Spatial biology is transforming how researchers study tissues, providing a new level of insight into how cells are organized and interact within their native environment. By preserving spatial context, these techniques allow scientists to explore how cell positioning and microenvironmental cues influence disease progression, therapeutic response, and tissue function. As spatial biology becomes more widely adopted, there is an ever-greater need for scalable, reproducible workflows. 

To explore how automation is helping laboratories meet these growing demands, Drug Discovery News spoke with Traci DeGeer, Director of Advanced Staining Innovation at Leica Biosystems. In this interview, DeGeer discusses how automation is helping to enhance workflow consistency, increase throughput, and enable the broader adoption of spatial biology across research and clinical settings.

Why has spatial biology become such a critical tool in areas such as oncology, immuno-oncology, and neurobiology research?

Spatial biology has become an indispensable tool, providing unprecedented insights into the tissue microenvironment and cellular interactions.

In oncology, spatial biology is rewriting how we understand cancer. Instead of viewing tumors as uniform masses, researchers can now map the complex cellular neighborhoods within them. This is enhancing biomarker discovery, driving precision medicine forward, and explaining tumor heterogeneity in ways that reveal why some cancers resist treatment. When you can see exactly how cancer cells organize themselves spatially, you gain crucial insights into tumor progression that were invisible before.

For immuno-oncologists, it's like getting a detailed battlefield map of the immune system's fight against cancer. By tracking immune-tumor cell interactions at the tissue level, scientists can finally see how immune responses play out in real space and time. This spatial intelligence is uncovering why some tumors evade immune attack and helping refine immunotherapy strategies based on the actual geography of the immune response.

In neurobiology, spatial biology is creating unprecedented molecular maps of the brain. This detailed spatial analysis is revealing exactly where molecular changes occur in diseases like Alzheimer's, Parkinson's, and epilepsy — not just what goes wrong, but precisely where it happens in brain tissue. This spatial context is identifying therapeutic targets that traditional approaches might have missed entirely.

What are the key challenges for spatial biology workflows? 

Spatial biology workflows come with their fair share of challenges. One major hurdle is the lack of standardization. With so many companies developing reagents and platforms, the result is a fragmented landscape of instruments and consumables. Some are platform-specific and can be prohibitively expensive, making accessibility a significant issue for many labs.

Another challenge is the sheer volume and complexity of the data generated. Spatial biology often involves integrating multiple types of omic data, creating massive datasets that can be difficult to manage, process, and interpret effectively.

How might automation help spatial biology techniques transition from the lab bench to the clinic?

The automation of spatial biology techniques has massively enhanced their consistency and reproducibility by eliminating manual steps and increasing throughput. Complex procedures that once depended on manual handling can now be executed with precision, reducing the risk of human error. This level of reliability is crucial for generating accurate data efficiently, supporting streamlined, high-throughput processes in both research and clinical settings.

What aspects of spatial biology workflows are most suitable for automation, and which remain reliant on human expertise?

The short answer? Machines excel at the routine, precision-heavy tasks, while people still drive the creative and interpretive work that makes discoveries meaningful.

Sample staining, imaging, and data processing have become automation success stories. Robotic systems can pipette samples with perfect consistency, imaging platforms can capture large numbers of high-resolution images overnight, and artificial intelligence (AI) algorithms can crunch through spatial data to identify patterns that would take human researchers weeks to spot. These repetitive, precision-dependent tasks play to machines' strengths in that they never get tired or lose focus.

However, the most crucial aspects of spatial biology still require uniquely human skills. Experimental design and hypothesis formation depend on creativity, intuition, and the ability to ask the right "what if" questions that lead to breakthroughs. Results interpretation requires contextual understanding, while AI can tell you that Cell Type A clusters near Cell Type B in diseased tissue, it takes human expertise to understand what that spatial relationship means for disease progression or treatment strategies.

Troubleshooting is another distinctly human strength. When experiments produce unexpected results, it requires experience and creative problem-solving to determine whether you've discovered something new or need to adjust your approach.

The most successful labs don't choose between automation and human expertise, they strategically combine both. Machines handle the heavy lifting of routine tasks, freeing researchers to focus on the creative, interpretive work that drives scientific discovery forward. 

How is AI being used to support or enhance spatial biology workflows and insights?

Think of AI as the research world's most talented pattern detective. It's completely transforming how we analyze tissue samples and understand cellular behavior.

AI algorithms can perform precise cell segmentation and identification across thousands of tissue images with consistent accuracy, dramatically reducing analysis time while eliminating human variability in cell classification. These systems can then model spatial relationships, analyzing cellular interactions and spatial organization patterns to generate predictive models that reveal how different cell types influence each other within the tissue microenvironment. This capability is essential for understanding disease mechanisms and therapeutic responses.

AI excels at processing multi-modal datasets that combine proteomics, transcriptomics, and spatial information, extracting meaningful biological patterns from these high-dimensional data structures that traditional analytical approaches struggle to handle effectively. Machine learning models trained on large tissue datasets can predict patient treatment responses and identify disease biomarkers, enabling researchers and pharmaceutical companies to develop more targeted therapeutic strategies and diagnostic tools. These advancements empower researchers and pharmaceutical companies to develop predictive models for innovative treatments and diagnostics.

What are some of the most exciting innovations you've seen recently in spatial biology technologies?

One of the most exciting innovations is the ability to extract multiomic data from a single slide. This can be achieved by using advanced assays from a single company or by combining different technologies, preserving precious tissue samples while providing unprecedented molecular insights.