What is Spatial Biology?

The body's building blocks mediate essential biological processes dynamically by interacting with each other as they move within their native microenvironments. Spatial biology studies the organization and movement of cells and molecules to elucidate mechanisms in health and disease.

This field provides critical insights into tumor microenvironments, proliferation, metastasis and abnormalities in tissue architecture. Unlike single-cell approaches, spatial biology focuses on positional information, enabling precise gene expression mapping, protein localization, morphology, clustering and migration, impacting disease progression.¹

Spatial biology encompasses several subfields collectively known as spatial omics. These include:

• spatial transcriptomics, which maps transcriptional activity across tissues²
• spatial proteomics, which localizes proteins, antibodies and biomarkers³
• spatial genomics, which identifies genomic architecture in the nucleus and variations throughout tissues⁴
• spatial metabolomics, which profiles the distribution of metabolites such as sugars and lipids⁵

Core Concepts in Spatial Biology

Spatial profiling and phenotyping

Spatial biology aims to profile biomolecules directly in situ, retaining their positional context within tissues. This process, called spatial profiling, captures the distribution of genes, transcriptional machinery, proteins and metabolites across cell or tissue samples while maintaining tissue integrity. ⁶

Spatial phenotyping, which identifies and classifies cell types based on their organization and interactions with their microenvironments, is closely linked to spatial profiling. ⁷

These approaches collectively provide an integrated view of tissue organization and cellular diversity.

Spatial atlas and spatial context

Advancements in high-throughput spatial biology gave rise to spatial atlases, catalogs of molecular and cellular landscapes of healthy and diseased tissues. These atlases provide frameworks for comparing tissue and cellular organization across individuals, conditions or experimental models. By integrating large-scale spatial datasets, researchers can uncover cellular communication patterns and spatial heterogeneity underlying disease profiles.⁸

Spatial resolution and organization

Spatially resolved data is a critical component of spatial biology. Spatial resolution indicates the level of organizational and dynamic information that can be collected from the sample. Spatially resolved data differ from single-cell data in the positional context of the molecules they harbor. Thus, it provides a richer understanding of how cellular states and interactions are embedded within tissue architecture.⁹

Molecular Targets and Biomolecules

Each subfield of spatial biology focuses on distinct classes of molecular targets and applies specific technologies to map them in situ.

Gene expression and transcriptomics

Spatial transcriptomics captures the positional context of transcriptional machinery, such as the localization of RNA molecules and transcripts directly within intact tissues. It is based on in situ hybridization, where RNA is localized by tagging with complementary DNAs or modified nucleic acids.⁹ Researchers can map gene expression patterns by combining this labeling method with next-generation sequencing or fluorescent imaging while preserving the native tissue architecture. Thus, they can uncover region-specific transcriptional programs and cell–cell interactions.²

Protein localization and proteomics

Spatial proteomics uses approaches such as antibody-based imaging and mass spectrometry to determine the distribution of proteins across tissue sections and how this distribution influences their function. These methods can reveal cell–type–specific protein expression and highlight spatial variations in signaling networks and functional domains within tissues. Spatial proteomics is commonly used to characterize the tissue-level spatial variations in cancer, autoimmune diseases, cardiovascular diseases and neurological disorders.³

Metabolite mapping and biomarkers

Spatial metabolomics profiles metabolites, lipids and other small molecules across tissues. Mapping these biomolecules through mass spectrometry, segmentation and clustering analysis provides insight into metabolic pathways, microenvironmental conditions and biochemical heterogeneity. Similarly, spatial mapping of clinically relevant biomarkers supports the study of disease mechanisms and therapeutic responses as a function of native tissue organization.⁵

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Techniques and Technologies in Spatial Biology

Imaging and cytometry

Imaging and quantification are fundamental in spatial biology. Techniques such as imaging mass cytometry, multiplex imaging, immunofluorescence and fluorescence imaging allow simultaneous detection of multiple proteins, RNA molecules, metabolic byproducts and other biomolecules within tissue sections.⁹ Advanced microscopy technologies, mainly super-resolution microscopy, further enhance the spatial resolution beyond the diffraction limit, providing insights into cellular organization and molecular interactions on a nanometer scale.¹⁰

Advanced analysis platforms

The advancements in automated imaging systems and integrated computational biology support have enabled high-plex spatial biology workflows. These platforms streamline steps from sample preparation to image acquisition and data analysis, allowing the simultaneous measurement and localization of several targets across thousands of cells. Each high-plex solution has distinct advantages in resolution, throughput and molecular coverage, giving researchers flexibility when selecting the appropriate method for their biological question.¹¹

Innovations in spatial biology

Innovations in spatial biology aim to enhance throughput and drive multiplex imaging, which allows the detection of hundreds of targets in a single sample. AI/ML-driven approaches, such as deep learning, strengthen the analysis of high-throughput workflows by facilitating cell type classification, spatial pattern recognition and predictive modeling. These advances are expected to improve speed and accuracy for comprehensive and high-resolution mapping of tissue microenvironments in disease research.¹²

Applications of Spatial Biology

Cancer research

Cancer cells are in constant contact with components in the tumor microenvironment, such as the extracellular matrix, immune cells, growth factors and cytokines. Therefore, spatial biology has become a critical tool in cancer research, providing detailed insights into tumor microenvironment heterogeneity.¹³ By mapping the spatial distribution of immune, stromal and tumor cells, researchers can better understand cell–cell interactions that influence tumor growth and metastasis. Furthermore, spatial profiling of target molecules in the tumor microenvironment empowers more accurate prediction of therapeutic responses, guiding the design of targeted treatments in precision oncology.¹,⁹

Developmental biology and tissue mapping

Spatial profiling is crucial for developmental biology when mapping tissue architecture and identifying patterns that govern organogenesis.¹⁴ Researchers can develop comprehensive benchmarks for understanding normal tissue formation and cellular differentiation processes by generating spatial atlases of organs at different developmental stages. These atlases serve as foundational resources for studying normal development and developmental disorders.⁸

Disease research

Alongside cancer, spatial biology studies various diseases by tracking changes in tissue organization and cellular interactions. Investigating spatial alterations in tissue microenvironments enables researchers to characterize disease progression, identify early pathological events and study the interplay between different cell types in health and disease. This spatially resolved perspective is invaluable for uncovering mechanisms overlooked in single-cell analyses.¹⁵⁻¹⁷

References

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