Cancer goes beyond cell-level mutations. It is a disorder of context.
Tumors grow, adapt, and resist treatment within dynamic microenvironments shaped by surrounding immune cells, vasculature, and supporting tissue. But even the most advanced molecular tools often fail to capture how these spatial relationships influence cancer biology.
That’s where spatial omics comes in. Spatial omics is a term encompassing a range of techniques that simultaneously measure physical tissue structure and its molecular characteristics. In oncology, these techniques allow scientists to study tumors as ecosystems, preserving both the identity of each cell and its location within the tissue. The result is a multidimensional view of cancer biology—one that can uncover hidden patterns governing tumor growth, immune evasion, and metastasis.
With recent advances in resolution, accessibility, and throughput, spatial omics are no longer experimental novelties. As a result, spatial omics will support the development of more effective diagnostics and therapies as it transforms cancer research.
Mapping cellular structure and spatial expression patterns in tumors
Spatial transcriptomics allows researchers to visualize gene expression within the context of tissue sections. Unlike bulk or single-cell RNA-sequencing methods that rely on tissue dissociation, spatial approaches use a variety of techniques to link sequence data to the native context of cells, revealing how location shapes cellular function.
Early spatial transcriptomics techniques were like viewing a city through a blurry aerial photo: you could distinguish general areas—parks, industrial zones, residential blocks—but not the individual buildings or streets. These methods measured gene expression across larger tissue regions, blending signals from many neighboring cells and masking local differences. Recent advances changed that.
Today’s technologies offer single-cell and even subcellular resolution, letting researchers identify which genes are active in each cell and where that cell resides in the tissue. With this sharper view, it becomes possible to understand how cancer cells interact with their neighbors and how those interactions shift across time and space.
The following two examples illustrate the power of this approach.
Case study 1: Immune cell mapping in prostate tumors
In a 2023 study, Hirz and colleagues combined single-cell RNA sequencing and a spatial transcriptomics technique (Slide-seqV2) to analyze immune infiltration in human prostate tumors. Single-cell RNA sequencing was used to characterize the immune microenvironment in high resolution, and Slide-seqV2 provided spatial context by mapping where those immune cells were located within intact tissue sections.
Their analysis revealed substantial spatial and functional heterogeneity of immune cells within tumors. Some regions were rich in active, tumor-fighting cytotoxic T cells, while others were dominated by immune cells with impaired function, including T cells with reduced cytotoxic activity and myeloid cells with immunosuppressive features.
The team also found that tumor cells influence nearby immune cells through localized molecular signals. These signals modified gene expression patterns in immune cells, reducing their ability to attack tumor cells and coordinate effective immune responses in specific regions. Such interactions would not be detectable using single-cell RNA-sequencing alone, as tissue dissociation disrupts spatial relationships.
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Case study 2: Spatial immune profiling in melanoma brain metastases
Biermann et al. used single-cell RNA sequencing to characterize and classify the types of immune and tumor cells present in melanoma brain metastases, and Slide-seqV2 to map where these cells were located within the tissue. By combining both techniques, they linked cell identity with spatial distribution.
They found that certain tumor regions were densely infiltrated by immune cells, but many of these cells showed signs of dysfunction. For example, some CD8+ T cells (typically responsible for attacking cancer cells) showed signs of reduced activity. Macrophages nearby also exhibited traits linked to tumor progression, such as signals that suppress immune activity.
In addition, cancer cells in brain metastases displayed gene expression patterns resembling those of neurons, suggesting an adaptation to the brain environment that may help them survive or evade the immune system.
These patterns were only visible because the researchers preserved the spatial context of the tissue. Without the added spatial dimension, these regionally-specific characteristics—immune dysfunction and tumor cell adaptation to local environments—would have been impossible to uncover from single-cell data.
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Characterizing microenvironments at the genome scale
Much of what we know about cancer growth and survival comes from large-scale genomic studies. These approaches have been crucial for identifying tumor-driving mutations, but they have limitations in cancers where malignant cells are few and the surrounding microenvironment plays a dominant role. Classic Hodgkin lymphoma and follicular lymphoma are examples of microenvironment-associated tumors. In these cases, cancer cells often rely on support from nearby cells rather than on their own genetic mutations to survive.
Studying these cancers is a particular challenge because traditional methods miss that important spatial context underlying cell behavior or have limited resolution. Techniques like single-cell RNA sequencing require breaking tissue apart, which removes key information about spatial relationships. Other options, like bulk sequencing methods, average signals across all cells, masking important differences between individual cells.
Recent advances in spatial and single-cell technologies make it possible to study rare tumor cells in their original tissue context. A study in classic Hodgkin lymphoma illustrates how this approach can uncover tumor survival mechanisms hidden within the microenvironment.
Case study 3: Identifying survival signals in Hodgkin lymphoma
In classic Hodgkin lymphoma, the cancer cells—called Hodgkin Reed-Sternberg (HRS) cells—constitute only a small part of the tumor. Most of the tissue is composed of surrounding immune and stromal cells, which may play a key role in helping the tumor survive. Shanmugam and colleagues combined single-nucleus RNA sequencing with spatial transcriptomics to understand how these cancer cells survive.
Their analysis showed that HRS cells are typically surrounded by a structured neighborhood rich in CD4+ T cells and myeloid cells, but depleted of plasma cells. This specific arrangement suggested that nearby cells might provide signals that help the cancer survive. Using machine learning to scan for possible communication signals between cells, the team identified interleukin-13 (a molecule produced by immune cells) as a potential survival factor. They then confirmed in lab experiments that blocking interleukin-13 or its receptor led to the death of HRS cells.
Because interleukin-13-blocking antibodies are already approved for other diseases, this discovery—made possible with spatial omics—may open new therapeutic possibilities for Hodgkin lymphoma.
Lab considerations: bridging innovation and implementation
The potential of spatial omics is immense, but integrating these technologies into lab workflows requires careful planning.
Costs can be high, especially for platforms that require custom slide arrays or specialized imaging systems. Before investing, labs should assess how much spatial resolution they truly need. Some research questions can be addressed with lower-resolution methods that are simpler and more affordable to implement.
Data analysis is another common hurdle. Spatial datasets are large and complex, often requiring bioinformatics support or dedicated pipelines. Labs without in-house expertise may benefit from partnering with core facilities or selecting platforms that offer streamlined software tools and cloud-based processing.
Throughput is another consideration. High-resolution platforms generate rich data but often process fewer samples at a time. For studies involving large cohorts or time-sensitive projects, lower-throughput platforms may not be practical.
Newer solutions are making it easier for labs to get started with spatial omics. Some protocols now use tightly packed bead arrays to achieve single-cell resolution (~10 μm) without the need for custom imaging instruments or advanced microscopy expertise. These methods rely on standard molecular biology techniques, making them easier to integrate into existing workflows. They are also flexible—applicable across various organisms and tissue types. In addition, some modern solutions simplify data analysis by offering workflows that generate ready-to-use spatial gene expression maps, reducing the need for extensive bioinformatics expertise.
A new way to understand tumors
Spatial transcriptomics and spatial genomics have expanded the types of questions researchers can ask about cancer. Instead of studying tumors only as mixtures of cells, scientists can now investigate how the location of specific cells, their gene activity, and their interactions influence tumor growth, immune evasion, and treatment resistance.
This shift moves research beyond simply cataloging genetic changes. It allows scientists to map where critical cellular interactions occur and understand why certain tumor niches promote disease progression or therapy resistance. For researchers aiming to uncover not only what’s happening in a tumor, but also where and why, it’s an exciting time to be in the lab.
