Spatial omics have revolutionized biomedical research in recent years. The use of spatial techniques unlocks cell-specific understanding of disease, which supports the development of precision therapies aimed at the right cells and pathways. This has been especially valuable in oncology, where cellular heterogeneity and cell-to-cell interactions influence tumor development and treatment response.
In the brain, which is made up of over 3,000 cell types, these techniques help clarify how specific cell types contribute to brain disease and how local patterns influence disease progression. This level of resolution supports more targeted approaches to mechanism research and biomarker discovery.
Spatial context also informs how neuroscience teams study disease pathways within the brain and spinal cord. Spatial maps reveal how vulnerable cell populations cluster, how microenvironments evolve with pathology, and how these patterns connect to measurable biomarkers. These insights guide early development decisions, including target refinement, biomarker selection, model evaluation, and translational planning.
This blog outlines how spatial omics are being used in neuroscience today, with examples from real programs and a biomarker development pipeline aligned to sponsor workflows.
Spatial omics can uncover disease processes within intact regions of the brain and spinal cord. These maps reveal patterns that influence the selection of targets and biomarkers.
Spatial techniques reveal how disease activity organizes within specific regions of the brain. In Alzheimer’s disease, microglial activation increases around amyloid plaques. In amyotrophic lateral sclerosis (ALS), astrocyte states shift based on proximity to degenerating motor neurons. These patterns clarify how disease evolves and help identify biomarkers that reflect meaningful biology.
In Parkinson’s disease, dopaminergic neuron subtypes map to distinct zones of the substantia nigra. Resistant versus vulnerable dopaminergic neurons also express distinct markers. This type of vulnerability signature can guide the search for biomarkers tied to the affected subtype and can help identify patient groups who may benefit from targeted therapies.
Spatial maps can help answer these key questions in early research:
Accordingly, spatial context can support a clearer prioritization of biomarkers and stronger biological rationales in early R&D.
Spatial omics can contribute to neuroscience programs by strengthening the connection between target biology, tissue architecture, and translational biomarkers. While broad adoption in neuroscience is still emerging, several industry initiatives demonstrate how spatial platforms can support biomarker development. These approaches can be applied directly to brain diseases, where cell states and local microenvironments define disease progression.
Owkin has developed an AI-powered spatial omics platform designed to extract biomarker signatures from tissue sections. Their work focuses largely on oncology, where spatial biomarkers are used to characterize cell–cell interactions, immune environments, and histological patterns relevant to drug response. The underlying principles of this approach translate well to neuroscience.
Such tools can be applied to brain tissue to identify cell states, map local pathology, and derive spatially anchored biomarkers. These biomarkers can then be linked to measurable readouts such as cerebrospinal fluid (CSF) markers, imaging signals, or digital endpoints. This workflow supports early translational planning by grounding biomarker selection in real human tissue architecture.
Spatial omics can help define patient subgroups based on cell state profiles or regional vulnerability patterns seen in brain tissue. These signatures can inform stratification hypotheses by identifying patients whose biology aligns most closely with the pathway of interest.
This approach can help teams determine which molecular subtypes may respond to a specific mechanism, which biomarkers should be used for enrichment, and how disease heterogeneity might influence trial design.
Spatial data from human tissue provide a reference point for evaluating preclinical models. Comparing spatial signatures across species helps identify which models replicate key features of human disease. This improves the selection of models for biomarker testing, mechanism studies, and early pharmacology work.
Spatial pattern alignment can also support refinement of disease models by highlighting where cellular composition or regional patterns diverge from human biology.
Spatial approaches allow teams to trace how a disease mechanism unfolds within specific cellular neighborhoods. This information supports refinement of therapeutic hypotheses by showing which cell populations drive pathology, how target pathways behave in local contexts, and which downstream events should be monitored during development.
These insights can strengthen the rationale for mechanism-based therapies and guide decisions about which molecular targets and pathways to prioritize.
The five-star matrix for neuroscience drug discovery, proposed by Roche researchers, outlines five dimensions of translational evidence: biodistribution, target binding and occupancy, proximal effect, biological effect, and disease effect. It also structures evidence across five systems: biochemical, cellular, ex vivo, preclinical, and clinical.
Spatial omics can contribute to several of these dimensions and systems:
Biodistribution: Spatial transcriptomics and proteomics can map where a target or pathway is active across brain regions and cell types, providing ex vivo evidence that supports model selection and early mechanism assessments.
Proximal and biological effects: Spatial signatures can reveal the immediate local consequences of target engagement. This helps identify biomarkers that reflect pathway activity in disease-relevant cell populations.
Disease effect: Spatially anchored cell states show how disease processes evolve within specific tissue contexts. These signatures help identify biomarkers that connect early molecular events to downstream clinical endpoints.
By aligning spatial outputs with the matrix, teams can place spatial biomarkers into a broader translational strategy that links early mechanistic work to clinical readouts.
Spatial omics offer a structured way to understand how brain diseases form within intact tissue, which supports biomarker development, patient stratification, model selection, and therapeutic hypothesis refinement. These insights help create stronger translational foundations for neuroscience programs and increase confidence in early development decisions.
Spatially grounded biomarkers and cell-state signatures can also strengthen clinical trial strategies. When a biomarker reflects a disease-relevant cell population or regional vulnerability pattern, it can be linked to genotyping, phenotyping, or clinical enrichment workflows. This helps teams identify the right patient groups, map tissue-level biology to accessible endpoints, and build study populations that align with the underlying mechanism. These applications support more precise trial design without extending beyond what is feasible today.
As spatial methods continue to advance, neuroscience teams can integrate these insights into biomarker strategies, IND packages, and early clinical planning. The next steps include identifying disease areas where cell-state signatures influence progression, evaluating spatial findings that align with a program’s mechanism of action, and determining which biomarkers can be traced back to these signatures in clinical samples.
For a deeper look at how precision biomarkers shape brain disease research, read our whitepaper on precision medicine in brain tumors.