations, and Future Directions

Spatial omics confronts several unresolved challenges (Noorbakhsh et al., 30 Jun 2025, Fan et al., 16 Sep 2025):

Technical:
- Data interoperability and standardization across diverse platforms and modalities.
- Scalability for high-dimensional datasets (spatial transcriptomics, multiplexed imaging, multi-omics fusion).
- Limited availability of time-series and perturbation datasets needed for mechanistic inference.
- Benchmarking and evaluation lacking consensus multiscale tasks for model comparison (cell-level, microenvironment, slide-level).
Biological:
- Need to jointly model genotype, epigenetic states, and morphology for integrated spatial evolutionary mapping.
- Cross-species transferability (mouse–human mapping) of mechanistic discoveries.
- Incorporation of mechanical and metabolic tissue constraints affecting heterogeneity and subtype organization.
Modeling:
- Development of hybrid architectures coupling explicit biophysical/biological priors with deep learning for improved interpretability and lower data requirements.
- Establishment of multiscale benchmarking datasets and annotation schemas (SenNet, HTAN, CROST, STOmicsDB).
- Extension to 3D spatial omics, multi-layer simultaneous assays, and real-time AI-driven experimental design.

Recommendations include community-led standard setting for data formats and protocols, investments in mechanistic experimental platforms to validate AI models, and promotion of open-source repositories for reproducibility and cross-domain application (Noorbakhsh et al., 30 Jun 2025).

6. Key Statistical and Topological Tools

Spatial statistics and TDA play essential roles in quantifying spatial associations and heterogeneity (Emons et al., 2 Dec 2024, Boyle et al., 7 May 2025):

Point and lattice statistics: Ripley’s K/L-function and Moran’s I measure spatial clustering, autocorrelation, and hot-spot detection, supporting identification of spatial domains and SVGs.
Neighborhood enrichment: Analytical approaches supplant traditional permutation-based methods (Squidpy) for rapid and high-fidelity calculation of co-localization z-scores (Andersson et al., 23 Jun 2025).
Persistent homology: PersiST computes a continuous Coefficient of Spatial Structure (CoSS) from barcodes of birth–death pairs in lower-star filtrations, robustly identifying spatially variable genes and enabling cross-sample heterogeneity comparisons (Boyle et al., 7 May 2025).
Visualization and interactivity: Integrated visual analysis (raincloud plots, heatmaps, cluster-linked tissue views) allows effective cohort comparison, microenvironment discovery, and outlier detection, enhancing interpretability of high-dimensional spatial omics data (Somarakis et al., 2020, Xu et al., 2021).

7. Integration, Multi-Modality, and Translation

The ongoing evolution of spatial omics research focuses on deep integration of multi-modal molecular data—transcriptomic, proteomic, epigenomic, metabolomic, and morphological features—and translation from bench to bedside:

Multi-modal fusion: Adaptive graph-based aggregation (PRAGA (Huang et al., 19 Sep 2024)) and prototype-aware contrastive learning optimize cluster discovery in cross-modal datasets, attenuating sequencing noise and batch effects.
Morphology–omics models: Frameworks distinguish translation (gene expression prediction from morphology; super-resolution mapping) from integration (domain and niche discovery, trajectory mapping) via information-theoretic and deep learning approaches (Chelebian et al., 30 Jul 2024, Zhu et al., 20 Aug 2025).
Clinical impact: Standardized pipelines and large-scale, multimodal foundation models enable precision medicine applications, virtual cell simulations, and spatial biomarker stratification, with explicit attention to model robustness, generalizability, and annotation standards (Fan et al., 16 Sep 2025, Noorbakhsh et al., 30 Jun 2025).

By uniting methodological, technological, and biological advances, spatial omics now enables systematic exploration of the architecture, dynamics, and regulation of mammalian tissues, supporting both theoretical insights and translational applications across biomedicine.