De-Risking Drug Discovery Through Spatially Resolved Biology
What if understanding where cells interact could unlock better drugs—and fewer failures?
The transition from bulk to single-cell next-generation sequencing (NGS) has helped distinguish true therapeutic targets from misleading signals. Single-cell NGS enables the identification of biomarkers associated with drug response and resistance, supporting more personalized therapies and adaptive clinical trial designs. This has been particularly impactful in oncology, where tumor heterogeneity strongly influences treatment outcomes.
However, even characterizing biological mechanisms at the single-cell level misses a key factor: cells do not exist nor function alone in a vacuum. Individual cell states can only be fully interpreted in the context of interactions with neighboring cells and surrounding tissue structures.
As a result, spatial biology tools have become as important to the new era of drug discovery as single-cell analysis approaches. By incorporating spatial context, scientists can characterize how cells of interest interact with key components of the tumor microenvironment.
The Spatial Biology Revolution 2026
This event will address emerging methods for high-resolution spatial analysis, offering new perspectives on tissue architecture, disease mechanisms and therapeutic targets.View Online Event
Historically, tools capable of preserving spatial context—such as traditional histopathology techniques—were limited in the information they could provide. Although these approaches reveal cell morphology and tissue architecture, they lack access to crucial molecular information, including genomic alterations, epigenetic features, immune receptor profiles, and RNA transcripts.
Without these data, it is difficult to define cell states or assess the presence of key genes and receptors. These gaps drove the development of spatial transcriptomics, which integrates histological context with molecular profiling across intact tissue.
Today, a growing class of scalable spatial biology tools is designed to pair with established single-cell workflows, enabling spatial multiomic analysis that extends transcriptomic, epigenetic, and other molecular readouts into intact tissue at true single-cell scale.
In drug discovery, this integration is especially valuable for early-stage research—such as target identification, mechanism-of-action studies, and disease modeling—where spatial context directly informs biological interpretation.
Ultimately, incorporating spatial data into a multiomic analysis workflow will lead to higher-confidence drug candidates that are better characterized and less likely to fail later in the development pipeline. By revealing context-dependent biology earlier, spatial multiomics helps eliminate unsuitable candidates sooner, reducing downstream costs and improving the likelihood of clinical success.
Key applications in drug discovery
Across drug discovery and development, opportunities to apply spatial biology tools to reveal novel insights are expanding rapidly. Several stages of the drug discovery pipeline stand to benefit substantially from spatial multiomic approaches compared with conventional techniques.
For example, scientists at Cornell University recently paired spatial transcriptomics with long-read sequencing of immune receptors to characterize the immune response in lymph nodes following viral infection.1
This in-depth spatial analysis of adaptive immune cells uncovered key localized activation niches shortly after infection, revealing actionable information that may inform the design of vaccines and immunotherapies.
Target identification is another important area where spatial information is critical. Many therapeutic strategies are designed to target a specific cell type believed to regulate tumor growth or disease progression. However, without spatial context, it is difficult to confirm whether those cells are actually present within relevant disease regions or positioned to influence pathological processes.
A gene or protein may look promising in isolation, but its therapeutic relevance depends on where and with which other cells it is expressed. Spatial transcriptomics reveals whether a target is expressed in diseased regions, specific cell niches, or critical signaling hubs. Knowing whether cells interact with each other is extremely useful for determining and predicting biological responses and, therefore, for selecting the most promising drug targets.
Spatial tools are already being used to identify new targets. The Spatial transcriptOmics Analysis Resource (SOAR), an open-access database, integrates spatial transcriptomic datasets with drug perturbation data to screen current therapies for new applications.
To showcase the ability for this approach to streamline the drug discovery process, researchers used SOAR to screen for compounds that inhibit cancer-associated signaling through the PI3K/AKT/mTOR pathway and identified eight candidate drugs for further study.2 Notably, trichostatin A—an epigenetic modulator initially identified through this approach—is currently being evaluated in a Phase 1 clinical trial for patients with relapsed or refractory hematological malignancies.
Spatial analysis at single-cell resolution can also strengthen efforts to elucidate mechanisms of action. Once the drug candidate has been identified, examining cellular responses within complex systems—such as organoids or co-culture models containing multiple interacting cell types—offers valuable insight into how therapeutic effects propagate through tissue environments.
By capturing ligand–receptor interactions, paracrine signaling, and local feedback mechanisms, spatial tools clarify whether a drug’s impact is limited to its intended target or extends to surrounding cells and pathways.
Other potential application areas include toxicology and pharmacokinetics. In toxicology studies, spatial analysis at the single-cell level enables early detection of tissue damage. With spatial context and true single-cell resolution, researchers can pinpoint off-target effects by identifying vulnerable cell populations and exposure gradients across a tissue.
Similarly, for pharmacokinetic research, understanding cell-cell interactions can provide a clearer picture of how and where the drug is working, ensuring on-target activity while minimizing unintended effects in non-relevant regions.
Spatial technology in action
While spatial multiomics is still an emerging field, several studies already demonstrate its potential to enhance drug discovery. In one recent preprint, researchers compared 12 spatial and single-cell technologies to study tumor neighborhoods and cellular interactions across three skin cancer types: melanoma, basal cell carcinoma, and cutaneous squamous cell carcinoma.3
Spatial data allowed the scientists to identify cancer-specific signatures and validate gene markers, and the integration of transcriptomic, proteomic, and glycomic data enabled the study to identify clinically relevant colocalization events between distinct T-cell populations and key metabolites. The resulting datasets from the project have been made publicly available through the Skin Cancer Atlas, creating a valuable community resource.
Spatial Biology
Download this infographic to discover how spatial biology can elucidate biological activity in relation to environment, is utilized in neuroscience and cancer research, and can be explored using various technologies.View Infographic
In another study, scientists combined highly sensitive spatial transcriptomics featuring single-cell resolution with lineage-tracing technologies to map the spatial and temporal evolution of tumors as they transition to metastatic states.4
Using a mouse model of lung adenocarcinoma, they tracked tumor expansion as its microenvironment underwent remodeling. Rapid tumor expansion was associated with increased hypoxia and immunosuppression of the tumor’s surroundings, features that are consistent with the emergence of metastasis. The study also identified metastatic events linked to specific, spatially confined subclones within the primary tumors.
Considerations for implementing spatial technology
Adopting a spatial multiomic approach requires more than pairing spatial tools with previously generated single-cell datasets or adding other data layers to a single-analyte spatial platform. Success also depends on selecting technologies that align with specific biological questions and practical constraints. Key considerations often include sensitivity—particularly for detecting low-abundance transcripts—as well as resolution sufficient to resolve relevant cellular interactions.
Equally important are streamlined laboratory and computational workflows that minimize complexity and analysis burden. When capital investment is a concern, kit-based solutions can enable researchers to incorporate spatial data into established single-cell workflows without requiring additional instrumentation. In practice, researchers must balance resolution, throughput, and cost depending on whether the goal is exploratory discovery or scaled screening.
With a thoughtfully designed spatial multiomic workflow, drug discovery programs are better positioned to identify, evaluate, and prioritize truly actionable biological targets—ultimately reducing risk and accelerating the path from discovery to the clinic. By revealing spatial heterogeneity that often underlies variable drug response and late-stage failure, spatial multiomics supports more informed go/no-go decisions earlier in development.
In many clinical failures, promising molecular targets ultimately prove to be spatially heterogeneous, active only in subsets of cells or tissue regions that are insufficient to drive durable responses. These same spatial insights can later inform patient stratification strategies by linking therapeutic response to tissue-level organization rather than bulk molecular signatures alone.
References:
1. Jiang S, Freedman J, Mantri M, et al. A temporal and spatial atlas of adaptive immune responses in the lymph node following viral infection. Proc Natl Acad Sci USA. 2026;123(5):e2504742123. doi: 10.1073/pnas.2504742123
2. Li Y, Dennis S, Hutch MR, et al. SOAR elucidates disease mechanisms and empowers drug discovery through spatial transcriptomics. bioRxiv. Preprint posted online April 17, 2022. doi: 10.1101/2022.04.17.488596
3. Prakrithi P, Grice LF, Zhang F, et al. Integrating 12 spatial and single cell technologies to characterise tumour neighbourhoods and cellular interactions in three skin cancer types. bioRxiv. Preprint posted online July 28, 2025. doi: 10.1101/2025.07.25.666708
4. Jones MG, Sun D, Min KH (Joseph), et al. Spatiotemporal lineage tracing reveals the dynamic spatial architecture of tumour growth and metastasis. bioRxiv. Preprint posted online October 24, 2024. doi: 10.1101/2024.10.21.619529
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