Mapping Disease Through Advances in Spatial Phenotyping

Our current understanding of many diseases, from mechanism to treatment, has benefited greatly from bulk and single-cell genomic technologies. However, these methods often involve the characterization of isolated cells removed from their endogenous spatial context—removing many of the spatial relationships within tissue microenvironments that modulate cellular activity. To gain a more complete understanding of these complex cellular landscapes, techniques developed over the last 15 years have led to the advent of spatial biology, which involves a suite of techniques and technologies that use high-plex biomarker detection with single-cell resolution microscopy (Learn more here). Together, these advances have enabled researchers to study biomarker expression, spatial organization, and cell classes with higher throughput, greater sensitivity, and higher resolution.

Advances in spatial phenotyping have not only uncovered remarkable cellular diversity and dynamics across a variety of systems, but have also provided valuable insight into the mechanisms that underlie disease progression, particularly in immunology and oncology. Here we speak with several industry leaders to gain insight into recent advances in spatial phenotyping and how these technologies are shaping research related to tissue microenvironments and their impact on disease.

From bulk assays to spatial phenotyping

As Anastasiia Marchuk, Product Specialist at TissueGnostics, explains “Spatial biology taught us that the distribution and surrounding of a cell is no less important than its identity.”

Early spatial assays involved multiplex immunofluorescence or RNA in situ hybridization. Although these early approaches typically only measured three to six biomarkers simultaneously, they have revealed critical interactions within tissue microenvironments that drive disease. “Fundamentally, scientists now have a much more detailed view of what happens in diseased tissues…They think in diagrams showing cascades of cellular interactions and distinctive organizations of cells, called neighborhoods. Borrowing a phrase from an old friend, spatial is essentially a new field that one could call ‘interactomics’”, says Cliff Hoyt, VP, Translational Spatial Applications at Quanterix. Early discoveries, such as the identification of distinct tumor immune architectures (e.g., “immune desert,” “immune exclusion,” and “immune infiltrated”), have highlighted the importance of spatial context in disease biology and revealed how the tissue microenvironment can influence patient responses.

Current challenges

As is the case with most emerging technologies, there are several tradeoffs to be wary of. According to Marchuk, “There are several points to consider: high resolution to confidently identify individual cells, large analysis area to capture actual heterogeneous biology of a sample, and high molecular depth. Add costs on top—and it’s a hard call to make.”

Despite these challenges, researchers have emphasized the importance of addressing technological difficulties on a continuum—platforms should “address the specific needs…from basic research to clinical application, rather than trying to support the entire continuum with the same platform,” says Hoyt. As Marchuk goes on to explain, many studies are harnessing multi-omics for spatial phenotyping to navigate these tradeoffs, “for example, using single-cell RNA-Seq for data mining and then deploying multiplex imaging to scale spatially.”

Cutting edge advancements in spatial biology

Today, a wealth of advances in spatial phenotyping technologies have significantly increased both multiplex capacity and analytical depth. Modern platforms can now perform multimodal detection of dozens to hundreds of proteins, and in some cases, thousands of RNA molecules within intact tissue sections while preserving spatial architecture.

Next-generation spatial phenotyping platforms (e.g., The PhenoCycler^®-Fusion 2.0 from Quanterix) are capable of interrogating tissue sections for 100s of biomarkers, with improved throughput, and tissue vs. tissue comparisons on single slides. Together with high-resolution imaging and computational analysis pipelines, advances in spatial phenotyping analyses have enabled researchers to significantly accelerate their research questions. As Hoyt says, “With multimodal platforms and multiplexing levels into the 10s, 100s, and even 1000s, the number of questions we can ask of a sample or study cohort goes up exponentially.”

Over the last decade, research questions have shifted away from simply quantifying the abundance of a particular biomarker toward understanding how cells interact within “neighborhoods” and computational tools are increasingly designed to analyze these spatial relationships directly.

“The research focus is no longer solely on the quantities of a particular phenotype, but on its neighborhood,” explains Marchuk. “At TissueGnostics, we have been anticipating this shift for years now, and we offer a number of spatial biology analyses to choose from in our StrataQuest software, such as distance maps or phenotype interaction engines that allow researchers to visualize how two phenotypes of interest spatially interact and quantify these interactions.”

New spatial biology approaches are also expanding beyond protein detection toward integrated multi-omic measurements. Todd Dickinson, Ph.D., CEO of Stellaromics, highlights the company’s RIBOmap (designed for translatomics) platform, which enables 3D in situ measurement of thousands of transcripts and translation activity by detecting ribosome-bound mRNA in tissue blocks up to 100 µm thick. When combined with STARmap (for transcriptomics), these approaches enable researchers to examine gene expression, protein translation, and spatial organization simultaneously within three-dimensional tissue architecture.

These innovations are particularly valuable when studying rare cellular interactions within complex tissues. Indeed, Dickinson points to recent work using STARmap and RIBOmap in thick tumor sections to detect interactions between Langerhans cells and tumor-associated keratinocytes that contribute to immunotherapy resistance in cutaneous squamous cell carcinoma. “RIBOmap provides an active view of protein production at the single-cell level…using ribosome-bound mRNA as a proxy for protein expression and extending plex into the thousands,” Dickinson explains.

Spatial biology and clinical impact

Marchuk emphasizes that while spatial phenotyping signatures provide promise, “We are far away from using spatial phenotyping at scale in personalized medicine”. Nevertheless, recent developments are bringing spatial phenotyping signatures closer to clinical applications. Hoyt provides several examples, including the ALK “break-apart” FISH companion diagnostic for ALK inhibitors, a multiplex IF prognostic assay for Barrett’s esophagus, and a multiplex IF predictive assay for aromatase inhibitors in advanced prostate cancer.

Looking ahead, emerging spatial multi-omics platforms may further expand the translational potential of these approaches. As Dickinson explains, integrating spatial transcriptomic and translatomic readouts may help researchers better understand how therapeutic interventions affect gene expression and protein production directly within intact tissues.