The Future of Spatial Biology: Perspectives from the SiSP Summer School in Spatial Biology 2026 Expert Panel

Introduction

Spatial biology has rapidly emerged as one of the most transformative technologies in modern biomedical research. By preserving the spatial organization of cells while simultaneously measuring molecular features, spatial biology enables researchers to investigate biological systems in ways that were previously impossible. Rather than viewing tissues as collections of isolated cells, spatial approaches reveal how cellular neighborhoods, tissue architecture, and molecular interactions collectively determine biological function and disease.

The rapid expansion of the field has also created new challenges. Numerous imaging- and sequencing-based platforms have emerged, each offering different strengths, limitations, and applications. Researchers entering the field are therefore faced with difficult questions. Which platform should they choose? How much multiplexing is enough? Will artificial intelligence replace traditional analysis? Can spatial biology become part of routine clinical practice?

These questions formed the basis of the concluding expert panel discussion at the SiSP Summer School in Spatial Biology 2026, held on 6–10 July 2026 at the Srisavarindhira Building, Faculty of Medicine Siriraj Hospital, Mahidol University. Representatives from multiple leading spatial biology platforms — including t-CyCIF, Imaging Mass Cytometry (IMC), Quanterix, Miltenyi Biotec, Standard BioTools, Next Level Genomics, and Thermo Fisher Scientific — shared their perspectives on the future of the field. Although each platform reflects different technological philosophies, the discussion revealed remarkable consensus regarding the direction in which spatial biology is evolving.

Spatial Biology Is Moving Beyond Single-Modality Measurements

One of the clearest messages from the panel was that spatial biology is no longer viewed simply as multiplex imaging. Instead, the field is evolving toward comprehensive spatial characterization of biological systems through the integration of multiple molecular modalities.

Current platforms already allow simultaneous measurement of dozens to hundreds of proteins or thousands of RNA transcripts. However, panelists agreed that future spatial biology will increasingly combine information across DNA, RNA, proteins, metabolites, morphology, and tissue architecture within the same specimen. Several experts suggested that spatial genomics, including mutation detection and chromatin organization, represents the next major frontier once current technical limitations are overcome.

Equally important is the transition from two-dimensional tissue sections toward volumetric tissue reconstruction. Three-dimensional pathology was repeatedly highlighted as an emerging direction that will enable researchers to study biological organization at the organ level rather than within individual tissue slices. Together, multimodal and multidimensional datasets are expected to redefine how biological systems are studied over the coming decade.

Technology Should Follow the Biological Question

Despite representing different commercial platforms, the panelists consistently emphasized that no single technology is optimal for every research application.

High-plex imaging technologies excel in exploratory discovery, allowing investigators to characterize complex cellular ecosystems and identify previously unrecognized cell states. Lower-plex systems, by contrast, provide faster acquisition, greater accessibility, and improved scalability for validation studies and clinical applications. Three-dimensional imaging addresses questions related to tissue architecture that cannot be answered using conventional two-dimensional microscopy.

Rather than comparing technologies solely on the basis of multiplexing capability or resolution, the panel advocated a question-driven approach to experimental design. Investigators should first define the biological hypothesis they wish to address before selecting the technology that best answers that question. In this view, different spatial platforms should be regarded as complementary tools within an integrated experimental workflow rather than competing technologies.

Artificial Intelligence Will Become an Essential Partner

Artificial intelligence was widely viewed as one of the most significant drivers of future progress in spatial biology.

Current applications already include automated cell segmentation, phenotyping, image registration, and tissue annotation. As larger reference datasets become available, AI is expected to play increasingly important roles in biomarker discovery, disease classification, patient stratification, and clinical decision support.

However, panelists cautioned against viewing AI as a replacement for biological understanding. Machine learning models remain fundamentally dependent on high-quality experimental data. Poor tissue preparation, inconsistent staining, or inaccurate annotation inevitably propagate errors throughout downstream analyses. As one participant succinctly summarized the risk, poor inputs will always produce poor outputs, however sophisticated the model. Robust experimental design and careful biological interpretation will therefore remain essential despite continuing advances in computational methods.

Interestingly, several speakers also suggested that AI could reshape experimental planning itself. Rather than relying exclusively on experience or literature searches, researchers may increasingly use large language models and intelligent analytical tools to refine hypotheses, optimize marker panels, and prioritize experiments before entering the laboratory.

Clinical Translation Will Depend on Simplification Rather Than Complexity

Perhaps the most thought-provoking discussion concerned the future clinical implementation of spatial biology.

Unlike next-generation sequencing, where increasingly comprehensive molecular profiling became the clinical endpoint itself, panelists proposed that spatial biology may follow a different trajectory. Highly multiplexed spatial assays are likely to remain discovery tools that generate large training datasets for artificial intelligence. These datasets could subsequently be distilled into smaller, standardized biomarker panels suitable for routine pathology laboratories.

In this model, spatial biology functions as the discovery engine, while simplified assays and AI-assisted pathology become the clinical interface. Rather than performing highly multiplexed imaging on every patient, future clinical workflows may reserve comprehensive spatial profiling for selected cases while applying computational models trained on these datasets to broader patient populations.

The panel also recognized that successful clinical translation will require substantial standardization. Reproducible tissue processing, validated antibody panels, harmonized computational pipelines, and standardized reporting formats will likely prove as important as technological innovation itself.

Accessibility Remains the Major Challenge

Although enthusiasm for spatial biology continues to grow, cost remains a significant barrier for many laboratories.

Rather than waiting for technology costs to decrease dramatically, panelists advocated practical strategies for improving accessibility. Collaborative projects, shared antibody panels, institutional core facilities, tissue microarrays, multiplexed specimen layouts, and technology access programs were all highlighted as effective mechanisms for lowering experimental costs.

Importantly, the discussion emphasized that scientific creativity frequently outweighs financial resources. Many successful projects begin with small pilot studies, opportunistic collaborations, or shared infrastructure before expanding into larger research programs. Building relationships with neighboring laboratories, technology developers, and institutional core facilities was repeatedly identified as one of the most effective ways to initiate spatial biology research.

Preparing the Next Generation of Spatial Biologists

As the discussion concluded, the panel shifted its focus toward training future scientists.

Beyond mastering instrumentation, panelists encouraged young researchers to develop strong foundations in tissue biology, pathology, computational analysis, and interdisciplinary collaboration. Spatial biology sits at the intersection of biology, engineering, pathology, computer science, and clinical medicine; success in the field therefore requires the ability to communicate across traditional disciplinary boundaries.

Perhaps the most encouraging advice was also the simplest: do not wait for the perfect technology or the perfect experiment. Generate data, learn from experience, collaborate widely, and allow biological questions — not technological trends — to guide scientific discovery.

Conclusions

The expert panel revealed broad consensus that spatial biology is entering a new phase of maturity. The next decade will likely be characterized not by competition between individual technologies, but by increasing integration across molecular modalities, computational methods, and clinical applications.

Artificial intelligence, multimodal data integration, and three-dimensional tissue analysis will substantially expand the scope of biological discovery. At the same time, widespread clinical adoption will depend on simplifying workflows, reducing costs, and establishing community standards for data generation and interpretation.

Ultimately, the panel underscored a central principle that extends beyond spatial biology itself: technology is valuable only insofar as it enables researchers to ask — and answer — important biological questions. As spatial biology continues to evolve, its greatest impact will not come from measuring more markers or generating larger datasets, but from providing deeper insight into how cells interact within the complex architecture of living tissues, thereby advancing both fundamental biology and precision medicine.

About this Perspective

This perspective article synthesizes the concluding expert panel discussion of the SiSP Summer School in Spatial Biology 2026 ("From Tissue Architecture to Precision Medicine"), held 6–10 July 2026 at SiSP, Srisavarindhira Building, Faculty of Medicine Siriraj Hospital, Mahidol University. It is intended for public sharing with the spatial biology research community, technology partners, and prospective trainees.