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Imagine being able to map every cell within a tumor, understanding not just which ones are present, but also their precise locations and how they communicate. This is the promise of spatial biology—a cutting-edge approach to cancer research and precision medicine. By examining cells in their natural context, spatial biology helps researchers gain biological insights into tumor structure, cellular composition, proximity, and morphology, which could lead to faster tumor detection, more accurate diagnoses, and personalized treatment strategies.
A study by Arutha Kulasinghe, PhD, Scientific Director of the Queensland Spatial Biology Center at the University of Queensland, showcased the power of this technology by demonstrating how spatial biology enables cellular phenotyping and functional annotation of every cell in the tumor microenvironment (Figure 1).
Insights revealed at the bench are now being translated into the delivery of precision oncology at an entirely new level in clinical settings. This progress provides deeper insights into the current landscape and shapes the next generation of cancer research.
- “We know where the cells are and what they are doing. Using spatial biology, we can identify subsets of cells and their nuances. This will tell us which are key players in driving resistance or sensitivity to therapy.” —Arutha Kulasinghe, PhD, University of Queensland
A catalyst for spatial biology
An enormous amount of spatial data is being generated and combined with other “omics” data, bringing new clarity to the complexity of cancer. However, deriving actionable insights from these large datasets remains a challenge due to a lack of accessible computational tools. Expanding the use of AI in spatial biology can help pathologists and oncologists identify patterns within the tumor microenvironment, leading to more precise diagnostics, personalized treatment strategies, and a better understanding of how patients respond to treatments.
Spatial biology 2.0 elevates research
Despite its potential, spatial biology still faces hurdles. Current spatial biology multiplex imaging platforms are limited by slow processing speeds, complex workflows that limit throughput, and large data storage requirements. While AI can help overcome some of these challenges, limitations remain.
To address these challenges, Akoya Biosciences has developed Spatial Biology 2.0—end-to-end solutions including PhenoCycler®-Fusion 2.0 and PhenoImager® HT 2.0, designed to generate more data, faster, at any scale. From unlocking breakthroughs from individual samples and generating spatial atlases on a human scale to understanding tumor heterogeneity, and identifying cellular neighborhoods, these solutions are poised to transform our understanding of human biology and disease.
Spatial Biology 2.0 introduces key innovations including whole-slide, highspeed imaging, scalable multiplexing, and simplified workflows (Figure 2).
- “AI can deal better and more reproducibly with large amounts of data from multiplex and hyperplex studies. AI can help analyze data and identify
patterns we may not be able to discern.” —Suzanne Coberly, MD, Bristol Myers Squibb - “The intricacies of oncology as revealed by spatial biology, along with its ever-evolving landscape of treatments and methodologies, stand to benefit immensely from AI.” —Doug Flora, MD, St. Elizabeth Healthcare, and a pioneer in utilizing AI in precision oncology
Conclusion
The convergence of spatial biology and AI marks a transformative era in precision oncology. By harnessing the detailed cellular insights provided by spatial biology and the pattern recognition of AI, this approach offers insights into cancer complexity that translate directly to the clinic. The result: more precise diagnoses, personalized treatment strategies, and a deeper understanding of cancer and other diseases.
As we continue to push the boundaries of possibility, the future of cancer treatment has never looked brighter.
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