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Spatial architecture of development and disease

Nature Reviews Genetics volume 27, pages 118–136 (2026)Cite this article

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Abstract

Tissue architecture is a product of a multilayered molecular landscape, where even subtle disruptions in the spatial context can initiate or reflect disease processes. Recent advances in high-throughput spatial omics technologies have enabled the investigation of this complexity in stunning detail, providing groundbreaking insights into how spatial molecular organization shapes health and disease. Spatial analysis empowers the discovery of developmental and disease-associated molecular signatures, cell states and multicellular niches, as well as the evaluation of disease heterogeneity within and across organs. This Review examines spatially resolved pathological molecular alterations in a wide range of disease processes, such as developmental disorders, tumorigenesis, fibrosis and injury responses, neurodegeneration, infection and inflammation, through the lens of these universal biological frameworks. We discuss challenges, opportunities and promising examples in advancing these technologies to clinical applications, including the increasing importance of artificial intelligence. Finally, we explore possible avenues for a more comprehensive, multidimensional assessment of tissues.

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Fig. 1: Technical approaches for spatially resolved molecular capture.
Fig. 2: Biological frameworks for spatial data analysis.
Fig. 3: Spatial scales of development and disease.
Fig. 4: A possible strategy for the adoption of spatial omics into clinical pipelines.
Fig. 5: Expanding spatial molecular profiling beyond two dimensions.

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Acknowledgements

The authors thank B. Ayoglu, L. Bergenstråhle and M. Vicari for their technical insights, M. He and S. Saarenpää for their general comments on the manuscript and M. Karlén for his work on the presented illustrations. The authors are supported by the Erling-Persson Foundation, the Swedish Cancer Society, the Swedish Research Council, the Knut and Alice Wallenberg Foundation, and an ERC Advanced grant TWIGA 101021019/EC | EU Framework Programme for Research and Innovation H2020.

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Authors and Affiliations

  1. Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden

    Enikő Lázár & Joakim Lundeberg

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  1. Enikő Lázár
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  2. Joakim Lundeberg
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The authors contributed equally to all aspects of the article.

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Correspondence to Enikő Lázár or Joakim Lundeberg.

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Nature Reviews Genetics thanks Iwijn De Vlaminck, Kai Tan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

HuBMAP programme: www.hubmapconsortium.org

Human Cell Atlas: www.humancellatlas.org

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Explainable AI

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Regression models

Statistical or machine learning models that estimate relationships between variables to predict continuous outcomes.

Spatial metatranscriptomics

Mapping of microbial and host transcripts across tissue sections to study host–microbiome interactions in situ.

Spatial multi-omics

Integration of multiple molecular modalities (for example, transcriptomics, proteomics, metabolomics) with spatial resolution in tissues.

Spatial trajectory analysis

Computational reconstruction of cellular differentiation or migration paths within their native spatial context.

Spatial transcriptomic clock

A machine learning model that uses spatially resolved single-cell gene expression data to predict biological age within tissue context, capturing how cell types and their local interactions contribute to ageing and rejuvenation.

Spatial V(D)J sequencing

A method that maps immune receptor sequences to their spatial location in a tissue, enabling the study of localized immune repertoires.

Spatiotemporal perturbation

Experimental or computational disruption of biological systems with controlled variation across space and time.

Tertiary lymphoid structures

(TLSs). Ectopic lymphoid aggregates that form in chronically inflamed tissues or tumours and contribute to local immune responses.

TLS scoring

Quantitative assessment of the presence and maturity of tertiary lymphoid structures (TLSs) in tissue samples.

Tumour microenvironment

(TME). The complex cellular and molecular milieu that surrounds tumour cells and includes immune cells, fibroblasts, vasculature and extracellular matrix, influencing cancer progression.

Tumour-associated macrophages

Macrophages within tumours that often adopt pro-tumoural functions, such as promoting angiogenesis and suppressing immunity.

Virtual cell models

Computational abstractions that simulate the behaviour or state of a cell by integrating spatial and molecular data.

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Lázár, E., Lundeberg, J. Spatial architecture of development and disease. Nat Rev Genet 27, 118–136 (2026). https://doi.org/10.1038/s41576-025-00892-5

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